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Qin, C., Cao, Z., Fan, S., Wu, Y., Sun, Y., Politis, C., . . . Chen, X. (2019). An oral and maxillofacial navigation system for implant placement with automatic identification of fiducial points. International Journal of Computer Assisted Radiology and Surgery, 14(2), 281-289
Open this publication in new window or tab >>An oral and maxillofacial navigation system for implant placement with automatic identification of fiducial points
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2019 (English)In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 14, no 2, p. 281-289Article in journal (Refereed) Published
Abstract [en]

PurposeSurgical navigation system (SNS) has been an important tool in surgery. However, the complicated and tedious manual selection of fiducial points on preoperative images for registration affects operational efficiency to large extent. In this study, an oral and maxillofacial navigation system named BeiDou-SNS with automatic identification of fiducial points was developed and demonstrated.MethodsTo solve the fiducial selection problem, a novel method of automatic localization for titanium screw markers in preoperative images is proposed on the basis of a sequence of two local mean-shift segmentation including removal of metal artifacts. The operation of the BeiDou-SNS consists of the following key steps: The selection of fiducial points, the calibration of surgical instruments, and the registration of patient space and image space. Eight cases of patients with titanium screws as fiducial markers were carried out to analyze the accuracy of the automatic fiducial point localization algorithm. Finally, a complete phantom experiment of zygomatic implant placement surgery was performed to evaluate the whole performance of BeiDou-SNS. Results and conclusionThe coverage of Euclidean distances between fiducial marker positions selected automatically and those selected manually by an experienced dentist for all eight cases ranged from 0.373 to 0.847mm. Four implants were inserted into the 3D-printed model under the guide of BeiDou-SNS. And the maximal deviations between the actual and planned implant were 1.328mm and 2.326mm, respectively, for the entry and end point while the angular deviation ranged from 1.094 degrees to 2.395 degrees. The results demonstrate that the oral surgical navigation system with automatic identification of fiducial points can meet the requirements of the clinical surgeries.

Place, publisher, year, edition, pages
SPRINGER HEIDELBERG, 2019
Keywords
Surgical navigation, Oral and maxillofacial surgery, Automatic identification, Target registration error, Fiducial registration error
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-245152 (URN)10.1007/s11548-018-1870-z (DOI)000458112800010 ()30317436 (PubMedID)2-s2.0-8505532751 (Scopus ID)
Note

QC 20190308

Available from: 2019-03-08 Created: 2019-03-08 Last updated: 2019-03-08Bibliographically approved
Astaraki, M., Wang, C., Buizza, G., Toma-Dasu, I., Lazzeroni, M. & Smedby, Ö. (2019). Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Physica medica (Testo stampato), 60, 58-65
Open this publication in new window or tab >>Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
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2019 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, p. 58-65Article in journal (Refereed) Published
Abstract [en]

Purpose: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy. Methods: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC). Results: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROC(sALop) = 0.90 vs. AUROC(radiomic) = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values. Conclusion: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2019
Keywords
Survival prediction, Treatment response, Radiomics, Tumor heterogeneity, LONG ER, 1988, BIOMETRICS, V44, P837
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-251338 (URN)10.1016/j.ejmp.2019.03.024 (DOI)000464560200009 ()31000087 (PubMedID)2-s2.0-85063364742 (Scopus ID)
Note

QC 20190523

Available from: 2019-05-23 Created: 2019-05-23 Last updated: 2019-05-23Bibliographically approved
Mahbod, A., Chowdhury, M., Smedby, Ö. & Wang, C. (2018). Automatic brain segmentation using artificial neural networks with shape context. Pattern Recognition Letters, 101, 74-79
Open this publication in new window or tab >>Automatic brain segmentation using artificial neural networks with shape context
2018 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 101, p. 74-79Article in journal (Refereed) Published
Abstract [en]

