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Bahloul MA, Jabeen S, Benoumhani S, Alsaleh HA, Belkhatir Z, Al-Wabil A. Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning. J Appl Clin Med Phys 2024:e14499. [PMID: 39325781 DOI: 10.1002/acm2.14499] [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: 03/26/2024] [Revised: 06/27/2024] [Accepted: 07/26/2024] [Indexed: 09/28/2024] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) and Computed tomography (CT) are crucial imaging techniques in both diagnostic imaging and radiation therapy. MRI provides excellent soft tissue contrast but lacks the direct electron density data needed to calculate dosage. CT, on the other hand, remains the gold standard due to its accurate electron density information in radiation therapy planning (RTP) but it exposes patients to ionizing radiation. Synthetic CT (sCT) generation from MRI has been a focused study field in the last few years due to cost effectiveness as well as for the objective of minimizing side-effects of using more than one imaging modality for treatment simulation. It offers significant time and cost efficiencies, bypassing the complexities of co-registration, and potentially improving treatment accuracy by minimizing registration-related errors. In an effort to navigate the quickly developing field of precision medicine, this paper investigates recent advancements in sCT generation techniques, particularly those using machine learning (ML) and deep learning (DL). The review highlights the potential of these techniques to improve the efficiency and accuracy of sCT generation for use in RTP by improving patient care and reducing healthcare costs. The intricate web of sCT generation techniques is scrutinized critically, with clinical implications and technical underpinnings for enhanced patient care revealed. PURPOSE This review aims to provide an overview of the most recent advancements in sCT generation from MRI with a particular focus of its use within RTP, emphasizing on techniques, performance evaluation, clinical applications, future research trends and open challenges in the field. METHODS A thorough search strategy was employed to conduct a systematic literature review across major scientific databases. Focusing on the past decade's advancements, this review critically examines emerging approaches introduced from 2013 to 2023 for generating sCT from MRI, providing a comprehensive analysis of their methodologies, ultimately fostering further advancement in the field. This study highlighted significant contributions, identified challenges, and provided an overview of successes within RTP. Classifying the identified approaches, contrasting their advantages and disadvantages, and identifying broad trends were all part of the review's synthesis process. RESULTS The review identifies various sCT generation approaches, consisting atlas-based, segmentation-based, multi-modal fusion, hybrid approaches, ML and DL-based techniques. These approaches are evaluated for image quality, dosimetric accuracy, and clinical acceptability. They are used for MRI-only radiation treatment, adaptive radiotherapy, and MR/PET attenuation correction. The review also highlights the diversity of methodologies for sCT generation, each with its own advantages and limitations. Emerging trends incorporate the integration of advanced imaging modalities including various MRI sequences like Dixon sequences, T1-weighted (T1W), T2-weighted (T2W), as well as hybrid approaches for enhanced accuracy. CONCLUSIONS The study examines MRI-based sCT generation, to minimize negative effects of acquiring both modalities. The study reviews 2013-2023 studies on MRI to sCT generation methods, aiming to revolutionize RTP by reducing use of ionizing radiation and improving patient outcomes. The review provides insights for researchers and practitioners, emphasizing the need for standardized validation procedures and collaborative efforts to refine methods and address limitations. It anticipates the continued evolution of techniques to improve the precision of sCT in RTP.
