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Galapon AV, Thummerer A, Langendijk JA, Wagenaar D, Both S. Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy. Med Phys 2024; 51:2499-2509. [PMID: 37956266 DOI: 10.1002/mp.16838] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. PURPOSE This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep-learning-based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. METHODS Two deep-learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT-MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU-, range-, WET-, and dose-error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90-degree angle. To evaluate the dose distribution, a mask based on the 5%-isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs RESULTS: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty-HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty-range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty-WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3 mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). CONCLUSION The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD-based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning-based sCTs.
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Affiliation(s)
- Arthur Villanueva Galapon
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Germany
| | - Johannes Albertus Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Dirk Wagenaar
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Chen X, Zhao Y, Court LE, Wang H, Pan T, Phan J, Wang X, Ding Y, Yang J. SC-GAN: Structure-completion generative adversarial network for synthetic CT generation from MR images with truncated anatomy. Comput Med Imaging Graph 2024; 113:102353. [PMID: 38387114 DOI: 10.1016/j.compmedimag.2024.102353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/14/2023] [Accepted: 02/04/2024] [Indexed: 02/24/2024]
Abstract
Creating synthetic CT (sCT) from magnetic resonance (MR) images enables MR-based treatment planning in radiation therapy. However, the MR images used for MR-guided adaptive planning are often truncated in the boundary regions due to the limited field of view and the need for sequence optimization. Consequently, the sCT generated from these truncated MR images lacks complete anatomic information, leading to dose calculation error for MR-based adaptive planning. We propose a novel structure-completion generative adversarial network (SC-GAN) to generate sCT with full anatomic details from the truncated MR images. To enable anatomy compensation, we expand input channels of the CT generator by including a body mask and introduce a truncation loss between sCT and real CT. The body mask for each patient was automatically created from the simulation CT scans and transformed to daily MR images by rigid registration as another input for our SC-GAN in addition to the MR images. The truncation loss was constructed by implementing either an auto-segmentor or an edge detector to penalize the difference in body outlines between sCT and real CT. The experimental results show that our SC-GAN achieved much improved accuracy of sCT generation in both truncated and untruncated regions compared to the original cycleGAN and conditional GAN methods.
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Affiliation(s)
- Xinru Chen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
| | - Yao Zhao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - He Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tinsu Pan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Jack Phan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xin Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Ding
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
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Sherwani MK, Gopalakrishnan S. A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy. FRONTIERS IN RADIOLOGY 2024; 4:1385742. [PMID: 38601888 PMCID: PMC11004271 DOI: 10.3389/fradi.2024.1385742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024]
Abstract
The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.
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Affiliation(s)
- Moiz Khan Sherwani
- Section for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
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Hajim WI, Zainudin S, Mohd Daud K, Alheeti K. Optimized models and deep learning methods for drug response prediction in cancer treatments: a review. PeerJ Comput Sci 2024; 10:e1903. [PMID: 38660174 PMCID: PMC11042005 DOI: 10.7717/peerj-cs.1903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/31/2024] [Indexed: 04/26/2024]
Abstract
Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.
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Affiliation(s)
- Wesam Ibrahim Hajim
- Department of Applied Geology, College of Sciences, Tirkit University, Tikrit, Salah ad Din, Iraq
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Suhaila Zainudin
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Khattab Alheeti
- Department of Computer Networking Systems, College of Computer Sciences and Information Technology, University of Anbar, Al Anbar, Ramadi, Iraq
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Gong C, Huang Y, Luo M, Cao S, Gong X, Ding S, Yuan X, Zheng W, Zhang Y. Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images. Radiat Oncol 2024; 19:37. [PMID: 38486193 PMCID: PMC10938692 DOI: 10.1186/s13014-024-02429-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) plays an increasingly important role in radiotherapy, enhancing the accuracy of target and organs at risk delineation, but the absence of electron density information limits its further clinical application. Therefore, the aim of this study is to develop and evaluate a novel unsupervised network (cycleSimulationGAN) for unpaired MR-to-CT synthesis. METHODS The proposed cycleSimulationGAN in this work integrates contour consistency loss function and channel-wise attention mechanism to synthesize high-quality CT-like images. Specially, the proposed cycleSimulationGAN constrains the structural similarity between the synthetic and input images for better structural retention characteristics. Additionally, we propose to equip a novel channel-wise attention mechanism based on the traditional generator of GAN to enhance the feature representation capability of deep network and extract more effective features. The mean absolute error (MAE) of Hounsfield Units (HU), peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE) and structural similarity index (SSIM) were calculated between synthetic CT (sCT) and ground truth (GT) CT images to quantify the overall sCT performance. RESULTS One hundred and sixty nasopharyngeal carcinoma (NPC) patients who underwent volumetric-modulated arc radiotherapy (VMAT) were enrolled in this study. The generated sCT of our method were more consistent with the GT compared with other methods in terms of visual inspection. The average MAE, RMSE, PSNR, and SSIM calculated over twenty patients were 61.88 ± 1.42, 116.85 ± 3.42, 36.23 ± 0.52 and 0.985 ± 0.002 for the proposed method. The four image quality assessment metrics were significantly improved by our approach compared to conventional cycleGAN, the proposed cycleSimulationGAN produces significantly better synthetic results except for SSIM in bone. CONCLUSIONS We developed a novel cycleSimulationGAN model that can effectively create sCT images, making them comparable to GT images, which could potentially benefit the MRI-based treatment planning.
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Affiliation(s)
- Changfei Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Yuling Huang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Mingming Luo
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Shunxiang Cao
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Xiaochang Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
- Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Jiangxi, PR China
| | - Shenggou Ding
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Xingxing Yuan
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
| | - Wenheng Zheng
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China
| | - Yun Zhang
- Department of Radiation Oncology, Jiangxi Cancer Hospital, 330029, Nanchang, Jiangxi, PR China.
- The Second Affiliated Hospital of Nanchang Medical College, 330029, Nanchang, Jiangxi, PR China.
- Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Jiangxi, PR China.
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Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z. Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal 2024; 92:103046. [PMID: 38052145 DOI: 10.1016/j.media.2023.103046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 11/14/2023] [Accepted: 11/29/2023] [Indexed: 12/07/2023]
Abstract
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.
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Affiliation(s)
- Sanuwani Dayarathna
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
| | | | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, Faculty of Medicine, Monash University, Clayton VIC 3800, Australia
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, W12 7SL, United Kingdom
| | - Munawar Hayat
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Zhaolin Chen
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia; Monash Biomedical Imaging, Clayton VIC 3800, Australia
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Li X, Bellotti R, Meier G, Bachtiary B, Weber D, Lomax A, Buhmann J, Zhang Y. Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour. Radiother Oncol 2024; 191:110056. [PMID: 38104781 DOI: 10.1016/j.radonc.2023.110056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND AND PURPOSE Deep learning techniques excel in MR-based CT synthesis, but missing uncertainty prediction limits its clinical use in proton therapy. We developed an uncertainty-aware framework and evaluated its efficiency in robust proton planning. MATERIALS AND METHODS A conditional generative-adversarial network was trained on 64 brain tumour patients with paired MR-CT images to generate synthetic CTs (sCT) from combined T1-T2 MRs of three orthogonal planes. A Bayesian neural network predicts Laplacian distributions for all voxels with parameters (μ, b). A robust proton plan was optimized using three sCTs of μ and μ±b. The dosimetric differences between the plan from sCT (sPlan) and the recalculated plan (rPlan) on planning CT (pCT) were quantified for each patient. The uncertainty-aware robust plan was compared to conventional robust (global ± 3 %) and non-robust plans. RESULTS In 8-fold cross-validation, sCT-pCT image differences (Mean-Absolute-Error) were 80.84 ± 9.84HU (body), 35.78 ± 6.07HU (soft tissues) and 221.88 ± 31.69HU (bones), with Dice scores of 90.33 ± 2.43 %, 95.13 ± 0.80 %, and 85.53 ± 4.16 %, respectively. The uncertainty distribution positively correlated with absolute prediction error (Correlation Coefficient: 0.62 ± 0.01). The uncertainty-conditioned robust optimisation improved the rPlan-sPlan agreement, e.g., D95 absolute difference (CTV) was 1.10 ± 1.24 % compared to conventional (1.64 ± 2.71 %) and non-robust (2.08 ± 2.96 %) optimisation. This trend was consistent across all target and organs-at-risk indexes. CONCLUSION The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.
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Affiliation(s)
- Xia Li
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Computer Science, ETH Zurich, Switzerland
| | - Renato Bellotti
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Gabriel Meier
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland
| | | | - Damien Weber
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Radiation Oncology, University Hospital of Zurich, Switzerland; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Antony Lomax
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | | | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland.