Segmenting brain tissue from MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Many automatic or semi-automatic methods have been proposed in the literature in order to reduce the requirement of user intervention, but the level of accuracy in most cases is still inferior to that of manual segmentation. We propose a new brain segmentation method that integrates volumetric shape models into a supervised artificial neural network (ANN) framework. This is done by running a preliminary level-set based statistical shape fitting process guided by the image intensity and then passing the signed distance maps of several key structures to the ANN as feature channels, in addition to the conventional spatial-based and intensity-based image features. The so-called shape context information is expected to help the ANN to learn local adaptive classification rules instead of applying universal rules directly on the local appearance features. The proposed method was tested on a public datasets available within the open MICCAI grand challenge (MRBrainS13). The obtained average Dice coefficient were 84.78%, 88.47%, 82.76%, 95.37% and 97.73% for gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), brain (WM + GM) and intracranial volume respectively. Compared with other methods tested on the same dataset, the proposed method achieved competitive results with comparatively shorter training time.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-219889 (URN)10.1016/j.patrec.2017.11.016 (DOI)2-s2.0-85036471005 (Scopus ID)
Note

QC 20171215

Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2017-12-15Bibliographically approved
Wang, C. & Smedby, Ö. (2018). Automatic whole heart segmentation using deep learning and shape context. In: 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017: . Paper presented at 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, 10 September 2017 through 14 September 2017 (pp. 242-249). Springer, 10663
Open this publication in new window or tab >>Automatic whole heart segmentation using deep learning and shape context
2018 (English)In: 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Springer, 2018, Vol. 10663, p. 242-249Conference paper, Published paper (Refereed)
Abstract [en]

To assist 3D cardiac image analysis, we propose an automatic whole heart segmentation using a deep learning framework combined with shape context information that is encoded in volumetric shape models. The proposed processing pipeline consists of three major steps: scout segmentation with orthogonal 2D U-nets, shape context estimation and refining segmentation with U-net and shape context. The proposed method was evaluated using the MMWHS challenge data. Two sets of networks were trained separately for contrast-enhanced CT and MRI. On the 20 training datasets, using 5-fold cross-validation, the average Dice coefficients for the left ventricle, the right ventricle, the left atrium, the right atrium and the myocardium of the left ventricle were 0.895, 0.795, 0.847, 0.821, 0.807 for MRI and 0.935, 0.825, 0.908, 0.881, 0.879 for CT, respectively. Further improvement may be possible given more training data or advanced data augmentation strategy.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10663
Keywords
Deep learning, Fully convolutional network, Heart segmentation, Shape context, Statistic shape model
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-225494 (URN)10.1007/978-3-319-75541-0_26 (DOI)2-s2.0-85044467877 (Scopus ID)9783319755403 (ISBN)
Conference
8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, 10 September 2017 through 14 September 2017
Funder
Swedish Heart Lung Foundation, 2016-0609Swedish Research Council, 2014-6153
Note

QC 20180406

Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-04-06Bibliographically approved
Mahbod, A., Ellinger, I., Ecker, R., Smedby, Ö. & Wang, C. (2018). Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion. In: 15th International Conference on Image Analysis and Recognition, ICIAR 2018: . Paper presented at 27 June 2018 through 29 June 2018 (pp. 754-762). Springer
Open this publication in new window or tab >>Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion
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2018 (English)In: 15th International Conference on Image Analysis and Recognition, ICIAR 2018, Springer, 2018, p. 754-762Conference paper, Published paper (Refereed)
Abstract [en]

Breast cancer is the most common cancer type in women worldwide. Histological evaluation of the breast biopsies is a challenging task even for experienced pathologists. In this paper, we propose a fully automatic method to classify breast cancer histological images to four classes, namely normal, benign, in situ carcinoma and invasive carcinoma. The proposed method takes normalized hematoxylin and eosin stained images as input and gives the final prediction by fusing the output of two residual neural networks (ResNet) of different depth. These ResNets were first pre-trained on ImageNet images, and then fine-tuned on breast histological images. We found that our approach outperformed a previous published method by a large margin when applied on the BioImaging 2015 challenge dataset yielding an accuracy of 97.22%. Moreover, the same approach provided an excellent classification performance with an accuracy of 88.50% when applied on the ICIAR 2018 grand challenge dataset using 5-fold cross validation.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Breast cancer, Classification, Deep learning, Histological images, Biopsy, Classification (of information), Diseases, Image analysis, Medical imaging, Automatic method, Breast biopsies, Classification performance, Cross validation, Grand Challenge, Histological evaluation, Image classification
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-236392 (URN)10.1007/978-3-319-93000-8_85 (DOI)2-s2.0-85049429428 (Scopus ID)9783319929996 (ISBN)
Conference
27 June 2018 through 29 June 2018
Note

