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  • 1. Andersson, Malin
    et al.
    Jägervall, Karl
    Eriksson, Per
    Persson, Anders
    Granerus, Göran
    Wang, Chunliang
    KTH, School of Technology and Health (STH). Linköping Univ, Sweden.
    Smedby, Örjan
    KTH, School of Technology and Health (STH). Linköping Univ, Sweden.
    How to measure renal artery stenosis - a retrospective comparison of morphological measurement approaches in relation to hemodynamic significance2015In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, article id 42Article in journal (Refereed)
    Abstract [en]

    Background: Although it is well known that renal artery stenosis may cause renovascular hypertension, it is unclear how the degree of stenosis should best be measured in morphological images. The aim of this study was to determine which morphological measures from Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA) are best in predicting whether a renal artery stenosis is hemodynamically significant or not. Methods: Forty-seven patients with hypertension and a clinical suspicion of renovascular hypertension were examined with CTA, MRA, captopril-enhanced renography (CER) and captopril test (Ctest). CTA and MRA images of the renal arteries were analyzed by two readers using interactive vessel segmentation software. The measures included minimum diameter, minimum area, diameter reduction and area reduction. In addition, two radiologists visually judged the diameter reduction without automated segmentation. The results were then compared using limits of agreement and intra-class correlation, and correlated with the results from CER combined with Ctest (which were used as standard of reference) using receiver operating characteristics (ROC) analysis. Results: A total of 68 kidneys had all three investigations (CTA, MRA and CER + Ctest), where 11 kidneys (16.2 %) got a positive result on the CER + Ctest. The greatest area under ROC curve (AUROC) was found for the area reduction on MRA, with a value of 0.91 (95 % confidence interval 0.82-0.99), excluding accessory renal arteries. As comparison, the AUROC for the radiologists' visual assessments on CTA and MRA were 0.90 (0.82-0.98) and 0.91 (0.83-0.99) respectively. None of the differences were statistically significant. Conclusions: No significant differences were found between the morphological measures in their ability to predict hemodynamically significant stenosis, but a tendency of MRA having higher AUROC than CTA. There was no significant difference between measurements made by the radiologists and measurements made with fuzzy connectedness segmentation. Further studies are required to definitely identify the optimal measurement approach.

    Download full text (pdf)
    Andersson 2015 renal artery stenosis
  • 2. Andersson, Thord
    et al.
    Romu, Thobias
    Karlsson, Anette
    Norén, Bengt
    Forsgren, Mikael F
    Smedby, Örjan
    Linköping University.
    Kechagias, Stergios
    Almer, Sven
    Lundberg, Peter
    Borga, Magnus
    Leinhard, Olof Dahlqvist
    Consistent intensity inhomogeneity correction in water-fat MRI2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 2Article in journal (Refereed)
    Abstract [en]

    PURPOSE: To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities

    METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

    RESULTS: CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

    CONCLUSION: CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type.

  • 3.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Institutet, Department of Oncology-Pathology, SE-17176 Stockholm, Sweden .
    De Benetti, Francesca
    Yeganeh, Yousef
    Toma-Dasu, Iuliana
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    Navab, Nassir
    Wendler, Thomas
    Unsupervised Tumor SegmentationManuscript (preprint) (Other academic)
    Download full text (pdf)
    AnomalyTumor_draft
  • 4.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Univ Sjukhuset, Karolinska Inst, Dept Oncol Pathol, SE-17176 Stockholm, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Prior-aware autoencoders for lung pathology segmentation2022In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 80, p. 102491-, article id 102491Article in journal (Refereed)
    Abstract [en]

    Segmentation of lung pathology in Computed Tomography (CT) images is of great importance for lung disease screening. However, the presence of different types of lung pathologies with a wide range of heterogeneities in size, shape, location, and texture, on one side, and their visual similarity with respect to surrounding tissues, on the other side, make it challenging to perform reliable automatic lesion seg-mentation. To leverage segmentation performance, we propose a deep learning framework comprising a Normal Appearance Autoencoder (NAA) model to learn the distribution of healthy lung regions and re-construct pathology-free images from the corresponding pathological inputs by replacing the pathological regions with the characteristics of healthy tissues. Detected regions that represent prior information re-garding the shape and location of pathologies are then integrated into a segmentation network to guide the attention of the model into more meaningful delineations. The proposed pipeline was tested on three types of lung pathologies, including pulmonary nodules, Non-Small Cell Lung Cancer (NSCLC), and Covid-19 lesion on five comprehensive datasets. The results show the superiority of the proposed prior model, which outperformed the baseline segmentation models in all the cases with significant margins. On av-erage, adding the prior model improved the Dice coefficient for the segmentation of lung nodules by 0.038, NSCLCs by 0.101, and Covid-19 lesions by 0.041. We conclude that the proposed NAA model pro-duces reliable prior knowledge regarding the lung pathologies, and integrating such knowledge into a prior segmentation network leads to more accurate delineations.

  • 5.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Department of Oncology-Pathology, Karolinska Institutet Karolinska Universitetssjukhuset Stockholm Sweden.
    Toma-Dasu, Iuliana
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Normal appearance autoencoder for lung cancer detection and segmentation2019In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer Nature , 2019, p. 249-256Conference paper (Refereed)
    Abstract [en]

    One of the major differences between medical doctor training and machine learning is that doctors are trained to recognize normal/healthy anatomy first. Knowing the healthy appearance of anatomy structures helps doctors to make better judgement when some abnormality shows up in an image. In this study, we propose a normal appearance autoencoder (NAA), that removes abnormalities from a diseased image. This autoencoder is semi-automatically trained using another partial convolutional in-paint network that is trained using healthy subjects only. The output of the autoencoder is then fed to a segmentation net in addition to the original input image, i.e. the latter gets both the diseased image and a simulated healthy image where the lesion is artificially removed. By getting access to knowledge of how the abnormal region is supposed to look, we hypothesized that the segmentation network could perform better than just being shown the original slice. We tested the proposed network on the LIDC-IDRI dataset for lung cancer detection and segmentation. The preliminary results show the NAA approach improved segmentation accuracy substantially in comparison with the conventional U-Net architecture. 

  • 6.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Buizza, Giulia
    Toma-Dasu, Iuliana
    Lazzeroni, Marta
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method2019In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, p. 58-65Article in journal (Refereed)
  • 7.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Buizza, Giulia
    Toma-Dasu, Iuliana
    Lazzeroni, Marta
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    OC-0406 Early survival prediction in non-small cell lung cancer with PET/CT size aware longitudinal pattern2019In: Radiotherapy and Oncology, ISSN 0167-8140, Vol. 133, p. S208-S209Article in journal (Refereed)
  • 8.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Institutet, Department of Oncology-Pathology Karolinska Universitetssjukhuset Solna Sweden.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Carrizo, Garrizo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Toma-Dasu, Iuliana
    Karolinska Institutet, Department of Oncology-Pathology Karolinska Universitetssjukhuset Solna Sweden.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Multimodal brain tumor segmentation with normal appearance autoencoder2019In: International MICCAI Brainlesion Workshop, Springer Nature , 2019, p. 316-323Conference paper (Refereed)
    Abstract [en]

    We propose a hybrid segmentation pipeline based on the autoencoders’ capability of anomaly detection. To this end, we, first, introduce a new augmentation technique to generate synthetic paired images. Gaining advantage from the paired images, we propose a Normal Appearance Autoencoder (NAA) that is able to remove tumors and thus reconstruct realistic-looking, tumor-free images. After estimating the regions where the abnormalities potentially exist, a segmentation network is guided toward the candidate region. We tested the proposed pipeline on the BraTS 2019 database. The preliminary results indicate that the proposed model improved the segmentation accuracy of brain tumor subregions compared to the U-Net model. 

  • 9.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden..
    Yang, Guang
    Royal Brompton Hosp, Cardiovasc Res Ctr, London, England.;Imperial Coll London, Natl Heart & Lung Inst, London, England..
    Zakko, Yousuf
    Karolinska Univ Hosp, Dept Radiol Imaging & Funct, Solna, Sweden..
    Toma-Dasu, Iuliana
    Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.;Stockholm Univ, Dept Phys, Stockholm, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images2021In: Frontiers in Oncology, E-ISSN 2234-943X, Vol. 11, article id 737368Article in journal (Refereed)
    Abstract [en]

    ObjectivesBoth radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules. MethodsConventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction. ResultsThe best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean +/- standard deviations) of 0.792 +/- 0.025, 0.801 +/- 0.018, and 0.817 +/- 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 +/- 0.010, 0.824 +/- 0.021, and 0.936 +/- 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 +/- 0.010). ConclusionThe end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.