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Law MWK, Tse MY, Ho LCC, Lau KK, Wong OL, Yuan J, Cheung KY, Yu SK. A study of Bayesian deep network uncertainty and its application to synthetic CT generation for MR-only radiotherapy treatment planning. Med Phys 2024; 51:1244-1262. [PMID: 37665783 DOI: 10.1002/mp.16666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 06/05/2023] [Accepted: 07/20/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND The use of synthetic computed tomography (CT) for radiotherapy treatment planning has received considerable attention because of the absence of ionizing radiation and close spatial correspondence to source magnetic resonance (MR) images, which have excellent tissue contrast. However, in an MR-only environment, little effort has been made to examine the quality of synthetic CT images without using the original CT images. PURPOSE To estimate synthetic CT quality without referring to original CT images, this study established the relationship between synthetic CT uncertainty and Bayesian uncertainty, and proposed a new Bayesian deep network for generating synthetic CT images and estimating synthetic CT uncertainty for MR-only radiotherapy treatment planning. METHODS AND MATERIALS A novel deep Bayesian network was formulated using probabilistic network weights. Two mathematical expressions were proposed to quantify the Bayesian uncertainty of the network and synthetic CT uncertainty, which was closely related to the mean absolute error (MAE) in Hounsfield Unit (HU) of synthetic CT. These uncertainties were examined to demonstrate the accuracy of representing the synthetic CT uncertainty using a Bayesian counterpart. We developed a hybrid Bayesian architecture and a new data normalization scheme, enabling the Bayesian network to generate both accurate synthetic CT and reliable uncertainty information when probabilistic weights were applied. The proposed method was evaluated in 59 patients (13/12/32/2 for training/validation/testing/uncertainty visualization) diagnosed with prostate cancer, who underwent same-day pelvic CT- and MR-acquisitions. To assess the relationship between Bayesian and synthetic CT uncertainties, linear and non-linear correlation coefficients were calculated on per-voxel, per-tissue, and per-patient bases. For accessing the accuracy of the CT number and dosimetric accuracy, the proposed method was compared with a commercially available atlas-based method (MRCAT) and a U-Net conditional-generative adversarial network (UcGAN). RESULTS The proposed model exhibited 44.33 MAE, outperforming UcGAN 52.51 and MRCAT 54.87. The gamma rate (2%/2 mm dose difference/distance to agreement) of the proposed model was 98.68%, comparable to that of UcGAN (98.60%) and MRCAT (98.56%). The per-patient and per-tissue linear correlation coefficients between the Bayesian and synthetic CT uncertainties ranged from 0.53 to 0.83, implying a moderate to strong linear correlation. Per-voxel correlation coefficients varied from -0.13 to 0.67 depending on the regions-of-interest evaluated, indicating tissue-dependent correlation. The R2 value for estimating MAE solely using Bayesian uncertainty was 0.98, suggesting that the uncertainty of the proposed model was an ideal candidate for predicting synthetic CT error, without referring to the original CT. CONCLUSION This study established a relationship between the Bayesian model uncertainty and synthetic CT uncertainty. A novel Bayesian deep network was proposed to generate a synthetic CT and estimate its uncertainty. Various metrics were used to thoroughly examine the relationship between the uncertainties of the proposed Bayesian model and the generated synthetic CT. Compared with existing approaches, the proposed model showed comparable CT number and dosimetric accuracies. The experiments showed that the proposed Bayesian model was capable of producing accurate synthetic CT, and was an effective indicator of the uncertainty and error associated with synthetic CT in MR-only workflows.
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Affiliation(s)
- Max Wai-Kong Law
- Medical Physics Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
| | - Mei-Yan Tse
- Medical Physics Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
| | - Leon Chin-Chak Ho
- Medical Physics Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
| | - Ka-Ki Lau
- Medical Physics Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
| | - Oi Lei Wong
- Research Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium and Hospital, Hong Kong SAR, China
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Zhou L, Ni X, Kong Y, Zeng H, Xu M, Zhou J, Wang Q, Liu C. Mitigating misalignment in MRI-to-CT synthesis for improved synthetic CT generation: an iterative refinement and knowledge distillation approach. Phys Med Biol 2023; 68:245020. [PMID: 37976548 DOI: 10.1088/1361-6560/ad0ddc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/17/2023] [Indexed: 11/19/2023]
Abstract
Objective.Deep learning has shown promise in generating synthetic CT (sCT) from magnetic resonance imaging (MRI). However, the misalignment between MRIs and CTs has not been adequately addressed, leading to reduced prediction accuracy and potential harm to patients due to the generative adversarial network (GAN)hallucination phenomenon. This work proposes a novel approach to mitigate misalignment and improve sCT generation.Approach.Our approach has two stages: iterative refinement and knowledge distillation. First, we iteratively refine registration and synthesis by leveraging their complementary nature. In each iteration, we register CT to the sCT from the previous iteration, generating a more aligned deformed CT (dCT). We train a new model on the refined 〈dCT, MRI〉 pairs to enhance synthesis. Second, we distill knowledge by creating a target CT (tCT) that combines sCT and dCT images from the previous iterations. This further improves alignment beyond the individual sCT and dCT images. We train a new model with the 〈tCT, MRI〉 pairs to transfer insights from multiple models into this final knowledgeable model.Main results.Our method outperformed conditional GANs on 48 head and neck cancer patients. It reduced hallucinations and improved accuracy in geometry (3% ↑ Dice), intensity (16.7% ↓ MAE), and dosimetry (1% ↑γ3%3mm). It also achieved <1% relative dose difference for specific dose volume histogram points.Significance.This pioneering approach for addressing misalignment shows promising performance in MRI-to-CT synthesis for MRI-only planning. It could be applied to other modalities like cone beam computed tomography and tasks such as organ contouring.
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Affiliation(s)
- Leyuan Zhou
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, People's Republic of China
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, People's Republic of China
| | - Xinye Ni
- Radiation Oncology Center, Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, People's Republic of China
- Center of Medical Physics, Nanjing Medical University, Changzhou, People's Republic of China
| | - Yan Kong
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, People's Republic of China
| | - Haibin Zeng
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, People's Republic of China
| | - Muchen Xu
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, People's Republic of China
| | - Juying Zhou
- Department of Radiation Oncology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, People's Republic of China
| | - Qingxin Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China
| | - Cong Liu
- Radiation Oncology Center, Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, People's Republic of China
- Center of Medical Physics, Nanjing Medical University, Changzhou, People's Republic of China
- Faculty of Business Information, Shanghai Business School, Shanghai, People's Republic of China
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Liang X, Yen A, Bai T, Godley A, Shen C, Wu J, Meng B, Lin MH, Medin P, Yan Y, Owrangi A, Desai N, Hannan R, Garant A, Jiang S, Deng J. Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR-only radiotherapy. Med Phys 2023; 50:7368-7382. [PMID: 37358195 DOI: 10.1002/mp.16556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 05/29/2023] [Indexed: 06/27/2023] Open
Abstract
BACKGROUND MRI-only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR-to-sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone. PURPOSE The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP. METHODS To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in-phase (IP) MR images. Dixon images provide better bone contrast than T2-weighted images as inputs to a modified multi-channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11). RESULTS We compared model performance with and without bony structure constraints using single- and multi-channel inputs. Among all the models, multi-channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT. CONCLUSION Modified multi-channel CycleGAN with bony structure constraints, taking Dixon-constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy.
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Affiliation(s)
- Xiao Liang
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Allen Yen
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ti Bai
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Andrew Godley
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Chenyang Shen
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Junjie Wu
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Boyu Meng
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Paul Medin
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yulong Yan
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Amir Owrangi
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Neil Desai
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Raquibul Hannan
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Aurelie Garant
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jie Deng
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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11
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de Koster RJC, Thummerer A, Scandurra D, Langendijk JA, Both S. Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy. Med Phys 2023; 50:8023-8033. [PMID: 37831597 DOI: 10.1002/mp.16782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations. PURPOSE To compare daily CBCT-based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs in a scenario mimicking actual clinical practice. METHODS Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D98 difference for target volumes and Dmean difference for organs-at-risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia. RESULTS For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs. CONCLUSIONS Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.
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Affiliation(s)
- Rutger J C de Koster
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Daniel Scandurra
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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12
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Liu C, Liu Z, Holmes J, Zhang L, Zhang L, Ding Y, Shu P, Wu Z, Dai H, Li Y, Shen D, Liu N, Li Q, Li X, Zhu D, Liu T, Liu W. Artificial general intelligence for radiation oncology. META-RADIOLOGY 2023; 1:100045. [PMID: 38344271 PMCID: PMC10857824 DOI: 10.1016/j.metrad.2023.100045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.
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Affiliation(s)
- Chenbin Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | | | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | - Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Yuzhen Ding
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Peng Shu
- School of Computing, University of Georgia, USA
| | - Zihao Wu
- School of Computing, University of Georgia, USA
| | - Haixing Dai
- School of Computing, University of Georgia, USA
| | - Yiwei Li
- School of Computing, University of Georgia, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, China
- Shanghai United Imaging Intelligence Co., Ltd, China
- Shanghai Clinical Research and Trial Center, China
| | - Ninghao Liu
- School of Computing, University of Georgia, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | | | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, USA
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13
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Tian L, Lühr A. Proton range uncertainty caused by synthetic computed tomography generated with deep learning from pelvic magnetic resonance imaging. Acta Oncol 2023; 62:1461-1469. [PMID: 37703314 DOI: 10.1080/0284186x.2023.2256967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/04/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND In proton therapy, it is disputed whether synthetic computed tomography (sCT), derived from magnetic resonance imaging (MRI), permits accurate dose calculations. On the one hand, an MRI-only workflow could eliminate errors caused by, e.g., MRI-CT registration. On the other hand, the extra error would be induced due to an sCT generation model. This work investigated the systematic and random model error induced by sCT generation of a widely discussed deep learning model, pix2pix. MATERIAL AND METHODS An open-source image dataset of 19 patients with cancer in the pelvis was employed and split into 10, 5, and 4 for training, testing, and validation of the model, respectively. Proton pencil beams (200 MeV) were simulated on the real CT and generated sCT using the tool for particle simulation (TOPAS). Monte Carlo (MC) dropout was used for error estimation (50 random sCT samples). Systematic and random model errors were investigated for sCT generation and dose calculation on sCT. RESULTS For sCT generation, random model error near the edge of the body (∼200 HU) was higher than that within the body (∼100 HU near the bone edge and <10 HU in soft tissue). The mean absolute error (MAE) was 49 ± 5, 191 ± 23, and 503 ± 70 HU for the whole body, bone, and air in the patient, respectively. Random model errors of the proton range were small (<0.2 mm) for all spots and evenly distributed throughout the proton fields. Systematic errors of the proton range were -1.0(±2.2) mm and 0.4(±0.9)%, respectively, and were unevenly distributed within the proton fields. For 4.5% of the spots, large errors (>5 mm) were found, which may relate to MRI-CT mismatch due to, e.g., registration, MRI distortion anatomical changes, etc. CONCLUSION The sCT model was shown to be robust, i.e., had a low random model error. However, further investigation to reduce and even predict and manage systematic error is still needed for future MRI-only proton therapy.