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Affiliation(s)
- Mohamed A Bahloul
- College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
- Translational Biomedical Engineering Research Lab, College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
| | - Saima Jabeen
- College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
- Translational Biomedical Engineering Research Lab, College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
- AI Research Center, College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
| | - Sara Benoumhani
- College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
- AI Research Center, College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
| | | | - Zehor Belkhatir
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Areej Al-Wabil
- College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
- AI Research Center, College of Engineering, Alfaisal University, Riyadh, Saudi Arabia
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Luu HM, Yoo GS, Park W, Park SH. CycleSeg: Simultaneous synthetic CT generation and unsupervised segmentation for MR-only radiotherapy treatment planning of prostate cancer. Med Phys 2024; 51:4365-4379. [PMID: 38323835 DOI: 10.1002/mp.16976] [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: 07/13/2023] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND MR-only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose calculations to enable MR-only treatment planning. Automatic segmentation of relevant organs in MR images can accelerate the process by preventing the time-consuming manual contouring step. However, the segmentation label is available only for CT data in many cases. PURPOSE We propose CycleSeg, a unified framework that generates sCT and corresponding segmentation from MR images without access to MR segmentation labels METHODS: CycleSeg utilizes the CycleGAN formulation to perform unpaired synthesis of sCT and image alignment. To enable MR (sCT) segmentation, CycleSeg incorporates unsupervised domain adaptation by using a pseudo-labeling approach with feature alignment in semantic segmentation space. In contrast to previous approaches that perform segmentation on MR data, CycleSeg could perform segmentation on both MR and sCT. Experiments were performed with data from prostate cancer patients, with 78/7/10 subjects in the training/validation/test sets, respectively. RESULTS CycleSeg showed the best sCT generation results, with the lowest mean absolute error of 102.2 and the lowest Fréchet inception distance of 13.0. CycleSeg also performed best on MR segmentation, with the highest average dice score of 81.0 and 81.1 for MR and sCT segmentation, respectively. Ablation experiments confirmed the contribution of the proposed components of CycleSeg. CONCLUSION CycleSeg effectively synthesized CT and performed segmentation on MR images of prostate cancer patients. Thus, CycleSeg has the potential to expedite MR-only radiotherapy treatment planning, reducing the prescribed scans and manual segmentation effort, and increasing throughput.
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Affiliation(s)
- Huan Minh Luu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Gyu Sang Yoo
- Department of Radiation Oncology, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Won Park
- Department of Radiation Oncology, Samsung Medical Center, Seoul, Republic of Korea
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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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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Cao G, Li Y, Wu S, Li W, Long J, Xie Y, Xia J. Clinical feasibility of MRI-based synthetic CT imaging in the diagnosis of lumbar disc herniation: a comparative study. Acta Radiol 2024; 65:41-48. [PMID: 37071506 PMCID: PMC10798008 DOI: 10.1177/02841851231169173] [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: 07/29/2022] [Accepted: 12/05/2022] [Indexed: 04/19/2023]
Abstract
BACKGROUND Computed tomography (CT) and magnetic resonance imaging (MRI) are indicated for use in preoperative planning and may complicate diagnosis and place a burden on patients with lumbar disc herniation. PURPOSE To investigate the diagnostic potential of MRI-based synthetic CT with conventional CT in the diagnosis of lumbar disc herniation. MATERIAL AND METHODS After obtaining prior institutional review board approval, 19 patients who underwent conventional and synthetic CT imaging were enrolled in this prospective study. Synthetic CT images were generated from the MRI data using U-net. The two sets of images were compared and analyzed qualitatively by two musculoskeletal radiologists. The images were rated on a 4-point scale to determine their subjective quality. The agreement between the conventional and synthetic images for a diagnosis of lumbar disc herniation was determined independently using the kappa statistic. The diagnostic performances of conventional and synthetic CT images were evaluated for sensitivity, specificity, and accuracy, and the consensual results based on T2-weighted imaging were employed as the reference standard. RESULTS The inter-reader and intra-reader agreement were almost moderate for all evaluated modalities (κ = 0.57-0.79 and 0.47-0.75, respectively). The sensitivity, specificity, and accuracy for detecting lumbar disc herniation were similar for synthetic and conventional CT images (synthetic vs. conventional, reader 1: sensitivity = 91% vs. 81%, specificity = 83% vs. 100%, accuracy = 87% vs. 91%; P < 0.001; reader 2: sensitivity = 84% vs. 81%, specificity = 85% vs. 98%, accuracy = 84% vs. 90%; P < 0.001). CONCLUSION Synthetic CT images can be used in the diagnostics of lumbar disc herniation.