  • 10.
    Astaraki, Mehdi
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden.
    Zakko, Yousuf
    Karolinska University Hospital, Imaging and Function, Radiology Department, Solna, SE-17176 Stockholm, Sweden.
    Dasu, Iuliana Toma
    Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden ; Stockholm University, Department of Physics, SE-106 91 Stockholm, Sweden.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features2021In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 83, p. 146-153Article in journal (Refereed)
    Abstract [en]

    Purpose: Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features.

    Methods: To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric. Results: Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner.

    Conclusion: Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.

  • 11.
    Batool, Nazre
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Chowdhury, Manish
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Estimation of trabecular bone thickness in gray scale: a validation study2017In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, Vol. 12, no Supplement 1Article in journal (Refereed)
  • 12.
    Bendazzoli, Simone
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Brusini, Irene
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Astaraki, Mehdi
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Persson, Mats
    KTH, School of Engineering Sciences (SCI), Physics, Physics of Medical Imaging.
    Yu, Jimmy
    Connolly, Bryan
    Nyrén, Sven
    Strand, Fredrik
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT2020Manuscript (preprint) (Other academic)
  • 13.
    Bendazzoli, Simone
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Brusini, Irene
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Inst, Dept Neurobiol Care Sci & Soc, Alfred Nobels Alle 23,D3, S-14152 Huddinge, Sweden..
    Damberg, Peter
    Karolinska Inst, Dept Clin Neurosci, Tomtebodavagen 18A P1 5, S-17177 Stockholm, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Andersson, Leif
    Uppsala Univ, Dept Med Biochem & Microbiol, Sci Life Lab Uppsala, Biomedicinskt Ctr BMC, Husargatan 3, S-75237 Uppsala, Sweden..
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Automatic rat brain segmentation from MRI using statistical shape models and random forest2019In: MEDICAL IMAGING 2019: IMAGE PROCESSING / [ed] Angelini, ED Landman, BA, SPIE-INT SOC OPTICAL ENGINEERING , 2019, article id 1094920Conference paper (Refereed)
    Abstract [en]

    In MRI neuroimaging, the shimming procedure is used before image acquisition to correct for inhomogeneity of the static magnetic field within the brain. To correctly adjust the field, the brain's location and edges must first be identified from quickly-acquired low resolution data. This process is currently carried out manually by an operator, which can be time-consuming and not always accurate. In this work, we implement a quick and automatic technique for brain segmentation to be potentially used during the shimming. Our method is based on two main steps. First, a random forest classifier is used to get a preliminary segmentation from an input MRI image. Subsequently, a statistical shape model of the brain, which was previously generated from ground-truth segmentations, is fitted to the output of the classifier to obtain a model-based segmentation mask. In this way, a-priori knowledge on the brain's shape is included in the segmentation pipeline. The proposed methodology was tested on low resolution images of rat brains and further validated on rabbit brain images of higher resolution. Our results suggest that the present method is promising for the desired purpose in terms of time efficiency, segmentation accuracy and repeatability. Moreover, the use of shape modeling was shown to be particularly useful when handling low-resolution data, which could lead to erroneous classifications when using only machine learning-based methods.

  • 14. Bernard, Olivier
    et al.
    Bosch, J G
    Heyde, Brecht
    Alessandrini, Martino
    Barbosa, Daniel
    Camarasu-Pop, S
    Cervenansky, F
    Valette, S
    Mirea, O
    Bernier, M
    Jodoin, P M
    Domingos, J S
    Stebbing, R V
    Keraudren, K
    Oktay, O
    Caballero, J
    Shi, W
    Rueckert, D
    Milletari, F
    Ahmadi, S A
    Smistad, E
    Lindseth, F
    van Stralen, M
    Wang, Chunliang
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Donal, E
    Monaghan, M
    Papachristidis, A
    Geleijnse, M L
    Galli, E
    Dhooge, Jan
    Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography.2016In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 35, no 4, p. 967-977Article in journal (Refereed)
    Abstract [en]

    Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from 3 experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

  • 15. Blystad, I
    et al.
    Håkansson, I
    Tisell, A
    Ernerudh, J
    Smedby, Örjan
    aFrom the Departments of Radiology and Medical and Health Sciences (I.B., Ö.S.) bCentre for Medical Image Science and Visualization (I.B., A.T., Ö.S., P.L., E.-M.L.)Linköping University.
    Lundberg, P
    Larsson, E-M
    Quantitative MRI for Analysis of Active Multiple Sclerosis Lesions without Gadolinium-Based Contrast Agent2016In: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959X, Vol. 37, no 1, p. 94-100Article in journal (Refereed)
    Abstract [en]

    BACKGROUND AND PURPOSE: Contrast-enhancing MS lesions are important markers of active inflammation in the diagnostic work-up of MS and in disease monitoring with MR imaging. Because intravenous contrast agents involve an expense and a potential risk of adverse events, it would be desirable to identify active lesions without using a contrast agent. The purpose of this study was to evaluate whether pre-contrast injection tissue-relaxation rates and proton density of MS lesions, by using a new quantitative MR imaging sequence, can identify active lesions.

    MATERIALS AND METHODS: Forty-four patients with a clinical suspicion of MS were studied. MR imaging with a standard clinical MS protocol and a quantitative MR imaging sequence was performed at inclusion (baseline) and after 1 year. ROIs were placed in MS lesions, classified as nonenhancing or enhancing. Longitudinal and transverse relaxation rates, as well as proton density were obtained from the quantitative MR imaging sequence. Statistical analyses of ROI values were performed by using a mixed linear model, logistic regression, and receiver operating characteristic analysis.

    RESULTS: Enhancing lesions had a significantly (P < .001) higher mean longitudinal relaxation rate (1.22 ± 0.36 versus 0.89 ± 0.24), a higher mean transverse relaxation rate (9.8 ± 2.6 versus 7.4 ± 1.9), and a lower mean proton density (77 ± 11.2 versus 90 ± 8.4) than nonenhancing lesions. An area under the receiver operating characteristic curve value of 0.832 was obtained.

    CONCLUSIONS: Contrast-enhancing MS lesions often have proton density and relaxation times that differ from those in nonenhancing lesions, with lower proton density and shorter relaxation times in enhancing lesions compared with nonenhancing lesions.

  • 16.
    Blystad, I
    et al.
    Linköping Univ, Dept Radiol Linköping, Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden..
    Warntjes, J. B. M.
    Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden.;Linköping Univ, Div Cardiovasc Med, Dept Hlth Med & Caring Sci, Linköping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Linköping Univ, Dept Radiol Linköping, Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden..
    Lundberg, P.
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden.;Linköping Univ, Dept Radiat Phys, Linköping, Sweden..
    Larsson, E-M
    Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden.;Uppsala Univ, Dept Surg Sci, Radiol, Uppsala, Sweden..
    Tisell, A.
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden.;Linköping Univ, Dept Radiat Phys, Linköping, Sweden..
    Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema2020In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, no 1, article id 17986Article in journal (Refereed)
    Abstract [en]

    Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R-1), transverse relaxation (R-2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R-1-difference-map. The quantitative R-1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.

