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  • 1
    Online Resource
    Online Resource
    MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks
    Wiley ; 2019
    In:  Medical Physics Vol. 46, No. 8 ( 2019-08), p. 3565-3581
    Details
    In: Medical Physics, Wiley, Vol. 46, No. 8 ( 2019-08), p. 3565-3581
    Abstract: Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI‐only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle‐consistent generative adversarial networks (cycle GAN), a deep‐learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. Methods and materials The cycle GAN‐based model was developed to generate sCT images in a patch‐based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one‐to‐one mapping. Dense block‐based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. Results Leave‐one‐out cross‐validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. Conclusion We developed and validated a novel learning‐based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high‐quality sCT in minutes. The proposed method offers strong potential for supporting near real‐time MRI‐only treatment planning in the brain and pelvis.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Issue
    URL: Article
    DOI: 10.1002/mp.v46.8
    DOI: 10.1002/mp.13617
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1466421-5
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  • 2
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    MRI-based synthetic CT generation using semantic random forest with iterative refinement
    IOP Publishing ; 2019
    In:  Physics in Medicine & Biology Vol. 64, No. 8 ( 2019-04-05), p. 085001-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 64, No. 8 ( 2019-04-05), p. 085001-
    Type of Medium: Online Resource
    ISSN: 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/ab0b66
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2019
    detail.hit.zdb_id: 1473501-5
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  • 3
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    High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study
    IOP Publishing ; 2020
    In:  Biomedical Physics & Engineering Express Vol. 6, No. 3 ( 2020-04-27), p. 035029-
    Details
    In: Biomedical Physics & Engineering Express, IOP Publishing, Vol. 6, No. 3 ( 2020-04-27), p. 035029-
    Type of Medium: Online Resource
    ISSN: 2057-1976
    URL: Article
    DOI: 10.1088/2057-1976/ab8a74
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2844309-3
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  • 4
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    Learning-Based Stopping Power Mapping on Dual-Energy CT for Proton Radiation Therapy
    International Journal of Particle Therapy ; 2021
    In:  International Journal of Particle Therapy Vol. 7, No. 3 ( 2021-01-01), p. 46-60
    Details
    In: International Journal of Particle Therapy, International Journal of Particle Therapy, Vol. 7, No. 3 ( 2021-01-01), p. 46-60
    Abstract: Dual-energy computed tomography (DECT) has been used to derive relative stopping power (RSP) maps by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could potentially be compromised by image noise levels and the severity of artifacts when using physics-based mapping techniques. This work presents a noise-robust learning-based method to predict RSP maps from DECT for proton radiation therapy. Materials and Methods The proposed method uses a residual attention cycle-consistent generative adversarial network to bring DECT-to-RSP mapping close to a 1-to-1 mapping by introducing an inverse RSP-to-DECT mapping. To evaluate the proposed method, we retrospectively investigated 20 head-and-neck cancer patients with DECT scans in proton radiation therapy simulation. Ground truth RSP values were assigned by calculation based on chemical compositions and acted as learning targets in the training process for DECT datasets; they were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. Results The predicted RSP maps showed an average normalized mean square error of 2.83% across the whole body volume and an average mean error less than 3% in all volumes of interest. With additional simulated noise added in DECT datasets, the proposed method still maintained a comparable performance, while the physics-based stoichiometric method suffered degraded inaccuracy from increased noise level. The average differences from ground truth in dose volume histogram metrics for clinical target volumes were less than 0.2 Gy for D95% and Dmax with no statistical significance. Maximum difference in dose volume histogram metrics of organs at risk was around 1 Gy on average. Conclusion These results strongly indicate the high accuracy of RSP maps predicted by our machine-learning–based method and show its potential feasibility for proton treatment planning and dose calculation.