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Affiliation(s)
- Gan Cao
- Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, PR China
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, PR China
| | - Yafen Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Shibin Wu
- PingAn Technology, Shenzhen, Guangdong, PR China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, PR China
| | - Jia Long
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, PR China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, PR China
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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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Lane SA, Slater JM, Yang GY. Image-Guided Proton Therapy: A Comprehensive Review. Cancers (Basel) 2023; 15:cancers15092555. [PMID: 37174022 PMCID: PMC10177085 DOI: 10.3390/cancers15092555] [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: 03/04/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Image guidance for radiation therapy can improve the accuracy of the delivery of radiation, leading to an improved therapeutic ratio. Proton radiation is able to deliver a highly conformal dose to a target due to its advantageous dosimetric properties, including the Bragg peak. Proton therapy established the standard for daily image guidance as a means of minimizing uncertainties associated with proton treatment. With the increasing adoption of the use of proton therapy over time, image guidance systems for this modality have been changing. The unique properties of proton radiation present a number of differences in image guidance from photon therapy. This paper describes CT and MRI-based simulation and methods of daily image guidance. Developments in dose-guided radiation, upright treatment, and FLASH RT are discussed as well.
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Affiliation(s)
- Shelby A Lane
- James M. Slater, MD Proton Treatment and Research Center, Loma Linda University, Loma Linda, CA 92354, USA
| | - Jason M Slater
- James M. Slater, MD Proton Treatment and Research Center, Loma Linda University, Loma Linda, CA 92354, USA
| | - Gary Y Yang
- James M. Slater, MD Proton Treatment and Research Center, Loma Linda University, Loma Linda, CA 92354, USA
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Nelissen KJ, Versteijne E, Senan S, Hoffmans D, Slotman BJ, Verbakel WFAR. Evaluation of a workflow for cone-beam CT-guided online adaptive palliative radiotherapy planned using diagnostic CT scans. J Appl Clin Med Phys 2023; 24:e13841. [PMID: 36573256 PMCID: PMC10018665 DOI: 10.1002/acm2.13841] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2003] [Revised: 09/15/2022] [Accepted: 10/17/2022] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Single-visit radiotherapy (RT) is beneficial for patients requiring pain control and can limit interruptions to systemic treatments. However, the requirement for a dedicated planning CT (pCT)-scan can result in treatment delays. We developed a workflow involving preplanning on available diagnostic CT (dCT) imaging, followed by online plan adaption using a cone-beam CT (CBCT)-scan prior to RT-delivery, in order to account for any changes in anatomy and target position. METHODS Patients previously treated with palliative RT for bone metastases were selected from our hospital database. Patient dCT-images were deformed to treatment CBCTs in the Ethos platform (Varian Medical Systems) and a synthetic CT (sCT) generated. Treatment quality was analyzed by comparing a coverage of the V95% of the planning/clinical target volume and different organ-at-risk (OAR) doses between adapted and initial clinical treatment plans. Doses were recalculated on the CBCT and sCT in a separate treatment planning system. Adapted plan doses were measured on-couch using an anthropomorphic phantom with a Gafchromic EBT3 dosimetric film and compared to dose calculations. RESULTS All adapted treatment plans met the clinical goals for target and OARs and outperformed the original treatment plans calculated on the (daily) sCT. Differences in V95% of the target volume coverage between the initial and adapted treatments were <0.2%. Dose recalculations on CBCT and sCT were comparable, and the average gamma pass rate (3%/2 mm) of dosimetric measurements was 98.8%. CONCLUSIONS Online daily adaptive RT using dCTs instead of a dedicated pCT is feasible using the Ethos platform. This workflow has now been implemented clinically.