  • 17. Blystad, Ida
    et al.
    Warntjes, J. B. Marcel
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.
    Lundberg, Peter
    Larsson, Elna-Marie
    Tisell, Anders
    Quantitative MRI for analysis of peritumoral edema in malignant gliomas2017In: PLOS ONE, E-ISSN 1932-6203, Vol. 12, no 5, article id e0177135Article in journal (Refereed)
    Abstract [en]

    Background and purpose Damage to the blood-brain barrier with subsequent contrast enhancement is a hallmark of glioblastoma. Non-enhancing tumor invasion into the peritumoral edema is, however, not usually visible on conventional magnetic resonance imaging. New quantitative techniques using relaxometry offer additional information about tissue properties. The aim of this study was to evaluate longitudinal relaxation R-1, transverse relaxation R-2, and proton density in the peritumoral edema in a group of patients with malignant glioma before surgery to assess whether relaxometry can detect changes not visible on conventional images. Methods In a prospective study, 24 patients with suspected malignant glioma were examined before surgery. A standard MRI protocol was used with the addition of a quantitative MR method (MAGIC), which measured R-1, R-2, and proton density. The diagnosis of malignant glioma was confirmed after biopsy/surgery. In 19 patients synthetic MR images were then created from the MAGIC scan, and ROIs were placed in the peritumoral edema to obtain the quantitative values. Dynamic susceptibility contrast perfusion was used to obtain cerebral blood volume (rCBV) data of the peritumoral edema. Voxel-based statistical analysis was performed using a mixed linear model. Results R-1, R-2, and rCBV decrease with increasing distance from the contrast-enhancing part of the tumor. There is a significant increase in R1 gradient after contrast agent injection (P<.0001). There is a heterogeneous pattern of relaxation values in the peritumoral edema adjacent to the contrast-enhancing part of the tumor. Conclusion Quantitative analysis with relaxometry of peritumoral edema in malignant gliomas detects tissue changes not visualized on conventional MR images. The finding of decreasing R-1 and R-2 means shorter relaxation times closer to the tumor, which could reflect tumor invasion into the peritumoral edema. However, these findings need to be validated in the future.

  • 18.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Carneiro, Miguel
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;Univ Porto, Dept Biol, Fac Ciencias, P-4169007 Porto, Portugal..
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Rubin, Carl-Johan
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden..
    Ring, Henrik
    Uppsala Univ, Dept Neurosci, S-75236 Uppsala, Sweden..
    Afonso, Sandra
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal..
    Blanco-Aguiar, Jose A.
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;CSIC, Inst Invest Recursos Cineget IREC, Ciudad Real 13005, Spain.;UCLM, CSIC, Ciudad Real 13005, Spain..
    Ferrand, Nuno
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;Univ Porto, Dept Biol, Fac Ciencias, P-4169007 Porto, Portugal.;Univ Johannesburg, Dept Zool, ZA-2006 Auckland Pk, South Africa..
    Rafati, Nima
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden..
    Villafuerte, Rafael
    CSIC, IESA, Cordoba 14004, Spain..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Damberg, Peter
    Karolinska Univ Hosp, Karolinska Expt Res & Imaging Ctr, S-17176 Solna, Sweden..
    Hallbook, Finn
    Uppsala Univ, Dept Neurosci, S-75236 Uppsala, Sweden..
    Fredrikson, Mats
    Uppsala Univ, Dept Psychol, S-75236 Uppsala, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden..
    Andersson, Leif
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden.;Texas A&M Univ, Coll Vet Med & Biomed Sci, Dept Vet Integrat Biosci, College Stn, TX 77843 USA.;Swedish Univ Agr Sci, Dept Anim Breeding & Genet, S-75007 Uppsala, Sweden..
    Changes in brain architecture are consistent with altered fear processing in domestic rabbits2018In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 115, no 28, p. 7380-7385Article in journal (Refereed)
    Abstract [en]

    The most characteristic feature of domestic animals is their change in behavior associated with selection for tameness. Here we show, using high-resolution brain magnetic resonance imaging in wild and domestic rabbits, that domestication reduced amygdala volume and enlarged medial prefrontal cortex volume, supporting that areas driving fear have lost volume while areas modulating negative affect have gained volume during domestication. In contrast to the localized gray matter alterations, white matter anisotropy was reduced in the corona radiata, corpus callosum, and the subcortical white matter. This suggests a compromised white matter structural integrity in projection and association fibers affecting both afferent and efferent neural flow, consistent with reduced neural processing. We propose that compared with their wild ancestors, domestic rabbits are less fearful and have an attenuated flight response because of these changes in brain architecture.

  • 19.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Jörgens, Daniel
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Dependency of neural tracts'€™ curvature estimations on tractography methods2017Conference paper (Refereed)
  • 20.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Jörgens, Daniel
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Influence of Tractography Algorithms and Settings on Local Curvature Estimations2017Conference paper (Refereed)
  • 21.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Jörgens, Daniel
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry2019Conference paper (Refereed)
    Abstract [en]

    This paper aims at proposing a method to generate atlases of white matter fibers’ geometry that consider local orientation and curvature of fibers extracted from tractography data. Tractography was performed on diffusion magnetic resonance images from a set of healthy subjects and each tract was characterized voxel-wise by its curvature and Frenet–Serret frame, based on which similar tracts could be clustered separately for each voxel and each subject. Finally, the centroids of the clusters identified in all subjects were clustered to create the final atlas. The proposed clustering technique showed promising results in identifying voxel-wise distributions of curvature and orientation. Two tractography algorithms (one deterministic and one probabilistic) were tested for the present work, obtaining two different atlases. A high agreement between the two atlases was found in several brain regions. This suggests that more advanced tractography methods might only be required for some specific regions in the brain. In addition, the probabilistic approach resulted in the identification of a higher number of fiber orientations in various white matter areas, suggesting it to be more adequate for investigating complex fiber configurations in the proposed framework as compared to deterministic tractography.

  • 22.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Solna, Sweden..
    Lindberg, Olof
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Solna, Sweden..
    Muehlboeck, J-Sebastian
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Solna, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Westman, Eric
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Solna, Sweden..
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus2020In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 14, article id 15Article in journal (Refereed)
    Abstract [en]

    Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.

  • 23.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Inst, Dept Neurobiol Care Sci & Soc, Stockholm, Sweden..
    MacNicol, Eilidh
    Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Neuroimaging, London, England..
    Kim, Eugene
    Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Neuroimaging, London, England..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Westman, Eric
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Stockholm, Sweden..
    Veronese, Mattia
    Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Neuroimaging, London, England.;Univ Padua, Dept Informat Engn, Padua, Italy..
    Turkheimer, Federico
    Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Neuroimaging, London, England..
    Cash, Diana
    Kings Coll London, Inst Psychiat Psychol & Neurosci, Dept Neuroimaging, London, England..
    MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention2022In: Neurobiology of Aging, ISSN 0197-4580, E-ISSN 1558-1497, Vol. 109, p. 204-215Article in journal (Refereed)
    Abstract [en]

    The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats ( p = 0 . 015 for the interaction term). Cox re-gression showed that older BrainAGE at 5 months was associated with higher mortality risk ( p = 0 . 03 ). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.

  • 24.
    Buizza, Giulia
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems. Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy..
    Toma-Dasu, Iuliana
    Karolinska Univ Sjukhuset, Karolinska Inst, Dept Oncol Pathol, Med Radiat Phys, S-17176 Solna, Sweden..
    Lazzeroni, Marta
    Karolinska Univ Sjukhuset, Karolinska Inst, Dept Oncol Pathol, Med Radiat Phys, S-17176 Solna, Sweden..
    Paganelli, Chiara
    Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy..
    Riboldi, Marco
    Politecn Milan, CartCasLab, Dept Elect Informat & Bioengn, Piazza Leonardo Da Vinci 42, I-20133 Milan, Italy.;Ludwig Maximilians Univ Munchen, Fac Phys, Coloumbwall 1, D-5748 Garching, Germany..
    Chang, Yong Jun
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans2018In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, p. 21-29Article in journal (Refereed)
    Abstract [en]

    Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested. Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters. Results: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters. Conclusions: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.

  • 25.
    Chang, Yongjun
    et al.
    KTH, School of Technology and Health (STH).
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Effects of preprocessing in slice-level classification of interstitial lung disease based on deep convolutional networks2018In: VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017, Springer Netherlands, 2018, Vol. 27, p. 624-629Conference paper (Refereed)
    Abstract [en]

    Several preprocessing methods are applied to the automatic classification of interstitial lung disease (ILD). The proposed methods are used for the inputs to an established convolutional neural network in order to investigate the effect of those preprocessing techniques to slice-level classification accuracy. Experimental results demonstrate that the proposed preprocessing methods and a deep learning approach outperformed the case of the original images input to deep learning without preprocessing.

  • 26.
    Chowdhury, Manish
    et al.
    KTH, School of Technology and Health (STH).
    Jörgens, Daniel
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Wang, Chunliang
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. KTH, School of Technology and Health (STH), Medical Engineering, Medical Imaging.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Segmentation of Cortical Bone using Fast Level Sets2017In: MEDICAL IMAGING 2017: IMAGE PROCESSING / [ed] Styner, MA Angelini, ED, SPIE - International Society for Optical Engineering, 2017, article id UNSP 1013327Conference paper (Refereed)
    Abstract [en]

    Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.