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Affiliation(s)
- Liheng Tian
- Department of Physics, TU Dortmund University, Dortmund, Germany
| | - Armin Lühr
- Department of Physics, TU Dortmund University, Dortmund, Germany
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14
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Tsai P, Tseng YL, Shen B, Ackerman C, Zhai HA, Yu F, Simone CB, Choi JI, Lee NY, Kabarriti R, Lazarev S, Johnson CL, Liu J, Chen CC, Lin H. The Applications and Pitfalls of Cone-Beam Computed Tomography-Based Synthetic Computed Tomography for Adaptive Evaluation in Pencil-Beam Scanning Proton Therapy. Cancers (Basel) 2023; 15:5101. [PMID: 37894469 PMCID: PMC10605451 DOI: 10.3390/cancers15205101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
PURPOSE The study evaluates the efficacy of cone-beam computed tomography (CBCT)-based synthetic CTs (sCT) as a potential alternative to verification CT (vCT) for enhanced treatment monitoring and early adaptation in proton therapy. METHODS Seven common treatment sites were studied. Two sets of sCT per case were generated: direct-deformed (DD) sCT and image-correction (IC) sCT. The image qualities and dosimetric impact of the sCT were compared to the same-day vCT. RESULTS The sCT agreed with vCT in regions of homogeneous tissues such as the brain and breast; however, notable discrepancies were observed in the thorax and abdomen. The sCT outliers existed for DD sCT when there was an anatomy change and for IC sCT in low-density regions. The target coverage exhibited less than a 5% variance in most DD and IC sCT cases when compared to vCT. The Dmax of serial organ-at-risk (OAR) in sCT plans shows greater deviation from vCT than small-volume dose metrics (D0.1cc). The parallel OAR volumetric and mean doses remained consistent, with average deviations below 1.5%. CONCLUSION The use of sCT enables precise treatment and prompt early adaptation for proton therapy. The quality assurance of sCT is mandatory in the early stage of clinical implementation.
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Affiliation(s)
- Pingfang Tsai
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Yu-Lun Tseng
- Proton Center, Taipei Medical University, Taipei 11031, Taiwan;
- Department of Radiation Oncology, Taipei Medical University, Taipei 11031, Taiwan
| | - Brian Shen
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | | | - Huifang A. Zhai
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Francis Yu
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Charles B. Simone
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - J. Isabelle Choi
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Rafi Kabarriti
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10467, USA;
| | - Stanislav Lazarev
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Casey L. Johnson
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Jiayi Liu
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Chin-Cheng Chen
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Haibo Lin
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
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15
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Masitho S, Grigo J, Brandt T, Lambrecht U, Szkitsak J, Weiss A, Fietkau R, Putz F, Bert C. Synthetic CTs for MRI-only brain RT treatment: integration of immobilization systems. Strahlenther Onkol 2023; 199:739-748. [PMID: 37285037 PMCID: PMC10361877 DOI: 10.1007/s00066-023-02090-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/25/2023] [Indexed: 06/08/2023]
Abstract
PURPOSE Auxiliary devices such as immobilization systems should be considered in synthetic CT (sCT)-based treatment planning (TP) for MRI-only brain radiotherapy (RT). A method for auxiliary device definition in the sCT is introduced, and its dosimetric impact on the sCT-based TP is addressed. METHODS T1-VIBE DIXON was acquired in an RT setup. Ten datasets were retrospectively used for sCT generation. Silicone markers were used to determine the auxiliary devices' relative position. An auxiliary structure template (AST) was created in the TP system and placed manually on the MRI. Various RT mask characteristics were simulated in the sCT and investigated by recalculating the CT-based clinical plan on the sCT. The influence of auxiliary devices was investigated by creating static fields aimed at artificial planning target volumes (PTVs) in the CT and recalculated in the sCT. The dose covering 50% of the PTV (D50) deviation percentage between CT-based/recalculated plan (∆D50[%]) was evaluated. RESULTS Defining an optimal RT mask yielded a ∆D50[%] of 0.2 ± 1.03% for the PTV and between -1.6 ± 3.4% and 1.1 ± 2.0% for OARs. Evaluating each static field, the largest ∆D50[%] was delivered by AST positioning inaccuracy (max: 3.5 ± 2.4%), followed by the RT table (max: 3.6 ± 1.2%) and the RT mask (max: 3.0 ± 0.8% [anterior], 1.6 ± 0.4% [rest]). No correlation between ∆D50[%] and beam depth was found for the sum of opposing beams, except for (45° + 315°). CONCLUSION This study evaluated the integration of auxiliary devices and their dosimetric influence on sCT-based TP. The AST can be easily integrated into the sCT-based TP. Further, we found that the dosimetric impact was within an acceptable range for an MRI-only workflow.
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Affiliation(s)
- Siti Masitho
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany.
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
| | - Johanna Grigo
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Tobias Brandt
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Ulrike Lambrecht
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Juliane Szkitsak
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Alexander Weiss
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Strahlenklinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
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Olberg S, Choi BS, Park I, Liang X, Kim JS, Deng J, Yan Y, Jiang S, Park JC. Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction. Med Phys 2023; 50:1436-1449. [PMID: 36336718 DOI: 10.1002/mp.16087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/22/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The growing adoption of magnetic resonance imaging (MRI)-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting. PURPOSE We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting. METHODS Comparisons are made between the following models: (1) the paired-data fully convolutional DenseNet (FCDN), (2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, (3) the unpaired-data CycleGAN, (4) the CycleGAN with the IDOL training strategy, and (5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross-validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random. RESULTS In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal-to-noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20%; bone MAE: 16%; PSNR: 10%; SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant. CONCLUSIONS We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step toward fully enabling these powerful and attractive unpaired-data frameworks.
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Affiliation(s)
- Sven Olberg
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Byong Su Choi
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Inkyung Park
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Xiao Liang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jin Sung Kim
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
- Oncosoft Inc., Seoul, South Korea
| | - Jie Deng
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yulong Yan
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Justin C Park
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Medical Physics and Biomedical Engineering Lab (MPBEL), Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA
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Parrella G, Vai A, Nakas A, Garau N, Meschini G, Camagni F, Molinelli S, Barcellini A, Pella A, Ciocca M, Vitolo V, Orlandi E, Paganelli C, Baroni G. Synthetic CT in Carbon Ion Radiotherapy of the Abdominal Site. Bioengineering (Basel) 2023; 10:bioengineering10020250. [PMID: 36829745 PMCID: PMC9951997 DOI: 10.3390/bioengineering10020250] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scenario. The sCTs were evaluated by means of similarity metrics (e.g., mean absolute error, MAE) and geometrical criteria (e.g., dice coefficient). Recalculated CIRT plans were evaluated through dose volume histogram, gamma analysis and range shift analysis. The CT-based test set presented optimal MAE on bones (86.03 ± 10.76 HU), soft tissues (55.39 ± 3.41 HU) and air (54.42 ± 11.48 HU). Higher values were obtained from the MRI-only test set (MAEBONE = 154.87 ± 22.90 HU). The global gamma pass rate reached 94.88 ± 4.9% with 3%/3 mm, while the range shift reached a median (IQR) of 0.98 (3.64) mm. The three-channel cGAN can generate acceptable abdominal sCTs and allow for CIRT dose recalculations comparable to the clinical plans.
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Affiliation(s)
- Giovanni Parrella
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: ; Tel.: +39-02-2399-18-9022
| | - Alessandro Vai
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Anestis Nakas
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Noemi Garau
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Francesca Camagni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Silvia Molinelli
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Amelia Barcellini
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
- Department of Internal Medicine and Medical Therapy, University of Pavia, 27100 Pavia, Italy
| | - Andrea Pella
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Mario Ciocca
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Viviana Vitolo
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Ester Orlandi
- Clinical Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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18
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Gao L, Xie K, Sun J, Lin T, Sui J, Yang G, Ni X. Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy. Med Phys 2023; 50:879-893. [PMID: 36183234 DOI: 10.1002/mp.16017] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/02/2022] [Accepted: 09/25/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Cone-beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT-based ART. PURPOSE To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate high-quality sCT images from CBCT and achieve an accurate dose calculation. METHODS The proposed SARN was trained in a self-supervised manner. Artifact-CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact-CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U-Net, cycleGAN, attention-gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy. RESULTS The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIUm ) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (p < 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U-Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm/2%) of 98.2% and 96.9%, respectively. CONCLUSIONS The proposed SARN can reduce serious streaking artifacts in CBCT images. The SARN combined with cycleGAN can generate high-quality sCT images with fewer artifacts, high-accuracy HU values, and accurate anatomical structures, thus providing reliable dose calculation in ART.