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Affiliation(s)
- Koen J. Nelissen
- Department of Radiation OncologyAmsterdam UMC location Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Cancer Center AmsterdamCancer Treatment and Quality of LifeAmsterdamThe Netherlands
| | - Eva Versteijne
- Department of Radiation OncologyAmsterdam UMC location Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Cancer Center AmsterdamCancer Treatment and Quality of LifeAmsterdamThe Netherlands
| | - Suresh Senan
- Department of Radiation OncologyAmsterdam UMC location Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Cancer Center AmsterdamCancer Treatment and Quality of LifeAmsterdamThe Netherlands
| | - Daan Hoffmans
- Department of Radiation OncologyAmsterdam UMC location Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Cancer Center AmsterdamCancer Treatment and Quality of LifeAmsterdamThe Netherlands
| | - Ben J. Slotman
- Department of Radiation OncologyAmsterdam UMC location Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Cancer Center AmsterdamCancer Treatment and Quality of LifeAmsterdamThe Netherlands
| | - Wilko F. A. R. Verbakel
- Department of Radiation OncologyAmsterdam UMC location Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Cancer Center AmsterdamCancer Treatment and Quality of LifeAmsterdamThe Netherlands
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Zhao B, Cheng T, Zhang X, Wang J, Zhu H, Zhao R, Li D, Zhang Z, Yu G. CT synthesis from MR in the pelvic area using Residual Transformer Conditional GAN. Comput Med Imaging Graph 2023; 103:102150. [PMID: 36493595 DOI: 10.1016/j.compmedimag.2022.102150] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/15/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022]
Abstract
Magnetic resonance (MR) image-guided radiation therapy is a hot topic in current radiation therapy research, which relies on MR to generate synthetic computed tomography (SCT) images for radiation therapy. Convolution-based generative adversarial networks (GAN) have achieved promising results in synthesizing CT from MR since the introduction of deep learning techniques. However, due to the local limitations of pure convolutional neural networks (CNN) structure and the local mismatch between paired MR and CT images, particularly in pelvic soft tissue, the performance of GAN in synthesizing CT from MR requires further improvement. In this paper, we propose a new GAN called Residual Transformer Conditional GAN (RTCGAN), which exploits the advantages of CNN in local texture details and Transformer in global correlation to extract multi-level features from MR and CT images. Furthermore, the feature reconstruction loss is used to further constrain the image potential features, reducing over-smoothing and local distortion of the SCT. The experiments show that RTCGAN is visually closer to the reference CT (RCT) image and achieves desirable results on local mismatch tissues. In the quantitative evaluation, the MAE, SSIM, and PSNR of RTCGAN are 45.05 HU, 0.9105, and 28.31 dB, respectively. All of them outperform other comparison methods, such as deep convolutional neural networks (DCNN), Pix2Pix, Attention-UNet, WPD-DAGAN, and HDL.
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Affiliation(s)
- Bo Zhao
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China
| | - Tingting Cheng
- Department of General practice, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China
| | - Xueren Zhang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China
| | - Jingjing Wang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China
| | - Hong Zhu
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China
| | - Rongchang Zhao
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China
| | - Zijian Zhang
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China
| | - Gang Yu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China
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VilasBoas-Ribeiro I, Franckena M, van Rhoon GC, Hernández-Tamames JA, Paulides MM. Using MRI to measure position and anatomy changes and assess their impact on the accuracy of hyperthermia treatment planning for cervical cancer. Int J Hyperthermia 2022; 40:2151648. [PMID: 36535922 DOI: 10.1080/02656736.2022.2151648] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE We studied the differences between planning and treatment position, their impact on the accuracy of hyperthermia treatment planning (HTP) predictions, and the relevance of including true treatment anatomy and position in HTP based on magnetic resonance (MR) images. MATERIALS AND METHODS All volunteers were scanned with an MR-compatible hyperthermia device, including a filled waterbolus, to replicate the treatment setup. In the planning setup, the volunteers were scanned without the device to reproduce the imaging in the current HTP. First, we used rigid registration to investigate the patient position displacements between the planning and treatment setup. Second, we performed HTP for the planning anatomy at both positions and the treatment mimicking anatomy to study the effects of positioning and anatomy on the quality of the simulated hyperthermia treatment. Treatment quality was evaluated using SAR-based parameters. RESULTS We found an average displacement of 2 cm between planning and treatment positions. These displacements caused average absolute differences of ∼12% for TC25 and 10.4%-15.9% in THQ. Furthermore, we found that including the accurate treatment position and anatomy in treatment planning led to an improvement of 2% in TC25 and 4.6%-10.6% in THQ. CONCLUSIONS This study showed that precise patient position and anatomy are relevant since these affect the accuracy of HTP predictions. The major part of improved accuracy is related to implementing the correct position of the patient in the applicator. Hence, our study shows a clear incentive to accurately match the patient position in HTP with the actual treatment.