  • 27.
    Chowdhury, Manish
    et al.
    KTH, School of Technology and Health (STH).
    Klintström, Benjamin
    KTH, School of Technology and Health (STH). Linköping University, Sweden.
    Klintström, E.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Granulometry-based trabecular bone segmentation2017In: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, p. 100-108Conference paper (Refereed)
    Abstract [en]

    The accuracy of the analyses for studying the three dimensional trabecular bone microstructure rely on the quality of the segmentation between trabecular bone and bone marrow. Such segmentation is challenging for images from computed tomography modalities that can be used in vivo due to their low contrast and resolution. For this purpose, we propose in this paper a granulometry-based segmentation method. In a first step, the trabecular thickness is estimated by using the granulometry in gray scale, which is generated by applying the opening morphological operation with ball-shaped structuring elements of different diameters. This process mimics the traditional sphere-fitting method used for estimating trabecular thickness in segmented images. The residual obtained after computing the granulometry is compared to the original gray scale value in order to obtain a measurement of how likely a voxel belongs to trabecular bone. A threshold is applied to obtain the final segmentation. Six histomorphometric parameters were computed on 14 segmented bone specimens imaged with cone-beam computed tomography (CBCT), considering micro-computed tomography (micro-CT) as the ground truth. Otsu’s thresholding and Automated Region Growing (ARG) segmentation methods were used for comparison. For three parameters (Tb.N, Tb.Th and BV/TV), the proposed segmentation algorithm yielded the highest correlations with micro-CT, while for the remaining three (Tb.Nd, Tb.Tm and Tb.Sp), its performance was comparable to ARG. The method also yielded the strongest average correlation (0.89). When Tb.Th was computed directly from the gray scale images, the correlation was superior to the binary-based methods. The results suggest that the proposed algorithm can be used for studying trabecular bone in vivo through CBCT.

  • 28.
    Chowdhury, Manish
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Rota Bulò, S.
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Kundu, M.K.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network2016In: 2016 23rd International Conference on Pattern Recognition (ICPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3134-3139, article id 7900116Conference paper (Refereed)
    Abstract [en]

    Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high- level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

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  • 29.
    Dohlmar, Frida
    et al.
    Linköping Univ, Dept Hlth Med & Caring Sci, Med Radiat Phys, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat, CMIV, Linköping, Sweden.;Linköping Univ Hosp, Ohuset Ingang 34 Pl 08, S-58185 Linköping, Sweden..
    Moren, Bjorn
    Linköping Univ, Dept Math, Linköping, Sweden..
    Sandborg, Michael
    Linköping Univ, Dept Hlth Med & Caring Sci, Med Radiat Phys, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat, CMIV, Linköping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Valdman, Alexander
    Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden..
    Larsson, Torbjorn
    Linköping Univ, Dept Math, Linköping, Sweden..
    Tedgren, Asa Carlsson
    Linköping Univ, Dept Hlth Med & Caring Sci, Med Radiat Phys, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat, CMIV, Linköping, Sweden.;Karolinska Univ Hosp, Dept Med Radiat Phys & Nucl Med, Stockholm, Sweden.;Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden..
    Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study2023In: Brachytherapy, ISSN 1538-4721, E-ISSN 1873-1449, Vol. 22, no 3, p. 407-415Article in journal (Refereed)
    Abstract [en]

    PURPOSE: The aim was to evaluate a postprocessing optimization algorithm's ability to improve the spatial properties of a clinical treatment plan while preserving the target coverage and the dose to the organs at risk. The goal was to obtain a more homogenous treatment plan, minimizing the need for manual adjustments after inverse treatment planning. MATERIALS AND METHODS: The study included 25 previously treated prostate cancer pa-tients. The treatment plans were evaluated on dose-volume histogram parameters established clin-ical and quantitative measures of the high dose volumes. The volumes of the four largest hot spots were compared and complemented with a human observer study with visual grading by eight oncologists. Statistical analysis was done using ordinal logistic regression. Weighted kappa and Fleiss' kappa were used to evaluate intra-and interobserver reliability. RESULTS: The quantitative analysis showed that there was no change in planning target volume (PTV) coverage and dose to the rectum. There were significant improvements for the adjusted treatment plan in: V150% and V200% for PTV, dose to urethra, conformal index, and dose nonhomogeneity ratio. The three largest hot spots for the adjusted treatment plan were significantly smaller compared to the clinical treatment plan. The observers preferred the adjusted treatment plan in 132 cases and the clinical in 83 cases. The observers preferred the adjusted treatment plan on homogeneity and organs at risk but preferred the clinical plan on PTV coverage. CONCLUSIONS: Quantitative analysis showed that the postadjustment optimization tool could improve the spatial properties of the treatment plans while maintaining the target coverage.

  • 30.
    Guha, Indranil
    et al.
    Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, IA, United States of America .
    Klintström, Benjamin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Klintström, Eva
    Department of Medical and Health Sciences and Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
    Zhang, Xiaoliu
    Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, IA, United States of America .
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Saha, Punam K
    Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, IA, United States of America ; Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA, United States of America.
    A comparative study of trabecular bone micro-structural measurements using different CT modalities2020In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 65, no 23, p. 235029-Article in journal (Refereed)
    Abstract [en]

    Osteoporosis, characterized by reduced bone mineral density and micro-architectural degeneration, significantly enhances fracture-risk. There are several viable methods for trabecular bone micro-imaging, which widely vary in terms of technology, reconstruction principle, spatial resolution, and acquisition time. We have performed an excised cadaveric bone specimen study to evaluate different computed tomography (CT)-imaging modalities for trabecular bone micro-structural analysis. Excised cadaveric bone specimens from the distal radius were scanned using micro-CT and four in vivo CT imaging modalities: high-resolution peripheral quantitative computed tomography (HR-pQCT), dental cone beam CT (CBCT), whole-body multi-row detector CT (MDCT), and extremity CBCT. A new algorithm was developed to optimize soft thresholding parameters for individual in vivo CT modalities for computing quantitative bone volume fraction maps. Finally, agreement of trabecular bone micro-structural measures, derived from different in vivo CT imaging, with reference measures from micro-CT imaging was examined. Observed values of most trabecular measures, including trabecular bone volume, network area, transverse and plate-rod micro-structure, thickness, and spacing, for in vivo CT modalities were higher than their micro-CT-based reference values. In general, HR-pQCT-based trabecular bone measures were closer to their reference values as compared to other in vivo CT modalities. Despite large differences in observed values of measures among modalities, high linear correlation (r ∈ [0.94 0.99]) was found between micro-CT and in vivo CT-derived measures of trabecular bone volume, transverse and plate micro-structural volume, and network area. All HR-pQCT-derived trabecular measures, except the erosion index, showed high correlation (r ∈ [0.91 0.99]). The plate-width measure showed a higher correlation (r ∈ [0.72 0.91]) among in vivo and micro-CT modalities than its counterpart binary plate-rod characterization-based measure erosion index (r ∈ [0.65 0.81]). Although a strong correlation was observed between micro-structural measures from in vivo and micro-CT imaging, large shifts in their values for in vivo modalities warrant proper scanner calibration prior to adopting in multi-site and longitudinal studies.

  • 31. Hadimeri, Ursula
    et al.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Fransson, Sven-Göran
    Stegmayr, Bernd
    Hadimeri, Henrik
    Fistula diameter correlates with echocardiographic characteristics in stable hemodialysis patients2015In: Nephrology@ Point of Care, ISSN 2059-3007, Vol. 1, no 1Article in journal (Refereed)
    Abstract [en]

    Aims and background: Left ventricular hypertrophy (LVH) is a common finding in hemodialysis patients. The aim of the present study was to investigate if the diameter of the distal radiocephalic fistula could influence left ventricular variables in stable hemodialysis patients. Methods: Nineteen patients were investigated. Measurements of the diameter of the arteriovenous (AV) fistula were performed in 4 different locations. The patients were investigated using M-mode recordings and measurements in the 2D image. Doppler ultrasound was also performed. Transonic measurements were performed after ultrasound investigation. Results: Fistula mean and maximal diameter correlated with left ventricular characteristics. Fistula flow correlated neither with the left ventricular characteristics nor with fistula diameters. Conclusions: The maximal diameter of the distal AV fistula seems to be a sensitive marker of LVH in stable hemodialysis patients.