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Affiliation(s)
- Liugang Gao
- School of Computer Science and Engineering, Southeast University, Nanjing, China
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Kai Xie
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Jiawei Sun
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Tao Lin
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Jianfeng Sui
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Guanyu Yang
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Xinye Ni
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
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Lapaeva M, La Greca Saint-Esteven A, Wallimann P, Günther M, Konukoglu E, Andratschke N, Guckenberger M, Tanadini-Lang S, Dal Bello R. Synthetic computed tomographies for low-field magnetic resonance-guided radiotherapy in the abdomen. Phys Imaging Radiat Oncol 2022; 24:173-179. [DOI: 10.1016/j.phro.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/13/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
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20
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Lenkowicz J, Votta C, Nardini M, Quaranta F, Catucci F, Boldrini L, Vagni M, Menna S, Placidi L, Romano A, Chiloiro G, Gambacorta MA, Mattiucci GC, Indovina L, Valentini V, Cusumano D. A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases. Radiother Oncol 2022; 176:31-38. [PMID: 36063982 DOI: 10.1016/j.radonc.2022.08.028] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION This study aims to apply a conditional Generative Adversarial Network (cGAN) to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance (MR) images of the thorax. METHODS Sixty patients treated for lung lesions were enrolled and divided into training (32), validation (8), internal (10,TA) and external (10,TB) test set. Image accuracy of generated sCT was evaluated computing the mean absolute (MAE) and mean error (ME) with respect the original CT. Three treatment plans were calculated for each patient considering MRI as reference image: original CT, sCT (pure sCT) and sCT with GTV density override (hybrid sCT) were used as Electron Density (ED) map. Dose accuracy was evaluated comparing treatment plans in terms of gamma analysis and Dose Volume Histogram (DVH) parameters. RESULTS No significant difference was observed between the test sets for image and dose accuracy parameters. Considering the whole test cohort, a MAE of 54.9 ± 10.5 HU and a ME of 4.4 ± 7.4 HU was obtained. Mean gamma passing rates for 2%/2mm, and 3%/3mm tolerance criteria were 95.5 ± 5.9% and 98.2 ± 4.1% for pure sCT, 96.1 ± 5.1% and 98.5 ± 3.9% for hybrid sCT: the difference between the two approaches was significant (p = 0.01). As regards DVH analysis, differences in target parameters estimation were found to be within 5% using hybrid approach and 20% using pure sCT. CONCLUSION The DL algorithm here presented can generate sCT images in the thorax with good image and dose accuracy, especially when the hybrid approach is used. The algorithm does not suffer from inter-scanner variability, making feasible the implementation of MR-only workflows for palliative treatments.
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Affiliation(s)
- Jacopo Lenkowicz
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Claudio Votta
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy; Mater Olbia Hospital, Olbia (SS), Italy.
| | - Matteo Nardini
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | | | | | - Luca Boldrini
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Marica Vagni
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | | | - Gian Carlo Mattiucci
- Mater Olbia Hospital, Olbia (SS), Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy; Mater Olbia Hospital, Olbia (SS), Italy
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21
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Thummerer A, Seller Oria C, Zaffino P, Visser S, Meijers A, Guterres Marmitt G, Wijsman R, Seco J, Langendijk JA, Knopf AC, Spadea MF, Both S. Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy. Med Phys 2022; 49:6824-6839. [PMID: 35982630 PMCID: PMC10087352 DOI: 10.1002/mp.15930] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. PURPOSE In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. METHODS A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals. RESULTS 4D-sCTs resulted in average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ± 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% ± 3.2% (single phase) and 94.4% ± 2.1% (average). The clinical target volume showed high agreement in D98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues. CONCLUSION In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.
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Affiliation(s)
- Adrian Thummerer
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Carmen Seller Oria
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Sabine Visser
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Arturs Meijers
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Gabriel Guterres Marmitt
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Robin Wijsman
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Joao Seco
- Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Johannes Albertus Langendijk
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antje Christin Knopf
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Stefan Both
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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22
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Chourak H, Barateau A, Tahri S, Cadin C, Lafond C, Nunes JC, Boue-Rafle A, Perazzi M, Greer PB, Dowling J, de Crevoisier R, Acosta O. Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods. Front Oncol 2022; 12:968689. [PMID: 36300084 PMCID: PMC9589295 DOI: 10.3389/fonc.2022.968689] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.
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Affiliation(s)
- Hilda Chourak
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
- The Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health and Biosecurity, Brisbane, QLD, Australia
- *Correspondence: Hilda Chourak, ; Jason Dowling,
| | - Anaïs Barateau
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Safaa Tahri
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Capucine Cadin
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Caroline Lafond
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Jean-Claude Nunes
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Adrien Boue-Rafle
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Mathias Perazzi
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Peter B. Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia
- Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, NSW, Australia
| | - Jason Dowling
- The Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health and Biosecurity, Brisbane, QLD, Australia
- *Correspondence: Hilda Chourak, ; Jason Dowling,
| | - Renaud de Crevoisier
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Oscar Acosta
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
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23
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Miscouridou M, Pineda-Pardo JA, Stagg CJ, Treeby BE, Stanziola A. Classical and Learned MR to Pseudo-CT Mappings for Accurate Transcranial Ultrasound Simulation. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:2896-2905. [PMID: 35984788 DOI: 10.1109/tuffc.2022.3198522] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Model-based treatment planning for transcranial ultrasound therapy typically involves mapping the acoustic properties of the skull from an X-ray computed tomography (CT) image of the head. Here, three methods for generating pseudo-CT (pCT) images from magnetic resonance (MR) images were compared as an alternative to CT. A convolutional neural network (U-Net) was trained on paired MR-CT images to generate pCT T images from either T1-weighted or zero-echo time (ZTE) MR images (denoted tCT and zCT, respectively). A direct mapping from ZTE to pCT was also implemented (denoted cCT). When comparing the pCT and ground-truth CT images for the test set, the mean absolute error was 133, 83, and 145 Hounsfield units (HU) across the whole head, and 398, 222, and 336 HU within the skull for the tCT, zCT, and cCT images, respectively. Ultrasound simulations were also performed using the generated pCT images and compared to simulations based on CT. An annular array transducer was used targeting the visual or motor cortex. The mean differences in the simulated focal pressure, focal position, and focal volume were 9.9%, 1.5 mm, and 15.1% for simulations based on the tCT images; 5.7%, 0.6 mm, and 5.7% for the zCT; and 6.7%, 0.9 mm, and 12.1% for the cCT. The improved results for images mapped from ZTE highlight the advantage of using imaging sequences, which improves the contrast of the skull bone. Overall, these results demonstrate that acoustic simulations based on MR images can give comparable accuracy to those based on CT.
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24
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Wang J, Yan B, Wu X, Jiang X, Zuo Y, Yang Y. Development of an unsupervised cycle contrastive unpaired translation network for MRI-to-CT synthesis. J Appl Clin Med Phys 2022; 23:e13775. [PMID: 36168935 PMCID: PMC9680583 DOI: 10.1002/acm2.13775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/27/2022] [Accepted: 08/09/2022] [Indexed: 11/29/2022] Open
Abstract
Purpose The purpose of this work is to develop and evaluate a novel cycle‐contrastive unpaired translation network (cycleCUT) for synthetic computed tomography (sCT) generation from T1‐weighted magnetic resonance images (MRI). Methods The cycleCUT proposed in this work integrated the contrastive learning module from contrastive unpaired translation network (CUT) into the cycle‐consistent generative adversarial network (cycleGAN) framework to effectively achieve unsupervised CT synthesis from MRI. The diagnostic MRI and radiotherapy planning CT images of 24 brain cancer patients were obtained and reshuffled to train the network. For comparison, the traditional cycleGAN and CUT were also implemented. The sCT images were then imported into a treatment planning system to verify their feasibility for radiotherapy planning. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and structural similarity index (SSIM) between the sCT and the corresponding real CT images were calculated. Gamma analysis between sCT‐ and CT‐based dose distributions was also conducted. Results Quantitative evaluation of an independent test set of six patients showed that the average MAE was 69.62 ± 5.68 Hounsfield Units (HU) for the proposed cycleCUT, significantly (p‐value < 0.05) lower than that for cycleGAN (77.02 ± 6.00 HU) and CUT (78.05 ± 8.29). The average PSNR was 28.73 ± 0.46 decibels (dB) for cycleCUT, significantly larger than that for cycleGAN (27.96 ± 0.49 dB) and CUT (27.95 ± 0.69 dB). The average SSIM for cycleCUT (0.918 ± 0.012) was also significantly higher than that for cycleGAN (0.906 ± 0.012) and CUT (0.903 ± 0.015). Regarding gamma analysis, cycleCUT achieved the highest passing rate (97.95 ± 1.24% at the 2%/2 mm criteria and 10% dose threshold) but was not significantly different from the others. Conclusion The proposed cycleCUT could be effectively trained using unaligned image data, and could generate better sCT images than cycleGAN and CUT in terms of HU number accuracy and fine structural details.
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Affiliation(s)
- Jiangtao Wang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.,Cancer Center, Sichuan Academy of Medical Sciences · Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Bing Yan
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Xinhong Wu
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Xiao Jiang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Yang Zuo
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.,Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yidong Yang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.,Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
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25
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Emergence of MR-Linac in Radiation Oncology: Successes and Challenges of Riding on the MRgRT Bandwagon. J Clin Med 2022; 11:jcm11175136. [PMID: 36079065 PMCID: PMC9456673 DOI: 10.3390/jcm11175136] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 12/05/2022] Open
Abstract
The special issue of JCM on “Advances of MRI in Radiation Oncology” provides a unique forum for scientific literature related to MR imaging in radiation oncology. This issue covered many aspects, such as MR technology, motion management, economics, soft-tissue–air interface issues, and disease sites such as the pancreas, spine, sarcoma, prostate, head and neck, and rectum from both camps—the Unity and MRIdian systems. This paper provides additional information on the success and challenges of the two systems. A challenging aspect of this technology is low throughput and the monumental task of education and training that hinders its use for the majority of therapy centers. Additionally, the cost of this technology is too high for most institutions, and hence widespread use is still limited. This article highlights some of the difficulties and how to resolve them.