    Type of Medium: Online Resource
    ISSN: 2331-5180
    URL: Article
    DOI: 10.14338/IJPT-D-20-00020.1
    Language: English
    Publisher: International Journal of Particle Therapy
    Publication Date: 2021
    detail.hit.zdb_id: 2846890-9
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  • 5
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    Online Resource
    Automatic delineation of cardiac substructures using a region‐based fully convolutional network
    Wiley ; 2021
    In:  Medical Physics Vol. 48, No. 6 ( 2021-06), p. 2867-2876
    Details
    In: Medical Physics, Wiley, Vol. 48, No. 6 ( 2021-06), p. 2867-2876
    Abstract: Radiation dose to specific cardiac substructures, such as the atria and ventricles, has been linked to post‐treatment toxicity and has shown to be more predictive of these toxicities than dose to the whole heart. A deep learning‐based algorithm for automatic generation of these contours is proposed to aid in either retrospective or prospective dosimetric studies to better understand the relationship between radiation dose and toxicities. Methods The proposed method uses a mask‐scoring regional convolutional neural network (RCNN) which consists of five major subnetworks: backbone, regional proposal network (RPN), RCNN head, mask head, and mask‐scoring head. Multiscale feature maps are learned from computed tomography (CT) via the backbone network. The RPN utilizes these feature maps to detect the location and region‐of‐interest (ROI) of all substructures, and the final three subnetworks work in series to extract structural information from these ROIs. The network is trained using 55 patient CT datasets, with 22 patients having contrast scans. Threefold cross validation (CV) is used for evaluation on 45 datasets, and a separate cohort of 10 patients are used for holdout evaluation. The proposed method is compared to a 3D UNet. Results The proposed method produces contours that are qualitatively similar to the ground truth contours. Quantitatively, the proposed method achieved average Dice score coefficients (DSCs) for the whole heart, chambers, great vessels, coronary arteries, the valves of the heart of 0.96, 0.94, 0.93, 0.66, and 0.77 respectively, outperforming the 3D UNet, which achieved DSCs of 0.92, 0.87, 0.88, 0.48, and 0.59 for the corresponding substructure groups. Mean surface distances (MSDs) between substructures segmented by the proposed method and the ground truth were 〈 2 mm except for the left anterior descending coronary artery and the mitral and tricuspid valves, and 〈 5 mm for all substructures. When dividing results into noncontrast and contrast datasets, the model performed statistically significantly better in terms of DSC, MSD, centroid mean distance (CMD), and volume difference for the chambers and whole heart with contrast. Notably, the presence of contrast did not statistically significantly affect coronary artery segmentation DSC or MSD. After network training, all substructures and the whole heart can be segmented on new datasets in less than 5 s. Conclusions A deep learning network was trained for automatic delineation of cardiac substructures based on CT alone. The proposed method can be used as a tool to investigate the relationship between cardiac substructure dose and treatment toxicities.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Issue
    URL: Article
    DOI: 10.1002/mp.v48.6
    DOI: 10.1002/mp.14810
    Language: English
    Publisher: Wiley
    Publication Date: 2021
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  • 6
    Online Resource
    Online Resource
    Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy
    Wiley ; 2020
    In:  Medical Physics Vol. 47, No. 9 ( 2020-09), p. 4416-4427
    Details
    In: Medical Physics, Wiley, Vol. 47, No. 9 ( 2020-09), p. 4416-4427
    Abstract: In intensity‐modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs‐at‐risk. However, due to uncertainties in both patient setup and relative stopping power (RSP) calculation, margins are added to the treatment volume during treatment planning, leading to higher doses to normal tissues. Cone‐beam computed tomography (CBCT) images are taken daily before treatment; however, the poor image quality of CBCT limits the use of these images for online dose calculation. In this work, we use a deep‐learning‐based method to predict RSP maps from daily CBCT images, allowing for online dose calculation in a step toward adaptive radiation therapy. Methods Twenty‐three head‐and‐neck cancer patients were simulated using a Siemens TwinBeam dual‐energy CT (DECT) scanner. Mixed‐energy scans (equivalent to a 120 kVp single‐energy CT scan) were converted to RSP maps for treatment planning. Cone‐beam computed tomography images were taken on the first day of treatment, and the planning RSP maps were registered to these images. A deep learning network based on a cycle‐GAN architecture, relying on a compound loss function designed for structural and contrast preservation, was then trained to create an RSP map from a CBCT image. Leave‐one‐out and holdout cross validations were used for evaluation, and mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio (PSNR), and structural similarity (SSIM) were used to quantify the differences between the CT‐based and CBCT‐based RSP maps. The proposed method was compared to a deformable image registration‐based method which was taken as the ground truth and two other deep learning methods. For one patient who underwent resimulation, the new planning RSP maps and CBCT images were used for further evaluation and validation. Results The CBCT‐based RSP generation method was evaluated on 23 head‐and‐neck cancer patients. From leave‐one‐out testing, the MAE between CT‐based and CBCT‐based RSP was 0.06 ± 0.01 and the ME was −0.01 ± 0.01. The proposed method statistically outperformed the comparison DL methods in terms of MAE and ME when compared to the planning CT. In terms of dose comparison, the mean gamma passing rate at 3%/3 mm was 94% when three‐dimensional (3D) gamma index was calculated per plan and 96% when gamma index was calculated per field. Conclusions The proposed method provides sufficiently accurate RSP map generation from CBCT images, allowing for evaluation of daily dose based on CBCT and possibly allowing for CBCT‐guided adaptive treatment planning for IMPT.