  • 32. Holstensson, Maria
    et al.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Poludniowski, Gavin
    Sanches Crespo, Alejandro
    Savitcheva, Irina
    Öberg, Michael
    Grybäck, Per
    Gabrielson, Stefan
    Sandqvist, Patricia
    Bartholdson, Erika
    Axelsson, Rimma
    Comparison of acquisition protocols for ventilation/perfusion SPECT - a Monte Carlo study2019In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 64, no 23, article id 235018Article in journal (Refereed)
    Abstract [en]

    One of the most commonly used imaging techniques for diagnosing pulmonary embolism (PE) is ventilation/perfusion (V/P) scintigraphy. The aim of this study was to evaluate the performance of the currently used imaging protocols for V/P single photon emission computed tomography (V/P SPECT) at two nuclear medicine department sites and to investigate the effect of altering important protocol parameters. &amp;#13; &amp;#13; The Monte Carlo technique was used to simulate 4D digital phantoms with perfusion defects. Six imaging protocols were included in the study and a total of 72 digital patients were simulated. Six dually trained radiologists/nuclear medicine physicians reviewed the images and reported all perfusion mismatch findings. The radiologists also visually graded the image quality. &amp;#13; &amp;#13; No statistically significant differences in diagnostic performance were found between the studied protocols, but visual grading analysis pointed out one protocol as significantly superior to four of the other protocols. Considering the study results, we have decided to harmonize our clinical protocols for imaging patients with suspected PE. The administered Technegas and macro aggregated albumin activities have been altered, a low energy all purpose collimator is used instead of a low energy high resolution collimator and the acquisition times have been lowered.

  • 33.
    Jensen, Kristin
    et al.
    Oslo Univ Hosp, Dept Diagnost Phys, N-0454 Oslo, Norway.;Univ Oslo, Dept Phys, POB 1048 Blindern, N-0316 Oslo, Norway.;Oslo & Akershus Univ Coll Appl Sci, Dept Life Sci & Hlth, POB 4 St Olavs Plass, N-0130 Oslo, Norway..
    Andersen, Hilde Kjernlie
    Oslo Univ Hosp, Dept Diagnost Phys, N-0454 Oslo, Norway..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Osteras, Bjorn Helge
    Oslo Univ Hosp, Dept Diagnost Phys, N-0454 Oslo, Norway.;Univ Oslo, Inst Clin Med, Oslo, Norway..
    Aarsnes, Anette
    Oslo Univ Hosp, Dept Diagnost Phys, N-0454 Oslo, Norway..
    Tingberg, Anders
    Lund Univ, Dept Med Radiat Phys, Skane Univ Hosp, Malmo, Sweden..
    Fosse, Erik
    Univ Oslo, Inst Clin Med, Oslo, Norway.;Natl Hosp Norway, Intervent Ctr, Oslo, Norway..
    Martinsen, Anne Catrine
    Oslo Univ Hosp, Dept Diagnost Phys, N-0454 Oslo, Norway.;Univ Oslo, Dept Phys, POB 1048 Blindern, N-0316 Oslo, Norway..
    Quantitative Measurements Versus Receiver Operating Characteristics and Visual Grading Regression in CT Images Reconstructed with Iterative Reconstruction: A Phantom Study2018In: Academic Radiology, ISSN 1076-6332, E-ISSN 1878-4046, Vol. 25, no 4, p. 509-518Article in journal (Refereed)
    Abstract [en]

    Rationale and Objectives: This study aimed to evaluate the correlation of quantitative measurements with visual grading regression (VGR) and receiver operating characteristics (ROC) analysis in computed tomography (CT) images reconstructed with iterative reconstruction. Materials and Methods: CT scans on a liver phantom were performed on CT scanners from GE, Philips, and Toshiba at three dose levels. Images were reconstructed with filtered back projection (FBP) and hybrid iterative techniques (ASiR, iDose, and AIDR 3D of different strengths). Images were visually assessed by five readers using a four- and five-grade ordinal scale for liver low contrast lesions and for 10 image quality criteria. The results were analyzed with ROC and VGR. Standard deviation, signal-to-noise ratios, and contrast to-noise ratios were measured in the images. Results: All data were compared to FBP. The results of the quantitative measurements were improved for all algorithms. ROC analysis showed improved lesion detection with ASiR and AIDR and decreased lesion detection with iDose. VGR found improved noise properties for all algorithms, increased sharpness with iDose and AIDR, and decreased artifacts from the spine with AIDR, whereas iDose increased the artifacts from the spine. The contrast in the spine decreased with ASiR and iDose. Conclusions: Improved quantitative measurements in images reconstructed with iterative reconstruction compared to FBP are not equivalent to improved diagnostic image accuracy.

  • 34.
    Jörgens, Daniel
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Clustering of tensor votes for inference of fibre orientations in DTI data2016Conference paper (Other academic)
    Abstract [en]

    mong the various diffusion MRI techniques, diffusion ten-sor imaging (DTI) is still most commonly used in clinicalpractice in order to investigate connectivity and fibre anatomyin the human brain. Besides its apparent advantages of a shortacquisition time and noise robustness compared to other tech-niques, it suffers from its major weakness of assuming a sin-gle fibre model in each voxel. This constitutes a problem forDTI fibre tracking algorithms in regions with crossing fibres.Methods approaching this problem in a postprocessing stepemploy diffusion-like techniques to correct the directional in-formation. We propose an extension of tensor voting in whichinformation from voxels with a single fibre is used to inferorientation distributions in multi fibre voxels. The method isable to resolve multiple fibre orientations by clustering tensorvotes instead of adding them up. Moreover, a new vote cast-ing procedure is proposed which is appropriate even for smallneighbourhoods. To account for the locality of DTI data, weuse a small neighbourhood for distributing information at atime, but apply the algorithm iteratively to close larger gaps.The method shows promising results in both synthetic casesand for processing DTI-data of the human brain.

  • 35.
    Jörgens, Daniel
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Learning a single step of streamline tractography based on neural networks2018In: Computational Diffusion MRI: MICCAI Workshop on Computational Diffusion MRI, CDMRI 2017, Quebec, 10 September 2017, Springer Nature , 2018, p. 103-116Chapter in book (Other academic)
    Abstract [en]

    This paper focuses on predicting a single step of streamline tractography from diffusion magnetic resonance imaging data by using different predictors based on neural networks. We train 18 different classifiers in order to assess the effect of including neighbourhood information in the learning step or as a post processing step. Moreover, the performance using four different post processing approaches as well as the variation of the number of classes resulting in a total of 60 experimental configurations are assessed. Further, a comparison to 12 regression-based networks is performed and the effect of including several streamline steps in the network input is investigated. All networks are trained and tested on the ISMRM 2015 tractography challenge data. Our results do not indicate a clear improvement when using neighbouring data (regardless if it used as an input or as a post processing). Also, the linear interpolation of the diffusion data does not outperform the less expensive nearest neighbour approach. As opposed to that, using a linear model on top of the output of the classifiers is beneficial and—in combination with at least 200 classes—resulted in a similar performance as the regression approach. Finally, providing the networks with additional curvature information led to a clear improvement of prediction performance. Our analysis of accuracy based on average angular errors suggests that also considering spatial location in the learning step might further improve machine learning-based streamline tractography algorithms.

  • 36.
    Jörgens, Daniel
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Steering second-order tensor voting by vote clustering2016Conference paper (Refereed)
    Abstract [en]

    Among the various diffusion MRI techniques, diffusion tensor imaging (DTI) is still most commonly used in clinical practice in order to investigate connectivity and fibre anatomy in the human brain. Besides its apparent advantages of a short acquisition time and noise robustness compared to other techniques, it suffers from its major weakness of assuming a single fibre model in each voxel. This constitutes a problem for DTI fibre tracking algorithms in regions with crossing fibres. Methods approaching this problem in a postprocessing step employ diffusion-like techniques to correct the directional information. We propose an extension of tensor voting in which information from voxels with a single fibre is used to infer orientation distributions in multi fibre voxels. The method is able to resolve multiple fibre orientations by clustering tensor votes instead of adding them up. Moreover, a new vote casting procedure is proposed which is appropriate even for small neighbourhoods. To account for the locality of DTI data, we use a small neighbourhood for distributing information at a time, but apply the algorithm iteratively to close larger gaps. The method shows promising results in both synthetic cases and for processing DTI-data of the human brain.