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Shokraei Fard A, Reutens DC, Vegh V. From CNNs to GANs for cross-modality medical image estimation. Comput Biol Med 2022; 146:105556. [DOI: 10.1016/j.compbiomed.2022.105556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/03/2022] [Accepted: 04/22/2022] [Indexed: 11/03/2022]
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Bäumer C, Frakulli R, Kohl J, Nagaraja S, Steinmeier T, Worawongsakul R, Timmermann B. Adaptive Proton Therapy of Pediatric Head and Neck Cases Using MRI-Based Synthetic CTs: Initial Experience of the Prospective KiAPT Study. Cancers (Basel) 2022; 14:cancers14112616. [PMID: 35681594 PMCID: PMC9179385 DOI: 10.3390/cancers14112616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 12/07/2022] Open
Abstract
BACKGROUND AND PURPOSE Interfractional anatomical changes might affect the outcome of proton therapy (PT). We aimed to prospectively evaluate the role of Magnetic Resonance Imaging (MRI) based adaptive PT for children with tumors of the head and neck and base of skull. METHODS MRI verification images were acquired at half of the treatment course. A synthetic computed tomography (CT) image was created using this MRI and a deformable image registration (DIR) to the reference MRI. The methodology was verified with in-silico phantoms and validated using a clinical case with a shrinking cystic hygroma on the basis of dosimetric quantities of contoured structures. The dose distributions on the verification X-ray CT and on the synthetic CT were compared with a gamma-index test using global 2 mm/2% criteria. RESULTS Regarding the clinical validation case, the gamma-index pass rate was 98.3%. Eleven patients were included in the clinical study. The most common diagnosis was rhabdomyosarcoma (73%). Craniofacial tumor site was predominant in 64% of patients, followed by base of skull (18%). For one individual case the synthetic CT showed an increase in the median D2 and Dmax dose on the spinal cord from 20.5 GyRBE to 24.8 GyRBE and 14.7 GyRBE to 25.1 GyRBE, respectively. Otherwise, doses received by OARs remained relatively stable. Similarly, the target volume coverage seen by D95% and V95% remained unchanged. CONCLUSIONS The method of transferring anatomical changes from MRIs to a synthetic CTs was successfully implemented and validated with simple, commonly available tools. In the frame of our early results on a small cohort, no clinical relevant deterioration for neither PTV coverage nor an increased dose burden to OARs occurred. However, the study will be continued to identify a pediatric patient cohort, which benefits from adaptive treatment planning.
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Affiliation(s)
- Christian Bäumer
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Department of Physics, Technische Universität Dortmund, 44227 Dortmund, Germany
- Correspondence:
| | - Rezarta Frakulli
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
| | - Jessica Kohl
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
| | - Sindhu Nagaraja
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
| | - Theresa Steinmeier
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
| | - Rasin Worawongsakul
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- Department of Particle Therapy, 45147 Essen, Germany
- Radiation Oncology Unit, Department of Diagnostic and Therapeutic Radiology, Ramathibodi Hospital, Mahidol University, Nakhon 73170, Thailand
| | - Beate Timmermann
- West German Proton Therapy Centre Essen, 45147 Essen, Germany; (R.F.); (J.K.); (S.N.); (T.S.); (R.W.); (B.T.)
- University Hospital Essen, 45147 Essen, Germany
- West German Cancer Center (WTZ), 45147 Essen, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Department of Particle Therapy, 45147 Essen, Germany
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Li H, Hrinivich WT, Chen H, Sheikh K, Ho MW, Ger R, Liu D, Hales RK, Voong KR, Halthore A, Deville C. Evaluating Proton Dose and Associated Range Uncertainty Using Daily Cone-Beam CT. Front Oncol 2022; 12:830981. [PMID: 35449577 PMCID: PMC9016186 DOI: 10.3389/fonc.2022.830981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/02/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to quantitatively evaluate the range uncertainties that arise from daily cone-beam CT (CBCT) images for proton dose calculation compared to CT using a measurement-based technique. Methods For head and thorax phantoms, wedge-shaped intensity-modulated proton therapy (IMPT) treatment plans were created such that the gradient of the wedge intersected and was measured with a 2D ion chamber array. The measured 2D dose distributions were compared with 2D dose planes extracted from the dose distributions using the IMPT plan calculated on CT and CBCT. Treatment plans of a thymoma cancer patient treated with breath-hold (BH) IMPT were recalculated on 28 CBCTs and 9 CTs, and the resulting dose distributions were compared. Results The range uncertainties for the head phantom were determined to be 1.2% with CBCT, compared to 0.5% for CT, whereas the range uncertainties for the thorax phantom were 2.1% with CBCT, compared to 0.8% for CT. The doses calculated on CBCT and CT were similar with similar anatomy changes. For the thymoma patient, the primary source of anatomy change was the BH uncertainty, which could be up to 8 mm in the superior-inferior (SI) direction. Conclusion We developed a measurement-based range uncertainty evaluation method with high sensitivity and used it to validate the accuracy of CBCT-based range and dose calculation. Our study demonstrated that the CBCT-based dose calculation could be used for daily dose validation in selected proton patients.
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Affiliation(s)
- Heng Li
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - William T Hrinivich
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hao Chen
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Khadija Sheikh
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Meng Wei Ho
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Rachel Ger
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Dezhi Liu
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Russell Kenneth Hales
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Khinh Ranh Voong
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aditya Halthore
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Curtiland Deville
- Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Jabbarpour A, Mahdavi SR, Vafaei Sadr A, Esmaili G, Shiri I, Zaidi H. Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy. Comput Biol Med 2022; 143:105277. [PMID: 35123139 DOI: 10.1016/j.compbiomed.2022.105277] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/09/2022] [Accepted: 01/27/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE Absorbed dose calculation in magnetic resonance-guided radiation therapy (MRgRT) is commonly based on pseudo CT (pCT) images. This study investigated the feasibility of unsupervised pCT generation from MRI using a cycle generative adversarial network (CycleGAN) and a heterogenous multicentric dataset. A dosimetric analysis in three-dimensional conformal radiotherapy (3DCRT) planning was also performed. MATERIAL AND METHODS Overall, 87 T1-weighted and 102 T2-weighted MR images alongside with their corresponding computed tomography (CT) images of brain cancer patients from multiple centers were used. Initially, images underwent a number of preprocessing steps, including rigid registration, novel CT Masker, N4 bias field correction, resampling, resizing, and rescaling. To overcome the gradient vanishing problem, residual blocks and mean squared error (MSE) loss function were utilized in the generator and in both networks (generator and discriminator), respectively. The CycleGAN was trained and validated using 70 T1 and 80 T2 randomly selected patients in an unsupervised manner. The remaining patients were used as a holdout test set to report final evaluation metrics. The generated pCTs were validated in the context of 3DCRT. RESULTS The CycleGAN model using masked T2 images achieved better performance with a mean absolute error (MAE) of 61.87 ± 22.58 HU, peak signal to noise ratio (PSNR) of 27.05 ± 2.25 (dB), and structural similarity index metric (SSIM) of 0.84 ± 0.05 on the test dataset. T1-weighted MR images used for dosimetric assessment revealed a gamma index of 3%, 3 mm, 2%, 2 mm and 1%, 1 mm with acceptance criteria of 98.96% ± 1.1%, 95% ± 3.68%, 90.1% ± 6.05%, respectively. The DVH differences between CTs and pCTs were within 2%. CONCLUSIONS A promising pCT generation model capable of handling heterogenous multicenteric datasets was proposed. All MR sequences performed competitively with no significant difference in pCT generation. The proposed CT Masker proved promising in improving the model accuracy and robustness. There was no significant difference between using T1-weighted and T2-weighted MR images for pCT generation.