QC 20181101

Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved
Brusini, I., Carneiro, M., Wang, C., Rubin, C.-J., Ring, H., Afonso, S., . . . Andersson, L. (2018). Changes in brain architecture are consistent with altered fear processing in domestic rabbits. Proceedings of the National Academy of Sciences of the United States of America, 115(28), 7380-7385
Open this publication in new window or tab >>Changes in brain architecture are consistent with altered fear processing in domestic rabbits
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2018 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
National Academy of Sciences, 2018
Keywords
rabbit, domestication, brain morphology, magnetic resonance imaging, fear
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-232773 (URN)10.1073/pnas.1801024115 (DOI)000438050900076 ()29941556 (PubMedID)2-s2.0-85049643502 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationSwedish Research CouncilThe Swedish Brain Foundation
Note

QC 20180807

Available from: 2018-08-07 Created: 2018-08-07 Last updated: 2019-05-03
Buizza, G., Toma-Dasu, I., Lazzeroni, M., Paganelli, C., Riboldi, M., Chang, Y. J., . . . Wang, C. (2018). Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans. Physica medica (Testo stampato), 54, 21-29
Open this publication in new window or tab >>Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
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2018 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, p. 21-29Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2018
Keywords
Early tumor response, Feature extraction, Non-small cell lung cancer, PET/CT
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-238132 (URN)10.1016/j.ejmp.2018.09.003 (DOI)000447271300003 ()30337006 (PubMedID)2-s2.0-85053799575 (Scopus ID)
Funder
Swedish Childhood Cancer Foundation, MT2016-0016The Swedish Brain FoundationEU, FP7, Seventh Framework Programme, ARTFORCEEU, European Research Council, ERC-ADG-2015EU, Horizon 2020, 733008
Note

QC 20181113

Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2018-11-13Bibliographically approved
Buizza, G., Toma-Dasu, I., Lazzeroni, M., Paganelli, C., Riboldi, M., Chang, Y., . . . Wang, C. (2018). Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans. Physica medica (Testo stampato), 54, 21-29
Open this publication in new window or tab >>Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
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2018 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, p. 21-29Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Associazione Italiana di Fisica Medica, 2018
Keywords
Early tumor response, Feature extraction, Non-small cell lung cancer, PET/CT
National Category
Medical Biotechnology
Identifiers
urn:nbn:se:kth:diva-236648 (URN)10.1016/j.ejmp.2018.09.003 (DOI)000447271300003 ()2-s2.0-85053799575 (Scopus ID)
Funder
Swedish Childhood Cancer Foundation, MT2016-0016The Swedish Brain FoundationThe Cancer Research Funds of Radiumhemmet
Note