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  • 37. Karlsson, Anette
    et al.
    Leinhard, Olof Dahlqvist
    Aslund, Ulrika
    West, Janne
    Romu, Thobias
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linkoping Univ, Sweden.
    Zsigmond, Peter
    Peolsson, Anneli
    An Investigation of Fat Infiltration of the Multifidus Muscle in Patients With Severe Neck Symptoms Associated With Chronic Whiplash-Associated Disorder2016In: Journal of Orthopaedic and Sports Physical Therapy, ISSN 0190-6011, E-ISSN 1938-1344, Vol. 46, no 10, p. 886-893Article in journal (Refereed)
    Abstract [en]

    STUDY DESIGN: Cross-sectional study. BACKGROUND: Findings of fat infiltration in cervical spine multifidus, as a sign of degenerative morphometric changes due to whiplash injury, need to be verified. OBJECTIVES: To develop a method using water/fat magnetic resonance imaging (MRI) to investigate fat infiltration and cross-sectional area of multifidus muscle in individuals with whiplash associated disorders (WADS) compared to healthy controls. METHODS: Fat infiltration and cross-sectional area in the multifidus muscles spanning the C4 to C7 segmental levels were investigated by manual segmentation using water/fat-separated MRI in 31 participants with WAD and 31 controls, matched for age and sex. RESULTS: Based on average values for data spanning C4 to C7, participants with severe disability related to WAD had 38% greater muscular fat infiltration compared to healthy controls (P = .03) and 45% greater fat infiltration compared to those with mild to moderate disability related to WAD (P = .02). There were no significant differences between those with mild to moderate disability and healthy controls. No significant differences between groups were found for multifidus cross-sectional area. Significant differences were observed for both cross-sectional area and fat infiltration between segmental levels. CONCLUSION: Participants with severe disability after a whiplash injury had higher fat infiltration in the multifidus compared to controls and to those with mild/moderate disability secondary to WAD. Earlier reported findings using T1-weighted MRI were reproduced using refined imaging technology. The results of the study also indicate a risk when segmenting single cross-sectional slices, as both cross-sectional area and fat infiltration differ between cervical levels.

  • 38.
    Kataria, B.
    et al.
    Linköping Univ, Dept Radiol, Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden..
    Althen, J. Nilsson
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Dept Med Phys, Linköping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Persson, A.
    Linköping Univ, Dept Radiol, Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden..
    Sokjer, H.
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden..
    Sandborg, M.
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden.;Linköping Univ, Dept Med Phys, Linköping, Sweden..
    Image Quality And Potential Dose Reduction Using Advanced Modeled Iterative Reconstruction (ADMIRE) In Abdominal Ct - A Review2021In: Radiation Protection Dosimetry, ISSN 0144-8420, E-ISSN 1742-3406, Vol. 195, no 3-4, p. 177-187Article, review/survey (Refereed)
    Abstract [en]

    Traditional filtered back projection (FBP) reconstruction methods have served the computed tomography (CT) community well for over 40 years. With the increased use of CT during the last decades, efforts to minimise patient exposure, while maintaining sufficient or improved image quality, have led to the development of model-based iterative reconstruction (MBIR) algorithms from several vendors. The usefulness of the advanced modeled iterative reconstruction (ADMIRE) (Siemens Healthineers) MBIR in abdominal CT is reviewed and its noise suppression and/or dose reduction possibilities explored. Quantitative and qualitative methods with phantom and human subjects were used. Assessment of the quality of phantom images will not always correlate positively with those of patient images, particularly at the higher strength of the ADMIRE algorithm. With few exceptions, ADMIRE Strength 3 typically allows for substantial noise reduction compared to FBP and hence to significant (approximate to 30%) patient dose reductions. The size of the dose reductions depends on the diagnostic task.

  • 39.
    Kataria, Bharti
    et al.
    Linkoping Univ, Dept Radiol, Dept Med & Hlth Sci, Ctr Med Image Sci & Visualizat CMIV, S-58185 Linkoping, Sweden..
    Althen, Jonas Nilsson
    Linkoping Univ, Dept Med & Hlth Sci, Dept Med Phys, Linkoping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Persson, Anders
    Linkoping Univ, Dept Radiol, Dept Med & Hlth Sci, Ctr Med Image Sci & Visualizat CMIV, S-58185 Linkoping, Sweden..
    Sokjer, Hannibal
    Linkoping Univ, Dept Med & Hlth Sci, S-58183 Linkoping, Sweden..
    Sandborg, Michael
    Linkoping Univ, Dept Med & Hlth Sci, Dept Med Phys, Linkoping, Sweden..
    Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction2018In: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 28, no 6, p. 2464-2473Article in journal (Refereed)
    Abstract [en]

    To estimate potential dose reduction in abdominal CT by visually comparing images reconstructed with filtered back projection (FBP) and strengths of 3 and 5 of a specific MBIR. A dual-source scanner was used to obtain three data sets each for 50 recruited patients with 30, 70 and 100% tube loads (mean CTDIvol 1.9, 3.4 and 6.2 mGy). Six image criteria were assessed independently by five radiologists. Potential dose reduction was estimated with Visual Grading Regression (VGR). Comparing 30 and 70% tube load, improved image quality was observed as a significant strong effect of log tube load and reconstruction method with potential dose reduction relative to FBP of 22-47% for MBIR strength 3 (p < 0.001). For MBIR strength 5 no dose reduction was possible for image criteria 1 (liver parenchyma), but dose reduction between 34 and 74% was achieved for other criteria. Interobserver reliability showed agreement of 71-76% (kappa (w) 0.201-0.286) and intra-observer reliability of 82-96% (kappa (w) 0.525-0.783). MBIR showed improved image quality compared to FBP with positive correlation between MBIR strength and increasing potential dose reduction for all but one image criterion. aEuro cent MBIR's main advantage is its de-noising properties, which facilitates dose reduction. aEuro cent MBIR allows for potential dose reduction in relation to FBP. aEuro cent Visual Grading Regression (VGR) produces direct numerical estimates of potential dose reduction. aEuro cent MBIR strengths 3 and 5 dose reductions were 22-34 and 34-74%. aEuro cent MBIR strength 5 demonstrates inferior performance for liver parenchyma.

  • 40.
    Kataria, Bharti
    et al.
    Linkoping Univ, Dept Radiol, Linkoping, Sweden.;Linkoping Univ, Dept Med & Hlth Sci, Linkoping, Sweden.;Linkoping Univ, Ctr Med Image Sci & Visualizat CMIV, Linkoping, Sweden..
    Althén, Jonas Nilsson
    Linkoping Univ, Dept Med & Hlth Sci, Linkoping, Sweden.;Linkoping Univ, Dept Med Phys, Linkoping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Persson, Anders
    Linkoping Univ, Dept Radiol, Linkoping, Sweden.;Linkoping Univ, Dept Med & Hlth Sci, Linkoping, Sweden.;Linkoping Univ, Ctr Med Image Sci & Visualizat CMIV, Linkoping, Sweden..
    Sökjer, Hannibal
    Linkoping Univ, Dept Med & Hlth Sci, Linkoping, Sweden..
    Sandborg, Michael
    Linkoping Univ, Dept Med & Hlth Sci, Linkoping, Sweden.;Linkoping Univ, Ctr Med Image Sci & Visualizat CMIV, Linkoping, Sweden.;Linkoping Univ, Dept Med Phys, Linkoping, Sweden..
    Image quality and pathology assessment in CT Urography: when is the low-dose series sufficient?2019In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 19, no 1, article id 64Article in journal (Refereed)
    Abstract [en]

    Background Our aim was to compare CT images from native, nephrographic and excretory phases using image quality criteria as well as the detection of positive pathological findings in CT Urography, to explore if the radiation burden to the younger group of patients or patients with negative outcomes can be reduced. Methods This is a retrospective study of 40 patients who underwent a CT Urography examination on a 192-slice dual source scanner. Image quality was assessed for four specific renal image criteria from the European guidelines, together with pathological assessment in three categories: renal, other abdominal, and incidental findings without clinical significance. Each phase was assessed individually by three radiologists with varying experience using a graded scale. Certainty scores were derived based on the graded assessments. Statistical analysis was performed using visual grading regression (VGR). The limit for significance was set at p = 0.05. Results For visual reproduction of the renal parenchyma and renal arteries, the image quality was judged better for the nephrogram phase (p < 0.001), whereas renal pelvis/calyces and proximal ureters were better reproduced in the excretory phase compared to the native phase (p < 0.001). Similarly, significantly higher certainty scores were obtained in the nephrogram phase for renal parenchyma and renal arteries, but in the excretory phase for renal pelvis/calyxes and proximal ureters. Assessment of pathology in the three categories showed no statistically significant differences between the three phases. Certainty scores for assessment of pathology, however, showed a significantly higher certainty for renal pathology when comparing the native phase to nephrogram and excretory phase and a significantly higher score for nephrographic phase but only for incidental findings. Conclusion Visualisation of renal anatomy was as expected with each post-contrast phase showing favourable scores compared to the native phase. No statistically significant differences in the assessment of pathology were found between the three phases. The low-dose CT (LDCT) seems to be sufficient in differentiating between normal and pathological examinations. To reduce the radiation burden in certain patient groups, the LDCT could be considered a suitable alternative as a first line imaging method. However, radiologists should be aware of its limitations.