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Affiliation(s)
- Amir Jabbarpour
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Department of Theoretical Physics and Center for Astroparticle Physics, Geneva University, Geneva, Switzerland
| | | | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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Wang C, Uh J, Patni T, Merchant T, Li Y, Hua CH, Acharya S. Toward MR-only proton therapy planning for pediatric brain tumors: synthesis of relative proton stopping power images with multiple sequence MRI and development of an online quality assurance tool. Med Phys 2022; 49:1559-1570. [PMID: 35075670 DOI: 10.1002/mp.15479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/23/2021] [Accepted: 01/11/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To generate synthetic relative proton-stopping-power (sRPSP) images from MRI sequence(s) and develop an online quality assurance (QA) tool for sRPSP to facilitate safe integration of MR-only proton planning into clinical practice. MATERIALS AND METHODS Planning CT and MR images of 195 pediatric brain tumor patients were utilized (training: 150, testing: 45). Seventeen consistent-cycle Generative Adversarial Network (ccGAN) models were trained separately using paired CT-converted RPSP and MRI datasets to transform a subject's MRI into sRPSP. T1-weighted (T1W), T2-weighted (T2W), and FLAIR MRI were permutated to form 17 combinations, with or without preprocessing, for determining the optimal training sequence(s). For evaluation, sRPSP images were converted to synthetic CT (sCT) and compared to the real CT in terms of mean absolute error (MAE) in HU. For QA, sCT was deformed and compared to a reference template built from training dataset to produce a flag map, highlighting pixels that deviate by >100 HU and fall outside the mean ± standard deviation reference intensity. The gamma intensity analysis (10%/3mm) of the deformed sCT against the QA template on the intensity difference was investigated as a surrogate of sCT accuracy. RESULTS The sRPSP images generated from a single T1W or T2W sequence outperformed that generated from multi-MRI sequences in terms of MAE (all P<0.05). Preprocessing with N4 bias and histogram matching reduced MAE of T2W MRI-based sCT (54±21 HU vs. 42±13 HU, P = .002). The gamma intensity analysis of sCT against the QA template was highly correlated with the MAE of sCT against the real CT in the testing cohort (r = -0.89 for T1W sCT; r = -0.93 for T2W sCT). CONCLUSION Accurate sRPSP images can be generated from T1W/T2W MRI for proton planning. A QA tool highlights regions of inaccuracy, flagging problematic cases unsuitable for clinical use. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Chuang Wang
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States Of America
| | - Jinsoo Uh
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States Of America
| | - Tushar Patni
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, United States Of America
| | - Thomas Merchant
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States Of America
| | - Yimei Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, United States Of America
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States Of America
| | - Sahaja Acharya
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States Of America.,Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD, United States Of America
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Zimmermann L, Knäusl B, Stock M, Lütgendorf-Caucig C, Georg D, Kuess P. An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy. Z Med Phys 2021; 32:218-227. [PMID: 34920940 PMCID: PMC9948837 DOI: 10.1016/j.zemedi.2021.10.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/11/2021] [Accepted: 10/19/2021] [Indexed: 12/11/2022]
Abstract
A magnetic resonance imaging (MRI) sequence independent deep learning technique was developed and validated to generate synthetic computed tomography (sCT) scans for MR guided proton therapy. 47 meningioma patients previously undergoing proton therapy based on pencil beam scanning were divided into training (33), validation (6), and test (8) cohorts. T1, T2, and contrast enhanced T1 (T1CM) MRI sequences were used in combination with the planning CT (pCT) data to train a 3D U-Net architecture with ResNet-Blocks. A hyperparameter search was performed including two loss functions, two group sizes of normalisation, and depth of the network. Training outcome was compared between models trained for each individual MRI sequence and for all sequences combined. The performance was evaluated based on a metric and dosimetric analysis as well as spot difference maps. Furthermore, the influence of immobilisation masks that are not visible on MRIs was investigated. Based on the hyperparameter search, the final model was trained with fixed features per group for the group normalisation, six down-convolution steps, an input size of 128×192×192, and feature loss. For the test dataset for body/bone the mean absolute error (MAE) values were on average 79.8/216.3Houndsfield unit (HU) when trained using T1 images, 71.1/186.1HU for T2, and 82.9/236.4HU for T1CM. The structural similarity metric (SSIM) ranged from 0.95 to 0.98 for all sequences. The investigated dose parameters of the target structures agreed within 1% between original proton treatment plans and plans recalculated on sCTs. The spot difference maps had peaks at ±0.2cm and for 98% of all spots the difference was less than 1cm. A novel MRI sequence independent sCT generator was developed, which suggests that the training phase of neural networks can be disengaged from specific MRI acquisition protocols. In contrast to previous studies, the patient cohort consisted exclusively of actual proton therapy patients (i.e. "real-world data").
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Affiliation(s)
- Lukas Zimmermann
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria,Faculty of Engineering, University of Applied Sciences Wiener Neustadt, Austria,Competence Center for Preclinical Imaging and Biomedical Engineering, University of Applied Sciences Wiener Neustadt, Austria
| | - Barbara Knäusl
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria,MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Markus Stock
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | | | - Dietmar Georg
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Peter Kuess
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria; MedAustron Ion Therapy Center, Wiener Neustadt, Austria.
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Thummerer A, Seller Oria C, Zaffino P, Meijers A, Guterres Marmitt G, Wijsman R, Seco J, Langendijk JA, Knopf AC, Spadea MF, Both S. Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer. Med Phys 2021; 48:7673-7684. [PMID: 34725829 PMCID: PMC9299115 DOI: 10.1002/mp.15333] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/22/2021] [Accepted: 10/27/2021] [Indexed: 01/14/2023] Open
Abstract
Purpose Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients. Methods A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors. Results On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%). Conclusion CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.
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Affiliation(s)
- Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Carmen Seller Oria
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Gabriel Guterres Marmitt
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Robin Wijsman
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Joao Seco
- Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsfoschungszentrum (DKFZ), Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Johannes Albertus Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antje-Christin Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Boulanger M, Nunes JC, Chourak H, Largent A, Tahri S, Acosta O, De Crevoisier R, Lafond C, Barateau A. Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review. Phys Med 2021; 89:265-281. [PMID: 34474325 DOI: 10.1016/j.ejmp.2021.07.027] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose calculation. This work reviewed deep learning (DL) sCT generation methods and their associated image and dose evaluation, in the context of MRI-based dose calculation. METHODS We searched the PubMed and ScienceDirect electronic databases from January 2010 to March 2021. For each paper, several items were screened and compiled in figures and tables. RESULTS This review included 57 studies. The DL methods were either generator-only based (45% of the reviewed studies), or generative adversarial network (GAN) architecture and its variants (55% of the reviewed studies). The brain and pelvis were the most commonly investigated anatomical localizations (39% and 28% of the reviewed studies, respectively), and more rarely, the head-and-neck (H&N) (15%), abdomen (10%), liver (5%) or breast (3%). All the studies performed an image evaluation of sCTs with a diversity of metrics, with only 36 studies performing dosimetric evaluations of sCT. CONCLUSIONS The median mean absolute errors were around 76 HU for the brain and H&N sCTs and 40 HU for the pelvis sCTs. For the brain, the mean dose difference between the sCT and the reference CT was <2%. For the H&N and pelvis, the mean dose difference was below 1% in most of the studies. Recent GAN architectures have advantages compared to generator-only, but no superiority was found in term of image or dose sCT uncertainties. Key challenges of DL-based sCT generation methods from MRI in radiotherapy is the management of movement for abdominal and thoracic localizations, the standardization of sCT evaluation, and the investigation of multicenter impacts.
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Affiliation(s)
- M Boulanger
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Jean-Claude Nunes
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
| | - H Chourak
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France; CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia
| | - A Largent
- Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA
| | - S Tahri
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - O Acosta
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - R De Crevoisier
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - C Lafond
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - A Barateau
- Univ. Rennes 1, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
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Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Med Phys 2021; 48:6537-6566. [PMID: 34407209 DOI: 10.1002/mp.15150] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/06/2021] [Accepted: 07/13/2021] [Indexed: 01/22/2023] Open
Abstract
Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
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Affiliation(s)
- Maria Francesca Spadea
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Matteo Maspero
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands
| | - Paolo Zaffino
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Joao Seco
- Division of Biomedical Physics in Radiation Oncology, DKFZ German Cancer Research Center, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
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Seller Oria C, Thummerer A, Free J, Langendijk JA, Both S, Knopf AC, Meijers A. Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients. Med Phys 2021; 48:4498-4505. [PMID: 34077554 PMCID: PMC8456797 DOI: 10.1002/mp.15020] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/28/2021] [Accepted: 05/28/2021] [Indexed: 01/12/2023] Open
Abstract
Purpose Cone‐beam CT (CBCT)‐based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range‐probing (RP)‐based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs. Methods A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images. Results Mean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs. Conclusions The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.
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Affiliation(s)
- Carmen Seller Oria
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jeffrey Free
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antje C Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Irmak S, Zimmermann L, Georg D, Kuess P, Lechner W. Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region. Med Phys 2021; 48:4560-4571. [PMID: 34028053 DOI: 10.1002/mp.14987] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 05/06/2021] [Accepted: 05/09/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated. METHODS 41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( C B C T RS ) and a population-based dose calculation method ( C B C T Pop ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs. RESULTS The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2 mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0 ± 0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0 ± 0.8% and 99.1 ± 0.8% for the C B C T RS and C B C T Pop , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4 ± 1.6% and 99.2 ± 0.6% for C B C T RS and C B C T Pop , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. CONCLUSION The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the C B C T RS approach in an MR only workflow.
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Affiliation(s)
- Sinan Irmak
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Lukas Zimmermann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.,Faculty of Engineering, University of Applied Sciences, Wiener Neustadt, Austria.,Competence Center for Preclinical Imaging and Biomedical Engineering, University of Applied Sciences, Wiener Neustadt, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Peter Kuess
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
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Wang C, Uh J, Merchant TE, Hua CH, Acharya S. Facilitating MR-Guided Adaptive Proton Therapy in Children Using Deep Learning-Based Synthetic CT. Int J Part Ther 2021; 8:11-20. [PMID: 35127971 PMCID: PMC8768893 DOI: 10.14338/ijpt-20-00099.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 05/10/2021] [Indexed: 11/21/2022] Open
Abstract
Abstract
Purpose
To determine whether self-attention cycle-generative adversarial networks (cycle-GANs), a novel deep-learning method, can generate accurate synthetic computed tomography (sCT) to facilitate adaptive proton therapy in children with brain tumors.
Materials and Methods
Both CT and T1-weighted magnetic resonance imaging (MRI) of 125 children (ages 1-20 years) with brain tumors were included in the training dataset. A model introducing a self-attention mechanism into the conventional cycle-GAN was created to enhance tissue interfaces and reduce noise. The test dataset consisted of 7 patients (ages 2-14 years) who underwent adaptive planning because of changes in anatomy discovered on MRI during proton therapy. The MRI during proton therapy-based sCT was compared with replanning CT (ground truth).