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Affiliation(s)
- Iva VilasBoas-Ribeiro
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Martine Franckena
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Gerard C van Rhoon
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Applied Radiation and Isotopes, Reactor Institute Delft, Delft University of Technology, Delft, The Netherlands
| | - Juan A Hernández-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Margarethus M Paulides
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Care and Cure research lab (EM-4C&C) of the Electromagnetics Group, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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10
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Fan W, Sang Y, Zhou H, Xiao J, Fan Z, Ruan D. MRA-free intracranial vessel localization on MR vessel wall images. Sci Rep 2022; 12:6240. [PMID: 35422490 PMCID: PMC9010428 DOI: 10.1038/s41598-022-10256-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 03/31/2022] [Indexed: 11/08/2022] Open
Abstract
Analysis of vessel morphology is important in assessing intracranial atherosclerosis disease (ICAD). Recently, magnetic resonance (MR) vessel wall imaging (VWI) has been introduced to image ICAD and characterize morphology for atherosclerotic lesions. In order to automatically perform quantitative analysis on VWI data, MR angiography (MRA) acquired in the same imaging session is typically used to localize the vessel segments of interest. However, MRA may be unavailable caused by the lack or failure of the sequence in a VWI protocol. This study aims to investigate the feasibility to infer the vessel location directly from VWI. We propose to synergize an atlas-based method to preserve general vessel structure topology with a deep learning network in the motion field domain to correct the residual geometric error. Performance is quantified by examining the agreement between the extracted vessel structures from the pair-acquired and alignment-corrected angiogram, and the estimated output using a cross-validation scheme. Our proposed pipeline yields clinically feasible performance in localizing intracranial vessels, demonstrating the promise of performing vessel morphology analysis using VWI alone.
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Affiliation(s)
- Weijia Fan
- Department of Physics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yudi Sang
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Hanyue Zhou
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jiayu Xiao
- Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Zhaoyang Fan
- Department of Radiology, University of Southern California, Los Angeles, CA, USA
- Department of Radiation Oncology, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Dan Ruan
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA.
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Li X, Yadav P, McMillan AB. Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance-Only Radiation Therapy Planning Using Perceptual Loss Models. Pract Radiat Oncol 2022; 12:e40-e48. [PMID: 34450337 PMCID: PMC8741640 DOI: 10.1016/j.prro.2021.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/02/2021] [Accepted: 08/18/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region. METHODS AND MATERIALS Two models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT. RESULTS The sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image. CONCLUSIONS This study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.
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Affiliation(s)
| | - Poonam Yadav
- Human Oncology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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12
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Szalkowski G, Nie D, Zhu T, Yap PT, Lian J. Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept. Comput Biol Med 2021; 138:104917. [PMID: 34688037 DOI: 10.1016/j.compbiomed.2021.104917] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/16/2021] [Accepted: 09/29/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE To create synthetic CTs and digital reconstructed radiographs (DRRs) from MR images that allow for fiducial visualization and accurate dose calculation for MR-only radiosurgery. METHODS We developed a machine learning model to create synthetic CTs from pelvic MRs for prostate treatments. This model has been previously proven to generate synthetic CTs with accuracy on par or better than alternate methods, such as atlas-based registration. Our dataset consisted of 11 paired CT and conventional MR (T2) images used for previous CyberKnife (Accuray, Inc) radiotherapy treatments. The MR images were pre-processed to mimic the appearance of fiducial-enhancing images. Two models were trained for each parameter case, using a sub-set of the available image pairs, with the remaining images set aside for testing and validation of the model to identify the optimal patch size and number of image pairs used for training. Four models were then trained using the identified parameters and used to generate synthetic CTs, which in turn were used to generate DRRs at angles 45° and 315°, as would be used for a CyberKnife treatment. The synthetic CTs and DRRs were compared visually and using the mean squared error and peak signal-to-noise ratio against the ground-truth images to evaluate their similarity. RESULTS The synthetic CTs, as well as the DRRs generated from them, gave similar visualization of the fiducial markers in the prostate as the true counterparts. There was no significant difference found for the fiducial localization for the CTs and DRRs. Across the 8 DRRs analyzed, the mean MSE between the normalized true and synthetic DRRs was 0.66 ± 0.42% and the mean PSNR for this region was 22.9 ± 3.7 dB. For the full CTs, the mean MAE was 72.9 ± 88.1 HU and the mean PSNR was 31.2 ± 2.2 dB. CONCLUSIONS Our machine learning-based method provides a proof of concept of a way to generate synthetic CTs and DRRs for accurate dose calculation and fiducial localization for use in radiation treatment of the prostate.
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Affiliation(s)
- Gregory Szalkowski
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC, USA
| | - Dong Nie
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Tong Zhu
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Jun Lian
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC, USA.