  • 41.
    Kataria, Bharti
    et al.
    Linkoping Univ, Ctr Med Image Sci & Visualizat CMIV, Dept Radiol, Dept Med & Hlth Sci, S-58185 Linkoping, Sweden..
    Nilsson Althén, Jonas
    Linkoping Univ, Dept Med Phys, Dept Med & Hlth Sci, S-58185 Linkoping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Persson, Anders
    Linkoping Univ, Ctr Med Image Sci & Visualizat CMIV, Dept Radiol, Dept Med & Hlth Sci, S-58185 Linkoping, Sweden..
    Sökjer, Hannibal
    Linkoping Univ, Dept Med & Hlth Sci, S-58183 Linkoping, Sweden..
    Sandborg, Michael
    Linkoping Univ, Ctr Med Image Sci & Visualizat CMIV, Dept Med Phys, Dept Med & Hlth Sci, S-58185 Linkoping, Sweden..
    Assessment of image quality in abdominal computed tomography: Effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction2020In: European Journal of Radiology, ISSN 0720-048X, E-ISSN 1872-7727, Vol. 122, article id 108703Article in journal (Refereed)
    Abstract [en]

    Purpose: To determine the effect of tube load, model-based iterative reconstruction (MBIR) strength and slice thickness in abdominal CT using visual comparison of multi-planar reconstruction images. Method: Five image criteria were assessed independently by four radiologists on two data sets at 42- and 98-mAs tube loads for 25 patients examined on a 192-slice dual-source CT scanner. Effect of tube load, MBIR strength, slice thickness and potential dose reduction was estimated with Visual Grading Regression (VGR). Objective image quality was determined by measuring noise (SD), contrast-to-noise (CNR) ratio and noise-power spectra (NPS). Results: Comparing 42- and 98-mAs tube loads, improved image quality was observed as a strong effect of log tube load regardless of MBIR strength (p < 0.001). Comparing strength 5 to 3, better image quality was obtained for two criteria (p < 0.01), but inferior for liver parenchyma and overall image quality. Image quality was significantly better for slice thicknesses of 2mm and 3mm compared to 1mm, with potential dose reductions between 24%-41%. As expected, with decrease in slice thickness and algorithm strength, the noise power and SD (HU-values) increased, while the CNR decreased. Conclusion: Increasing slice thickness from 1 mm to 2 mm or 3 mm allows for a possible dose reduction. MBIR strength 5 shows improved image quality for three out of five criteria for 1 mm slice thickness. Increasing MBIR strength from 3 to 5 has diverse effects on image quality. Our findings do not support a general recommendation to replace strength 3 by strength 5 in clinical abdominal CT protocols. However, strength 5 may be used in task-based protocols.

  • 42.
    Kataria, Bharti
    et al.
    Linköping Univ, Dept Radiol, Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden..
    Oman, Jenny
    Linköping Univ, Dept Radiol, Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden..
    Sandborg, Michael
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Linköping, Sweden.;Linköping Univ, Dept Med Phys, Linköping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography2023In: EUROPEAN JOURNAL OF RADIOLOGY OPEN, ISSN 2352-0477, Vol. 10, article id 100490Article in journal (Refereed)
    Abstract [en]

    Objectives: Images reconstructed with higher strengths of iterative reconstruction algorithms may impair radi-ologists' subjective perception and diagnostic performance due to changes in the amplitude of different spatial frequencies of noise. The aim of the present study was to ascertain if radiologists can learn to adapt to the unusual appearance of images produced by higher strengths of Advanced modeled iterative reconstruction al-gorithm (ADMIRE). Methods: Two previously published studies evaluated the performance of ADMIRE in non-contrast and contrast -enhanced abdominal CT. Images from 25 (first material) and 50 (second material) patients, were reconstructed with ADMIRE strengths 3, 5 (AD3, AD5) and filtered back projection (FBP). Radiologists assessed the images using image criteria from the European guidelines for quality criteria in CT. To ascertain if there was a learning effect, new analyses of data from the two studies was performed by introducing a time variable in the mixed -effects ordinal logistic regression model. Results: In both materials, a significant negative attitude to ADMIRE 5 at the beginning of the viewing was strengthened during the progress of the reviews for both liver parenchyma (first material:-0.70, p < 0.01, second material:-0.96, p < 0.001) and overall image quality (first material:-0.59, p < 0.05, second materi-al::-1.26, p < 0.001). For ADMIRE 3, an early positive attitude for the algorithm was noted, with no significant change over time for all criteria except one (overall image quality), where a significant negative trend over time (-1.08, p < 0.001) was seen in the second material.Conclusions: With progression of reviews in both materials, an increasing dislike for ADMIRE 5 images was apparent for two image criteria. In this time perspective (weeks or months), no learning effect towards accepting the algorithm could be demonstrated.

  • 43.
    Klintström, Benjamin
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Henriksson, Lilian
    Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, SE-58185 Linköping, Sweden.;Linköping Univ, Dept Radiol, SE-58185 Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, SE-58185 Linköping, Sweden..
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Malusek, Alexandr
    Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, SE-58185 Linköping, Sweden.;Linköping Univ, Radiat Phys, Dept Hlth Med & Caring Sci, SE-58183 Linköping, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Woisetschlager, Mischa
    Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, SE-58185 Linköping, Sweden.;Linköping Univ, Dept Radiol, SE-58185 Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, SE-58185 Linköping, Sweden..
    Klintström, Eva
    Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, SE-58185 Linköping, Sweden.;Linköping Univ, Dept Radiol, SE-58185 Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, SE-58185 Linköping, Sweden..
    Photon-counting detector CT and energy-integrating detector CT for trabecular bone microstructure analysis of cubic specimens from human radius2022In: European radiology experimental, ISSN 2509-9280, Vol. 6, no 1, article id 31Article in journal (Refereed)
    Abstract [en]

    Background As bone microstructure is known to impact bone strength, the aim of this in vitro study was to evaluate if the emerging photon-counting detector computed tomography (PCD-CT) technique may be used for measurements of trabecular bone structures like thickness, separation, nodes, spacing and bone volume fraction. Methods Fourteen cubic sections of human radius were scanned with two multislice CT devices, one PCD-CT and one energy-integrating detector CT (EID-CT), using micro-CT as a reference standard. The protocols for PCD-CT and EID-CT were those recommended for inner- and middle-ear structures, although at higher mAs values: PCD-CT at 450 mAs and EID-CT at 600 (dose equivalent to PCD-CT) and 1000 mAs. Average measurements of the five bone parameters as well as dispersion measurements of thickness, separation and spacing were calculated using a three-dimensional automated region growing (ARG) algorithm. Spearman correlations with micro-CT were computed. Results Correlations with micro-CT, for PCD-CT and EID-CT, ranged from 0.64 to 0.98 for all parameters except for dispersion of thickness, which did not show a significant correlation (p = 0.078 to 0.892). PCD-CT had seven of the eight parameters with correlations rho > 0.7 and three rho > 0.9. The dose-equivalent EID-CT instead had four parameters with correlations rho > 0.7 and only one rho > 0.9. Conclusions In this in vitro study of radius specimens, strong correlations were found between trabecular bone structure parameters computed from PCD-CT data when compared to micro-CT. This suggests that PCD-CT might be useful for analysing bone microstructure in the peripheral human skeleton.