Results
The Hounsfield unit-mean absolute error was significantly reduced with self-attention cycle-GAN, as compared with conventional cycle-GAN (65.3 ± 13.9 versus 88.9 ± 19.3, P < .01). The average 3-dimensional gamma passing rates (2%/2 mm criteria) for the original plan on the anatomy of the day and for the adapted plan were high (97.6% ± 1.2% and 98.9 ± 0.9%, respectively) when using sCT generated by self-attention cycle-GAN. The mean absolute differences in clinical target volume (CTV) receiving 95% of the prescription dose and 80% distal falloff along the beam axis were 1.1% ± 0.8% and 1.1 ± 0.9 mm, respectively. Areas of greatest dose difference were distal to the CTV and corresponded to shifts in distal falloff. Plan adaptation was appropriately triggered in all test patients when using sCT.
Conclusion
The novel cycle-GAN model with self-attention outperforms conventional cycle-GAN for children with brain tumors. Encouraging dosimetric results suggest that sCT generation can be used to identify patients who would benefit from adaptive replanning.
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Affiliation(s)
- Chuang Wang
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Jinsoo Uh
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Thomas E. Merchant
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Chia-ho Hua
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Sahaja Acharya
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
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Training a deep neural network coping with diversities in abdominal and pelvic images of children and young adults for CBCT-based adaptive proton therapy. Radiother Oncol 2021; 160:250-258. [PMID: 33992626 DOI: 10.1016/j.radonc.2021.05.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE To train a deep neural network for correcting abdominal and pelvic cone-beam computed tomography (CBCT) of children and young adults in the presence of diverse patient size, anatomic extent, and scan parameters. MATERIALS AND METHODS Pretreatment CBCT and planning/repeat CT image pairs from 64 children and young adults treated with proton therapy (aged 1-23 years) were analyzed. To evaluate the impact of anatomic extent in CBCT and data size in the training data, we compared the performance of three cycle-consistent generative adversarial network models that were separately trained by three datasets comprising abdominal (n = 21), pelvic (n = 29), and combined abdominal-pelvic image pairs (n = 50), respectively. The maximum body width of each patient was normalized to a fixed width before training and model application to reduce the impact of variations in body size. The corrected CBCT images by the three models were comparatively evaluated against the repeat CT closest in time to the CBCT (median gap, 0 days; range, 0-6 days) in HU accuracy, estimated dose distribution, and proton range. RESULTS The network model trained by the combined dataset significantly outperformed the abdomen and pelvis models in mean absolute HU error of the corrected CBCT from 14 testing patients (47 ± 7 HU versus 51 ± 8 HU; paired Wilcoxon signed-rank test, P < 0.01). The larger error (60 ± 7 HU) without the body-size normalization confirmed the efficacy of the preprocessing. The model trained with the combined dataset resulted in gamma passing rates of 98.5 ± 1.9% (2%/2 mm criterion) and the range (80% distal fall-off) differences from the reference within ±3 mm for 91.2 ± 11.5% beamlets. CONCLUSION Combining data from adjacent anatomic sites and normalizing age-dependent body sizes in children and young adults were beneficial in training a neural network to accurately estimate proton dose from CBCT despite limited training data size and anatomic diversities.
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Rodriguez A, Pool R, Ortegon J, Escobar B, Barbosa R. Effect of the Agglomerate Geometry on the Effective Electrical Conductivity of a Porous Electrode. MEMBRANES 2021; 11:357. [PMID: 34068836 PMCID: PMC8153589 DOI: 10.3390/membranes11050357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/24/2021] [Accepted: 05/03/2021] [Indexed: 11/17/2022]
Abstract
The study of the microstructure of random heterogeneous materials, related to an electrochemical device, is relevant because their effective macroscopic properties, e.g., electrical or proton conductivity, are a function of their effective transport coefficients (ETC). The magnitude of ETC depends on the distribution and properties of the material phase. In this work, an algorithm is developed to generate stochastic two-phase (binary) image configurations with multiple geometries and polydispersed particle sizes. The recognizable geometry in the images is represented by the white phase dispersed and characterized by statistical descriptors (two-point and line-path correlation functions). Percolation is obtained for the geometries by identifying an infinite cluster to guarantee the connection between the edges of the microstructures. Finally, the finite volume method is used to determine the ETC. Agglomerate phase results show that the geometry with the highest local current distribution is the triangular geometry. In the matrix phase, the most significant results are obtained by circular geometry, while the lowest is obtained by the 3-sided polygon. The proposed methodology allows to establish criteria based on percolation and surface fraction to assure effective electrical conduction according to their geometric distribution; results provide an insight for the microstructure development with high projection to be used to improve the electrode of a Membrane Electrode Assembly (MEA).
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Affiliation(s)
- Abimael Rodriguez
- División de Ciencias e Ingeniería, Universidad de Quintana Roo, Boulevard Bahía s/n, Chetumal 77019, Quintana Roo, Mexico; (A.R.); (R.P.); (J.O.)
| | - Roger Pool
- División de Ciencias e Ingeniería, Universidad de Quintana Roo, Boulevard Bahía s/n, Chetumal 77019, Quintana Roo, Mexico; (A.R.); (R.P.); (J.O.)
| | - Jaime Ortegon
- División de Ciencias e Ingeniería, Universidad de Quintana Roo, Boulevard Bahía s/n, Chetumal 77019, Quintana Roo, Mexico; (A.R.); (R.P.); (J.O.)
| | - Beatriz Escobar
- Unidad de Energía Renovable, Centro de Investigación Científica de Yucatán, C 43 No 130, Chuburná de Hidalgo, Mérida 97200, Yucatán, Mexico;
| | - Romeli Barbosa
- División de Ciencias e Ingeniería, Universidad de Quintana Roo, Boulevard Bahía s/n, Chetumal 77019, Quintana Roo, Mexico; (A.R.); (R.P.); (J.O.)
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Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts. Sci Rep 2021; 11:6418. [PMID: 33742070 PMCID: PMC7979766 DOI: 10.1038/s41598-021-85671-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 02/28/2021] [Indexed: 12/24/2022] Open
Abstract
Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient’s image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation [Harrell’s concordance indices (HCIs) of 0.78, 0.74 and 0.80] in two out of three testing cohorts (HCIs of 0.88, 0.67 and 0.77). Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation.
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Liu R, Lei Y, Wang T, Zhou J, Roper J, Lin L, McDonald MW, Bradley JD, Curran WJ, Liu T, Yang X. Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN. Phys Med Biol 2021; 66:065014. [PMID: 33596558 DOI: 10.1088/1361-6560/abe736] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
MRI-only treatment planning is highly desirable in the current proton radiation therapy workflow due to its appealing advantages such as bypassing MR-CT co-registration, avoiding x-ray CT exposure dose and reduced medical cost. However, MRI alone cannot provide stopping power ratio (SPR) information for dose calculations. Given that dual energy CT (DECT) can estimate SPR with higher accuracy than conventional single energy CT, we propose a deep learning-based method in this study to generate synthetic DECT (sDECT) from MRI to calculate SPR. Since the contrast difference between high-energy and low-energy CT (LECT) is important, and in order to accurately model this difference, we propose a novel label generative adversarial network-based model which can not only discriminate the realism of sDECT but also differentiate high-energy CT (HECT) and LECT from DECT. A cohort of 57 head-and-neck cancer patients with DECT and MRI pairs were used to validate the performance of the proposed framework. The results of sDECT and its derived SPR maps were compared with clinical DECT and the corresponding SPR, respectively. The mean absolute error for synthetic LECT and HECT were 79.98 ± 18.11 HU and 80.15 ± 16.27 HU, respectively. The corresponding SPR maps generated from sDECT showed a normalized mean absolute error as 5.22% ± 1.23%. By comparing with the traditional Cycle GANs, our proposed method significantly improves the accuracy of sDECT. The results indicate that on our dataset, the sDECT image form MRI is close to planning DECT, and thus shows promising potential for generating SPR maps for proton therapy.
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Affiliation(s)
- Ruirui Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Mark W McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Jeffrey D Bradley
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
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Li W, Kazemifar S, Bai T, Nguyen D, Weng Y, Li Y, Xia J, Xiong J, Xie Y, Owrangi AM, Jiang SB. Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning. Biomed Phys Eng Express 2021; 7. [PMID: 33545707 DOI: 10.1088/2057-1976/abe3a7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/05/2021] [Indexed: 11/12/2022]
Abstract
PURPOSE Synthetic CT generation is the focus of many studies, however, only models on data with the same dataset were tested. Therefore, how well the trained model will work for data from different hospitals and MR protocols is still unknown. In this study, we addressed the model generalization problem for the MR-to-CT conversion task. METHODS Brain T2 MR and corresponding CT images were collected from one hospital and brain T1-FLAIR, T1-POST MR, and corresponding CT images were collected from another hospital. To investigate the model's generalizability ability, four potential solutions were proposed: source model, target model, combined model, and adapted model. All models were trained using the CycleGAN network. The source model was trained with a source domain dataset from scratch and tested with a target domain dataset. The target model was trained with a target domain dataset and tested with a target domain dataset. The combined model was trained with both source domain and target domain datasets, and tested with the target domain dataset. The adapted model used a transfer learning strategy to train a CycleGAN model with a source domain dataset and retrain the pre-trained model with a target domain dataset. MAE, RMSE, PSNR, and SSIM were used to quantitatively evaluate model performance on a target domain dataset. RESULTS The adapted model achieved best quantitative results of 74.56±8.61, 193.18±17.98, 28.30±0.83, and 0.84±0.01 for MAE, RMSE, PSNR, and SSIM using the T1-FLAIR dataset and 74.89±15.64, 195.73±31.29, 27.72±1.43, and 0.83±0.04 for MAE, RMSE, PSNR, and SSIM using the T1-POST dataset. The source model had the poorest performance. CONCLUSIONS This work indicates high generalization ability to generate synthetic CT images from small training datasets of MR images using pre-trained CycleGAN. The quantitative results of the test data, including different scanning protocols and different acquisition centers, indicated the proof of this concept.