Export Date: 22 October 2018; Article; CODEN: PHYME; Correspondence Address: Wang, C.; KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, Sweden; email: chunwan@kth.se; Funding details: 673780, MOEA, Ministry of Economic Affairs; Funding details: IDEAS-ERC, FP7 Ideas: European Research Council; Funding details: MT2016-0016, Barncancerfonden; Funding details: UM, Universiteit Maastricht; Funding details: H2020-2015-17, Eurostars; Funding details: 733008, Eurostars; Funding details: FO2016-0175, Hjärnfonden; Funding details: 694812; Funding details: ERC-ADG-2015; Funding details: 10696 DuCAT, STW, Stichting voor de Technische Wetenschappen; Funding text: The Swedish Childhood Cancer Foundation , Grant No. MT2016-0016 , the Swedish Brain Foundation , Grant No. FO2016-0175 , and the Cancer Research Funds of Radiumhemmet supported this work for the study design, article preparation, data interpretation and decision to submit the article. Professor Philippe Lambin and Dr. Wouter van Elmpt from Maastricht University Medical Center are kindly acknowledged for providing the patient data set used in this study. Initial data processing and data collection were supported by: EU FP7 funding (ARTFORCE); ERC advanced grant (ERC-ADG-2015, No. 694812 – Hypoximmuno); the Dutch technology Foundation STW (Grant No. 10696 DuCAT & No. P14-19 Radiomics STRaTegy), which is the applied science division of NWO; the Technology Programme of the Ministry of Economic Affairs; SME Phase 2 (EU proposal 673780 – RAIL); EUROSTARS (DART), the European Program H2020-2015-17 (ImmunoSABR – No. 733008); the Interreg V-A Euregio Meuse-Rhine (“Euradiomics”). QC 20181113

Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2018-11-13Bibliographically approved
Commowick, O., Istace, A., Kain, M., Laurent, B., Leray, F., Simon, M., . . . Barillot, C. (2018). Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. Scientific Reports, 8, Article ID 13650.
Open this publication in new window or tab >>Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
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2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 13650Article in journal (Refereed) Published
Abstract [en]

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning,.), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:kth:diva-235440 (URN)10.1038/s41598-018-31911-7 (DOI)000444374900001 ()30209345 (PubMedID)2-s2.0-85053246791 (Scopus ID)
Note

QC 20180927

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2018-10-09Bibliographically approved
Medrano-Gracia, P., Ormiston, J., Webster, M., Beier, S., Ellis, C., Wang, C., . . . Cowan, B. (2017). A Study of Coronary Bifurcation Shape in a Normal Population. Journal of Cardiovascular Translational Research, 10(1), 82-90
Open this publication in new window or tab >>A Study of Coronary Bifurcation Shape in a Normal Population
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2017 (English)In: Journal of Cardiovascular Translational Research, ISSN 1937-5387, E-ISSN 1937-5395, Vol. 10, no 1, p. 82-90Article in journal (Refereed) Published
Abstract [en]

During percutaneous coronary intervention, stents are placed in narrowings of the arteries to restore normal blood flow. Despite improvements in stent design, deployment techniques and drug-eluting coatings, restenosis and stent thrombosis remain a significant problem. Population stent design based on statistical shape analysis may improve clinical outcomes. Computed tomographic (CT) coronary angiography scans from 211 patients with a zero calcium score, no stenoses and no intermediate artery, were used to create statistical shape models of 446 major coronary artery bifurcations (left main, first diagonal and obtuse marginal and right coronary crux). Coherent point drift was used for registration. Principal component analysis shape scores were tested against clinical risk factors, quantifying the importance of recognised shape features in intervention including size, angles and curvature. Significant differences were found in (1) vessel size and bifurcation angle between the left main and other bifurcations; (2) inlet and curvature angle between the right coronary crux and other bifurcations; and (3) size and bifurcation angle by sex. Hypertension, smoking history and diabetes did not appear to have an association with shape. Physiological diameter laws were compared, with the Huo-Kassab model having the best fit. Bifurcation coronary anatomy can be partitioned into clinically meaningful modes of variation showing significant shape differences. A computational atlas of normal coronary bifurcation shape, where disease is common, may aid in the design of new stents and deployment techniques, by providing data for bench-top testing and computational modelling of blood flow and vessel wall mechanics.

Place, publisher, year, edition, pages
SPRINGER, 2017
Keywords
Coronary bifurcation anatomy, Atlasing, CT angiography
National Category
Clinical Medicine
Identifiers
urn:nbn:se:kth:diva-205108 (URN)10.1007/s12265-016-9720-2 (DOI)000395012700009 ()28028693 (PubMedID)2-s2.0-85007507461 (Scopus ID)
Note

QC 20170626

Available from: 2017-06-26 Created: 2017-06-26 Last updated: 2017-06-26Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0442-3524

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