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13
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Lui JCF, Tang AM, Law CC, Lee JCY, Lee FKH, Chiu J, Wong KH. A practical methodology to improve the dosimetric accuracy of MR-based radiotherapy simulation for brain tumors. Phys Med 2021; 91:1-12. [PMID: 34678686 DOI: 10.1016/j.ejmp.2021.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 11/25/2022] Open
Abstract
PURPOSE To investigate the dosimetric accuracy of synthetic computed tomography (sCT) images generated by a clinically-ready voxel-based MRI simulation package, and to develop a simple and feasible method to improve the accuracy. METHODS 20 patients with brain tumor were selected to undergo CT and MRI simulation. sCT images were generated by a clinical MRI simulation package. The discrepancy between planning CT and sCT in CT number and body contour were evaluated. To resolve the discrepancies, an sCT specific CT-relative electron density (RED) calibration curve was used, and a layer of pseudo-skin was created on the sCT. The dosimetric impact of these discrepancies, and the improvement brought about by the modifications, were evaluated by a planning study. Volumetric modulated arc therapy (VMAT) treatment plans for each patient were created and optimized on the planning CT, which were then transferred to the original sCT and the modified-sCT for dose re-calculation. Dosimetric comparisons and gamma analysis between the calculated doses in different images were performed. RESULTS The average gamma passing rate with 1%/1 mm criteria was only 70.8% for the comparison of dose distribution between planning CT and original sCT. The mean dose difference between the planning CT and the original sCT were -1.2% for PTV D95 and -1.7% for PTV Dmax, while the mean dose difference was within 0.7 Gy for all relevant OARs. After applying the modifications on the sCT, the average gamma passing rate was increased to 92.2%. Mean dose difference in PTV D95 and Dmax were reduced to -0.1% and -0.3% respectively. The mean dose difference was within 0.2 Gy for all OAR structures and no statistically significant difference were found. CONCLUSIONS The modified-sCT demonstrated improved dosimetric agreement with the planning CT. These results indicated the overall dosimetric accuracy and practicality of this improved MR-based treatment planning method.
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Affiliation(s)
- Jeffrey C F Lui
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong.
| | - Annie M Tang
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong
| | - C C Law
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong
| | - Jonan C Y Lee
- Department of Radiology, Queen Elizabeth Hospital, Hong Kong
| | - Francis K H Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong
| | - Jeffrey Chiu
- Department of Radiology, Queen Elizabeth Hospital, Hong Kong
| | - Kam-Hung Wong
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong
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14
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Olin AB, Thomas C, Hansen AE, Rasmussen JH, Krokos G, Urbano TG, Michaelidou A, Jakoby B, Ladefoged CN, Berthelsen AK, Håkansson K, Vogelius IR, Specht L, Barrington SF, Andersen FL, Fischer BM. Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging-Based Radiation Therapy Planning of Patients With Head and Neck Cancer. Adv Radiat Oncol 2021; 6:100762. [PMID: 34585026 PMCID: PMC8452789 DOI: 10.1016/j.adro.2021.100762] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/13/2021] [Accepted: 07/19/2021] [Indexed: 11/26/2022] Open
Abstract
Purpose Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients with head and neck cancer was evaluated in 2 scenarios: (1) using an independent external dataset, and (2) using a local dataset after an update of the model related to scanner software-induced changes to the input MRI. Methods and Materials Six patients from an external site and 17 patients from a local cohort were analyzed separately. Each patient underwent a CT and a PET/MRI with a Dixon MRI sequence over either one (external) or 2 (local) bed positions. For the external cohort, a previously developed deep learning model for deriving sCT from Dixon MRI was directly applied. For the local cohort, we adapted the model for an upgraded MRI acquisition using transfer learning and evaluated it in a leave-one-out process. The sCT mean absolute error for each patient was assessed. Radiotherapy dose plans based on sCT and CT were compared by assessing relevant absorbed dose differences in target volumes and organs at risk. Results The MAEs were 78 ± 13 HU and 76 ± 12 HU for the external and local cohort, respectively. For the external cohort, absorbed dose differences in target volumes were within ± 2.3% and within ± 1% in 95% of the cases. Differences in organs at risk were <2%. Similar results were obtained for the local cohort. Conclusions We have demonstrated a robust performance of a deep learning model for deriving sCT from MRI when applied to an independent external dataset. We updated the model to accommodate a larger axial field of view and software-induced changes to the input MRI. In both scenarios dose calculations based on sCT were similar to those of CT suggesting a robust and reliable method.