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Affiliation(s)
- Wen Li
- Department of Radiation Oncology, Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 75235, CHINA
| | - Samaneh Kazemifar
- UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Ti Bai
- Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Dan Nguyen
- Department of Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Yaochung Weng
- Radiation Oncology, UT Southwestern Medical, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Yafen Li
- Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 75235, CHINA
| | - Jun Xia
- Shenzhen Second People's Hospital, 2280 Inwood Rd., Shenzhen, 75235, CHINA
| | - Jing Xiong
- Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 75235, CHINA
| | - Yaoqin Xie
- Chinese Academy of Sciences, 2280 Inwood Rd., Beijing, 100864, CHINA
| | - Amir M Owrangi
- Department of Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
| | - Steve B Jiang
- Department of Radiation Oncology, UT Southwestern Department of Radiation Oncology, 2280 Inwood Rd., Dallas, Texas, 75235, UNITED STATES
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Zimmermann L, Buschmann M, Herrmann H, Heilemann G, Kuess P, Goldner G, Nyholm T, Georg D, Nesvacil N. An MR-only acquisition and artificial intelligence based image-processing protocol for photon and proton therapy using a low field MR. Z Med Phys 2021; 31:78-88. [PMID: 33455822 DOI: 10.1016/j.zemedi.2020.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/14/2020] [Accepted: 10/27/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Recent developments on synthetically generated CTs (sCT), hybrid MRI linacs and MR-only simulations underlined the clinical feasibility and acceptance of MR guided radiation therapy. However, considering clinical application of open and low field MR with a limited field of view can result in truncation of the patient's anatomy which further affects the MR to sCT conversion. In this study an acquisition protocol and subsequent MR image stitching is proposed to overcome the limited field of view restriction of open MR scanners, for MR-only photon and proton therapy. MATERIAL AND METHODS 12 prostate cancer patients scanned with an open 0.35T scanner were included. To obtain the full body contour an enhanced imaging protocol including two repeated scans after bilateral table movement was introduced. All required structures (patient contour, target and organ at risk) were delineated on a post-processed combined transversal image set (stitched MRI). The postprocessed MR was converted into a sCT by a pretrained neural network generator. Inversely planned photon and proton plans (VMAT and SFUD) were designed using the sCT and recalculated for rigidly and deformably registered CT images and compared based on D2%, D50%, V70Gy for organs at risk and based on D2%, D50%, D98% for the CTV and PTV. The stitched MRI and the untruncated MRI were compared to the CT, and the maximum surface distance was calculated. The sCT was evaluated with respect to delineation accuracy by comparing on stitched MRI and sCT using the DICE coefficient for femoral bones and the whole body. RESULTS Maximum surface distance analysis revealed uncertainties in lateral direction of 1-3mm on average. DICE coefficient analysis confirms good performance of the sCT conversion, i.e. 92%, 93%, and 100% were obtained for femoral bone left and right and whole body. Dose comparison resulted in uncertainties below 1% between deformed CT and sCT and below 2% between rigidly registered CT and sCT in the CTV for photon and proton treatment plans. DISCUSSION A newly developed acquisition protocol for open MR scanners and subsequent Sct generation revealed good acceptance for photon and proton therapy. Moreover, this protocol tackles the restriction of the limited FOVs and expands the capacities towards MR guided proton therapy with horizontal beam lines.
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Affiliation(s)
- Lukas Zimmermann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
| | - Martin Buschmann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Harald Herrmann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gerd Heilemann
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Peter Kuess
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gregor Goldner
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Dietmar Georg
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Nicole Nesvacil
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
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Thummerer A, de Jong BA, Zaffino P, Meijers A, Marmitt GG, Seco J, Steenbakkers RJHM, Langendijk JA, Both S, Spadea MF, Knopf AC. Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients. ACTA ACUST UNITED AC 2020; 65:235036. [DOI: 10.1088/1361-6560/abb1d6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Abstract
Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.
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Affiliation(s)
- Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States of America
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46
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Maspero M, Bentvelzen LG, Savenije MH, Guerreiro F, Seravalli E, Janssens GO, van den Berg CA, Philippens ME. Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy. Radiother Oncol 2020; 153:197-204. [DOI: 10.1016/j.radonc.2020.09.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/14/2020] [Accepted: 09/14/2020] [Indexed: 02/07/2023]
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Dumlu HS, Meschini G, Kurz C, Kamp F, Baroni G, Belka C, Paganelli C, Riboldi M. Dosimetric impact of geometric distortions in an MRI-only proton therapy workflow for lung, liver and pancreas. Z Med Phys 2020; 32:85-97. [PMID: 33168274 PMCID: PMC9948883 DOI: 10.1016/j.zemedi.2020.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 09/02/2020] [Accepted: 10/01/2020] [Indexed: 12/25/2022]
Abstract
In a radiation therapy workflow based on Magnetic Resonance Imaging (MRI), dosimetric errors may arise due to geometric distortions introduced by MRI. The aim of this study was to quantify the dosimetric effect of system-dependent geometric distortions in an MRI-only workflow for proton therapy applied at extra-cranial sites. An approach was developed, in which computed tomography (CT) images were distorted using an MRI displacement map, which represented the MR distortions in a spoiled gradient-echo sequence due to gradient nonlinearities and static magnetic field inhomogeneities. A retrospective study was conducted on 4DCT/MRI digital phantoms and 18 4DCT clinical datasets of the thoraco-abdominal site. The treatment plans were designed and separately optimized for each beam in a beam specific Planning Target Volume on the distorted CT, and the final dose distribution was obtained as the average. The dose was then recalculated in undistorted CT using the same beam geometry and beam weights. The analysis was performed in terms of Dose Volume Histogram (DVH) parameters. No clinically relevant dosimetric impact was observed on organs at risk, whereas in the target structure, geometric distortions caused statistically significant variations in the planned dose DVH parameters and dose homogeneity index (DHI). The dosimetric variations in the target structure were smaller in abdominal cases (ΔD2%, ΔD98%, and ΔDmean all below 0.1% and ΔDHI below 0.003) compared to the lung cases. Indeed, lung patients with tumors isolated inside lung parenchyma exhibited higher dosimetric variations (ΔD2%≥0.3%, ΔD98%≥15.9%, ΔDmean≥3.3% and ΔDHI≥0.102) than lung patients with tumor close to soft tissue (ΔD2%≤0.4%, ΔD98%≤5.6%, ΔDmean≤0.9% and ΔDHI≤0.027) potentially due to higher density variations along the beam path. Results suggest the potential applicability of MRI-only proton therapy, provided that specific analysis is applied for isolated lung tumors.
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Affiliation(s)
- Hatice Selcen Dumlu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany
| | - Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Centro Nazionale di Adroterapia Oncologica, Strada Campeggi 53, 27100 Pavia, Italy
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany; German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany.
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48
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Jiang J, Hu YC, Tyagi N, Wang C, Lee N, Deasy JO, Sean B, Veeraraghavan H. Self-derived organ attention for unpaired CT-MRI deep domain adaptation based MRI segmentation. Phys Med Biol 2020; 65:205001. [PMID: 33027063 DOI: 10.1088/1361-6560/ab9fca] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
To develop and evaluate a deep learning method to segment parotid glands from MRI using unannotated MRI and unpaired expert-segmented CT datasets. We introduced a new self-derived organ attention deep learning network for combined CT to MRI image-to-image translation (I2I) and MRI segmentation, all trained as an end-to-end network. The expert segmentations available on CT scans were combined with the I2I translated pseudo MR images to train the MRI segmentation network. Once trained, the MRI segmentation network alone is required. We introduced an organ attention discriminator that constrains the CT to MR generator to synthesize pseudo MR images that preserve organ geometry and appearance statistics as in real MRI. The I2I translation network training was regularized using the organ attention discriminator, global image-matching discriminator, and cycle consistency losses. MRI segmentation training was regularized by using cross-entropy loss. Segmentation performance was compared against multiple domain adaptation-based segmentation methods using the Dice similarity coefficient (DSC) and Hausdorff distance at the 95th percentile (HD95). All networks were trained using 85 unlabeled T2-weighted fat suppressed (T2wFS) MRIs and 96 expert-segmented CT scans. Performance upper-limit was based on fully supervised MRI training done using the 85 T2wFS MRI with expert segmentations. Independent evaluation was performed on 77 MRIs never used in training. The proposed approach achieved the highest accuracy (left parotid: DSC 0.82 ± 0.03, HD95 2.98 ± 1.01 mm; right parotid: 0.81 ± 0.05, HD95 3.14 ± 1.17 mm) compared to other methods. This accuracy was close to the reference fully supervised MRI segmentation (DSC of 0.84 ± 0.04, a HD95 of 2.24 ± 0.77 mm for the left parotid, and a DSC of 0.84 ± 0.06 and HD95 of 2.32 ± 1.37 mm for the right parotid glands).
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Affiliation(s)
- Jue Jiang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States of America
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Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance. Radiother Oncol 2020; 153:55-66. [PMID: 32920005 DOI: 10.1016/j.radonc.2020.09.008] [Citation(s) in RCA: 136] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 02/06/2023]
Abstract
Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.
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Peng Y, Chen S, Qin A, Chen M, Gao X, Liu Y, Miao J, Gu H, Zhao C, Deng X, Qi Z. Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning. Radiother Oncol 2020; 150:217-224. [DOI: 10.1016/j.radonc.2020.06.049] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 06/23/2020] [Accepted: 06/25/2020] [Indexed: 12/27/2022]
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