  • 44.
    Klintström, Benjamin
    et al.
    KTH, School of Technology and Health (STH).
    Klintström, E.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Feature space clustering for trabecular bone segmentation2017In: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, p. 65-75Conference paper (Refereed)
    Abstract [en]

    Trabecular bone structure has been shown to impact bone strength and fracture risk. In vitro, this structure can be measured by micro-computed tomography (micro-CT). For clinical use, it would be valuable if multi-slice computed tomography (MSCT) could be used to analyse trabecular bone structure. One important step in the analysis is image volume segmentation. Previous segmentation techniques have either been computer resource intensive or produced sub-optimal results when used on MSCT data. This paper proposes a new segmentation method that tries to balance good results against computational complexity. Material. Fourteen human radius specimens where scanned with MSCT and segmented using the proposed method as well as two segmentation methods previously used to segment trabecular bone (Otsu and Automated Region Growing (ARG)). The proposed method (named FCH) uses a combination of feature space clustering, edge detection and hysteresis thresholding. For evaluation, we computed correlations with the reference method micro-CT for 7 structure parameters and measured segmentation time. Results. Correlations with micro-CT were highest for FCH in 3 cases, highest for ARG in 3 cases, and in general lower for Otsu. Both FCH and ARG had correlations higher than 0.80 for all parameters, except for trabecular thickness and trabecular termini. FCH was 60 times slower than Otsu, but 5 times faster than ARG. Discussion. The high correlations with micro-CT suggest that with a suitable segmentation method it might be possible to analyse trabecular bone structure using MSCT-machines. The proposed segmentation method may represent a useful balance between speed and accuracy.

  • 45. Klintström, Eva
    et al.
    Klintström, Benjamin
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH).
    Brismar, Torkel B.
    Pahr, Dieter H.
    Smedby, Örjan
    KTH, School of Technology and Health (STH).
    Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an In Vitro Study Using Finite Element Analysis2016In: PLOS ONE, E-ISSN 1932-6203, Vol. 11, no 8, article id e0161101Article in journal (Refereed)
    Abstract [en]

    Stiffness and shear moduli of human trabecular bone may be analyzed in vivo by finite element (FE) analysis from image data obtained by clinical imaging equipment such as high resolution peripheral quantitative computed tomography (HR-pQCT). In clinical practice today, this is done in the peripheral skeleton like the wrist and heel. In this cadaveric bone study, fourteen bone specimens from the wrist were imaged by two dental cone beam computed tomography (CBCT) devices and one HR-pQCT device as well as by dual energy X-ray absorptiometry (DXA). Histomorphometric measurements from micro-CT data were used as gold standard. The image processing was done with an in-house developed code based on the automated region growing (ARG) algorithm. Evaluation of how well stiffness (Young's modulus E3) and minimum shear modulus from the 12, 13, or 23 could be predicted from the CBCT and HR-pQCT imaging data was studied and compared to FE analysis from the micro-CT imaging data. Strong correlations were found between the clinical machines and micro-CT regarding trabecular bone structure parameters, such as bone volume over total volume, trabecular thickness, trabecular number and trabecular nodes (varying from 0.79 to 0.96). The two CBCT devices as well as the HR-pQCT showed the ability to predict stiffness and shear, with adjusted R-2-values between 0.78 and 0.92, based on data derived through our in-house developed code based on the ARG algorithm. These findings indicate that clinically used CBCT may be a feasible method for clinical studies of bone structure and mechanical properties in future osteoporosis research.

  • 46.
    Klintström, Eva
    et al.
    Linköping Univ, Dept Med & Hlth Sci, Campus US, S-58185 Linköping, Sweden.;Linköping Univ, Ctr Med Image Sci & Visualizat CMIV, Campus US, S-58185 Linköping, Sweden..
    Klintström, Benjamin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Pahr, Dieter
    Vienna Univ Technol, Inst Lightweight Design & Struct Biomech, Vienna, Austria..
    Brismar, Torkel B.
    Karolinska Univ Hosp, Karolinska Inst, Dept Clin Sci Intervent & Technol, Stockholm, Sweden.;Karolinska Univ Hosp, Dept Radiol, Stockholm, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Linköping Univ, Dept Med & Hlth Sci, Linköping, Sweden..
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Direct estimation of human trabecular bone stiffness using cone beam computed tomography2018In: Oral surgery, oral medicine, oral pathology and oral radiology, ISSN 2212-4403, E-ISSN 2212-4411, Vol. 126, no 1, p. 72-82Article in journal (Refereed)
    Abstract [en]

    Objectives. The aim of this study was to evaluate the possibility of estimating the biomechanical properties of trabecular bone through finite element simulations by using dental cone beam computed tomography data. Study Design. Fourteen human radius specimens were scanned in 3 cone beam computed tomography devices: 3-D Accuitomo 80 (J. Morita MFG., Kyoto, Japan), NewTom 5 G (QR Verona, Verona, Italy), and Verity (Planmed, Helsinki, Finland). The imaging data were segmented by using 2 different methods. Stiffness (Young modulus), shear moduli, and the size and shape of the stiffness tensor were studied. Corresponding evaluations by using micro-CT were regarded as the reference standard. Results. The 3-D Accuitomo 80 (J. Morita MFG., Kyoto, Japan) showed good performance in estimating stiffness and shear moduli but was sensitive to the choice of segmentation method. Newtom 5 G (QR Verona, Verona, Italy) and Verity (Planmed, Helsinki, Finland) yielded good correlations, but they were not as strong as Accuitomo 80 U. Morita MFG., Kyoto, Japan). The cone beam computed tomography devices overestimated both stiffness and shear compared with the micro-CT estimations. Conclusions. Finite element-based calculations of biomechanics from cone beam computed tomography data are feasible, with strong correlations for the Accuitomo 80 scanner a. Morita MFG., Kyoto, Japan) combined with an appropriate segmentation method. Such measurements might be useful for predicting implant survival by in vivo estimations of bone properties.

  • 47.
    Kumar, Neeraj
    et al.
    Univ Illinois, Dept Pathol, Chicago, IL 60607 USA..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Sethi, Amit
    ITT Bombay, Dept Elect Engn, Mumbai 400076, Maharashtra, India..
    A Multi-Organ Nucleus Segmentation Challenge2020In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 39, no 5, p. 1380-1391Article in journal (Refereed)
    Abstract [en]

    Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

  • 48. Lidayova, Kristina
    et al.
    Frimmel, Hans
    Bengtsson, Ewert
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation2017In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, no 2, article id 024004Article in journal (Refereed)
    Abstract [en]

    Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70 s, and the technique was successful also in detecting very distal artery branches, e. g., in the foot.

  • 49. Lidayová, K.
    et al.
    Betancur, D. A. G.
    Frimmel, H.
    Hoyos, M. H.
    Orkisz, M.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Airway-tree segmentation in subjects with acute respiratory distress syndrome2017In: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, p. 76-87Conference paper (Refereed)
    Abstract [en]

    Acute respiratory distress syndrome (ARDS) is associated with a high mortality rate in intensive care units. To lower the number of fatal cases, it is necessary to customize the mechanical ventilator parameters according to the patient’s clinical condition. For this, lung segmentation is required to assess aeration and alveolar recruitment. Airway segmentation may be used to reach a more accurate lung segmentation. In this paper, we seek to improve lung segmentation results by proposing a novel automatic airway-tree segmentation that is able to address the heterogeneity of ARDS pathology by handling various lung intensities differently. The method detects a simplified airway skeleton, thereby obtains a set of seed points together with an approximate radius and intensity range related to each of the points. These seeds are the input for an onion-kernel region-growing segmentation algorithm where knowledge about radius and intensity range restricts the possible leakage in the parenchyma. The method was evaluated qualitatively on 70 thoracic Computed Tomography volumes of subjects with ARDS, acquired at significantly different mechanical ventilation conditions. It found a large proportion of airway branches including tiny poorly-aerated bronchi. Quantitative evaluation was performed indirectly and showed that the resulting airway segmentation provides important anatomic landmarks. Their correspondences are needed to help a registration-based segmentation of the lungs in difficult ARDS cases where the lung boundary contrast is completely missing. The proposed method takes an average time of 43 s to process a thoracic volume which is valuable for the clinical use.

  • 50. Lidayová, K.
    et al.
    Frimmel, H.
    Wang, Chunliang
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.
    Bengtsson, E.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.
    Fast vascular skeleton extraction algorithm2016In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, p. 67-75Article in journal (Refereed)
    Abstract [en]

    Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels - nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.

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