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  • 1.
    Astaraki, Mehdi
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning. Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Buizza, Giulia
    Politecn Milan, Dept Elect Informat & Bioengn, Piazza Leonardo da Vinci 42, I-20133 Milan, Italy..
    Toma-Dasu, Iuliana
    Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.;Stockholm Univ, Dept Phys, SE-10691 Stockholm, Sweden..
    Lazzeroni, Marta
    Karolinska Inst, Dept Oncol Pathol, Karolinska Univ Sjukhuset, SE-17176 Stockholm, Sweden.;Stockholm Univ, Dept Phys, SE-10691 Stockholm, Sweden..
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method2019Ingår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, s. 58-65Artikel i tidskrift (Refereegranskat)
    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.

  • 2.
    Buizza, Giulia
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem. 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, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.
    Smedby, Örjan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Wang, Chunliang
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Medicinsk avbildning.
    Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans2018Ingår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 54, s. 21-29Artikel i tidskrift (Refereegranskat)
    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.

  • 3.
    Bujila, Robert
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Fysik.
    Fransson, Annette
    Poludniowski, Gavin
    Practical approaches to approximating MTF and NPS in CT with an example application to task-based observer studies2017Ingår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 33, s. 16-25Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Purpose: To investigate two methods of approximating the Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) in computed tomography (CT) for a range of scan parameters, from limited image acquisitions. Methods: The two methods consist of 1) using a linear systems approach to approximate the NPS for different filtered backprojection (FBP) kernels with a filter function derived from the kernel ratio of determined MTFs and 2) using an empirical fitted model to approximate the MTF and NPS. In both cases a scaling function accounts for variations in mAs and kV. The two methods of approximating the MTF/ NPS are further investigated by comparing image quality figure of merits (FOM) d' and AUC calculated using approximations of the MTF/NPS and MTF/NPS that have been determined for different mAs/kV levels and reconstruction kernels. Results: The greatest RMSE for NPS approximated for a range of mAs/kVp/convolution kernels using both methods and compared to determined NPS was 0.05 of the peak value. The RMSE for FOM with the kernel ratio method were at most 0.1 for d' and 0.01 for the AUC. Using the empirical model method, the RMSE for FOM were at most 0.02 for d' and 0.001 for the AUC. Conclusions: The two methods proposed in this paper can provide a convenient way of approximating the MTF and NPS for use in, among other things, mathematical observer studies. Both methods require a relatively small number of direct determinations of NPS from scan acquisitions to model the NPS/MTF for arbitrary mAs and kV.

  • 4.
    Crafoord, Joakim
    et al.
    Karolinska Hospital, Department of Radiology.
    Mahmoud, Faaiza
    New York University, Department of Radiology.
    Kramer, Elissa L.
    New York University, Department of Radiology.
    Maguire Jr., Gerald Q.
    KTH, Tidigare Institutioner, Mikroelektronik och informationsteknik, IMIT.
    Noz, Marilyn E.
    New York University, Department of Radiology.
    Zeleznik, Michael P.
    RAHD Oncology Products, St. Lois, MO, USA.
    Comparison of two landmark based image registration methods for use with a body atlas2000Ingår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 16, nr 2, s. 75-82Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We describe preliminary work registering abdominal MRI images from three healthy male volunteers. Anatomically selected 3D homologous point pairs (landmarks), from which eigenvalues were generated to form the basis for a 3D non-affine polynomial transformation, were placed on axial slices alone and on axial, coronal and sagittal slices. Registration accuracy was judged visually by comparing superimposed 3D isosurfaces from the reference, untransformed, and transformed volume data and by comparing merged 2D slices projected fi om the transformed and reference volume data superimposed with 2D isolines. The squared sum of intensity differences between the transformed/untransformed and the reference volume was significant at the 0.05 (p >0.05) confidence level. The correlation coefficient improved by an average of 38% and the cross correlation between pixel values improved by an average of 22%. In each trial, the standard deviation of the landmarks after transformation was within one voxel and the standard error of the mean was not significantly different from zero at the 0.05 confidence level. Abdominal isosurface volume differences (between individuals) changed from an average of 14.5% before registration to 2.9% after registration. This experiment shows that it is possible to choose landmarks such that abdominal data from different subject volumes can be mapped to a common reference, and thus that it is possible to use this combined volume both to form an atlas and to warp abdominal data from an atlas to a patient volume.

  • 5. Nowik, P.
    et al.
    Poludniowski, G.
    Svensson, A.
    Bujila, Robert
    KTH, Skolan för teknikvetenskap (SCI), Fysik.
    Morsbach, F.
    Brismar, T. B.
    The synthetic localizer radiograph – A new CT scan planning method2019Ingår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 61, s. 58-63Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objective: To investigate if the conventional localizer radiograph (LR)can be replaced by a synthetic LR (SLR), generated from a low-dose spiral CT scan, for CT scan planning with minimal changes to current clinical workflows. Methods: A dosimetric comparison of SLRs and LRs was made using Monte Carlo methods. Water equivalent diameters (WEDs)of a centered and mis-centered phantom were estimated from low-dose spiral CT scans and LRs acquired at different angles. Body sizes, in the form of two lengths and two diameters obtained from SLRs and LRs, were compared for 10 patients (4 men and 6 women with a mean age of 74.8 and 76.2 years respectively)undergoing CT of thorax and abdomen. The image quality of SLRs for CT scan planning relative to LRs was rated using a 5-grade scale by four radiologists and two CT radiographers. Results: An SLR can be obtained at a comparable effective dose to that of traditionally acquired LRs: 0.14 mSv. WEDs from LRs were more affected by mis-centering than WEDs calculated from low-dose spiral scans. One significant discrepancy of estimated body sizes was observed, the broadest part of the patient that on lateral localizers showed a mean deviation of 17.7 mm (range: 7.3–28.7 mm, p < 0.001). The anteroposterior/posteroanterior SLR image quality was assessed as better compared to an LR while the same could not be shown for lateral localizers. Conclusions: SLRs based on low-dose spiral scans can replace LRs for CT planning.

  • 6.
    Siddiqui, Faaiza M.
    et al.
    New York University, Department of Radiology.
    Crafoord, Joakim
    Department of Radiology, Karolinska Hospital.
    Kramer, Elissa L.
    New York University, Department of Radiology.
    Maguire Jr., Gerald Q.
    KTH, Tidigare Institutioner, Teleinformatik.
    Noz, Marilyn E.
    New York University, Department of Radiology.
    Zeleznik, Michael P.
    Saya Systems Inc., Salt Lake City, UT, USA.
    Comparison of Two Landmark Based Image Registration Methods for Construction of a Body Atlas1999Ingår i: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 15, s. 194-195Artikel i tidskrift (Refereegranskat)
1 - 6 av 6
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