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Issue
    URL: Article
    DOI: 10.1002/mp.v47.9
    DOI: 10.1002/mp.14347
    Language: English
    Publisher: Wiley
    Publication Date: 2020
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  • 7
    Online Resource
    Online Resource
    Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography
    Wiley ; 2019
    In:  Medical Physics Vol. 46, No. 9 ( 2019-09), p. 3998-4009
    Details
    In: Medical Physics, Wiley, Vol. 46, No. 9 ( 2019-09), p. 3998-4009
    Abstract: The incorporation of cone‐beam computed tomography (CBCT) has allowed for enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning‐based method for generating high quality corrected CBCT (CCBCT) images is proposed. Methods The proposed method integrates a residual block concept into a cycle‐consistent adversarial network (cycle‐GAN) framework, called res‐cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle‐GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end‐to‐end CBCT‐to‐CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC) indices, and spatial non‐uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning‐based CBCT correction method. Results Overall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning‐based method. Conclusions The authors have developed a novel deep learning‐based method to generate high‐quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Issue
    URL: Article
    DOI: 10.1002/mp.v46.9
    DOI: 10.1002/mp.13656
    Language: English
    Publisher: Wiley
    Publication Date: 2019
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  • 8
    Online Resource
    Online Resource
    Automated left ventricular myocardium segmentation using 3D deeply supervised attention U‐net for coronary computed tomography angiography; CT myocardium segmentation
    Wiley ; 2020
    In:  Medical Physics Vol. 47, No. 4 ( 2020-04), p. 1775-1785
    Details
    In: Medical Physics, Wiley, Vol. 47, No. 4 ( 2020-04), p. 1775-1785
    Abstract: Segmentation of left ventricular myocardium (LVM) in coronary computed tomography angiography (CCTA) is important for diagnosis of cardiovascular diseases. Due to poor image contrast and large variation in intensity and shapes, LVM segmentation for CCTA is a challenging task. The purpose of this work is to develop a region‐based deep learning method to automatically detect and segment the LVM solely based on CCTA images. Methods We developed a 3D deeply supervised U‐Net, which incorporates attention gates (AGs) to focus on the myocardial boundary structures, to segment LVM contours from CCTA. The deep attention U‐Net (DAU‐Net) was trained on the patients’ CCTA images, with a manual contour‐derived binary mask used as the learning‐based target. The network was supervised by a hybrid loss function, which combined logistic loss and Dice loss to simultaneously measure the similarities and discrepancies between the prediction and training datasets. To evaluate the accuracy of the segmentation, we retrospectively investigated 100 patients with suspected or confirmed coronary artery disease (CAD). The LVM volume was segmented by the proposed method and compared with physician‐approved clinical contours. Quantitative metrics used were Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), the center of mass distance (CMD), and volume difference (VOD). Results The proposed method created contours with very good agreement to the ground truth contours. Our proposed segmentation approach is benchmarked primarily using fivefold cross validation. Model prediction correlated and agreed well with manual contour. The mean DSC of the contours delineated by our method was 91.6% among all patients. The resultant HD was 6.840 ± 4.410 mm. The proposed method also resulted in a small CMD (1.058 ± 1.245 mm) and VOD (1.640 ± 1.777 cc). Among all patients, the MSD and RMSD were 0.433 ± 0.209 mm and 0.724 ± 0.375 mm, respectively, between ground truth and LVM volume resulting from the proposed method. Conclusions We developed a novel deep learning‐based approach for the automated segmentation of the LVM on CCTA images. We demonstrated the high accuracy of the proposed learning‐based segmentation method through comparison with ground truth contour of 100 clinical patient cases using six quantitative metrics. These results show the potential of using automated LVM segmentation for computer‐aided delineation of CADs in the clinical setting.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Issue
    URL: Article
    DOI: 10.1002/mp.v47.4
    DOI: 10.1002/mp.14066
    Language: English
    Publisher: Wiley
    Publication Date: 2020
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