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Affiliation(s)
- Anders B Olin
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Christopher Thomas
- Department of Medical Physics, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.,Department of Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jacob H Rasmussen
- Department of Otorhinolaryngology, Head & Neck Surgery and Audiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Department of Otorhinolaryngology and Maxillofacial Surgery, Zealand University Hospital, Køge, Denmark
| | - Georgios Krokos
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Teresa Guerrero Urbano
- Department of Oncology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Andriana Michaelidou
- Department of Oncology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Björn Jakoby
- Siemens Healthcare GmbH, Erlangen, Germany.,University of Surrey, Guildford, Surrey, United Kingdom
| | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anne K Berthelsen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Katrin Håkansson
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ivan R Vogelius
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Lena Specht
- Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.,Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Sally F Barrington
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Barbara M Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
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15
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FDG-PET/CT and MR imaging for target volume delineation in rectal cancer radiotherapy treatment planning: a systematic review. JOURNAL OF RADIOTHERAPY IN PRACTICE 2021. [DOI: 10.1017/s1460396921000388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Abstract
Aim:
The aim of this systematic review was to synthesise and summarise evidence surrounding the clinical use of fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT) and magnetic resonance imaging (MRI) for target volume delineation (TVD) in rectal cancer radiotherapy planning.
Methods:
PubMed, EMBASE, Cochrane library, CINAHL, Web of Science and Scopus databases and other sources were systematically queried using keywords and relevant synonyms. Eligible full-text studies were assessed for methodological quality using the QUADAS-2 tool.
Results:
Eight of the 1448 studies identified met the inclusion criteria. Findings showed that MRI significantly delineate larger tumour volumes (TVs) than FDG-PET/CT while diffusion-weighted magnetic resonance imaging (DW-MRI) defined smaller gross tumour volumes (GTVs) compared to T2 weighted-Magnetic Resonance Image. CT-based GTVs were found to be larger compared to FDG-PET/CT. FDG-PET/CT also identified new lesions in 15–17% patients and TVs extending outside the routinely used clinical standard CT TV in 29–83% patients. Between observers, delineated volumes were similar and consistent between MRI sequences, whereas interobserver agreement was significantly improved with FDG-PET/CT than CT.
Conclusion:
FDG-PET/CT and DW-MRI appear to delineate smaller rectal TVs and show improved interobserver variability. Overall, this study provides valuable insights into the amount of attention in the research literature that has been paid to imaging for TVD in rectal cancer.
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Min L, Gu Y, Xue R, Ren Y, Gao B. Composite MRI Task Construction from CT Images based on Deep Convolution Neural Network. J Imaging Sci Technol 2021. [DOI: 10.2352/j.imagingsci.technol.2021.65.3.030404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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17
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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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18
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Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer. Int J Radiat Oncol Biol Phys 2020; 108:1329-1338. [DOI: 10.1016/j.ijrobp.2020.07.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 06/03/2020] [Accepted: 07/10/2020] [Indexed: 11/23/2022]
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Yang H, Sun J, Carass A, Zhao C, Lee J, Prince JL, Xu Z. Unsupervised MR-to-CT Synthesis Using Structure-Constrained CycleGAN. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4249-4261. [PMID: 32780700 DOI: 10.1109/tmi.2020.3015379] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Synthesizing a CT image from an available MR image has recently emerged as a key goal in radiotherapy treatment planning for cancer patients. CycleGANs have achieved promising results on unsupervised MR-to-CT image synthesis; however, because they have no direct constraints between input and synthetic images, cycleGANs do not guarantee structural consistency between these two images. This means that anatomical geometry can be shifted in the synthetic CT images, clearly a highly undesirable outcome in the given application. In this paper, we propose a structure-constrained cycleGAN for unsupervised MR-to-CT synthesis by defining an extra structure-consistency loss based on the modality independent neighborhood descriptor. We also utilize a spectral normalization technique to stabilize the training process and a self-attention module to model the long-range spatial dependencies in the synthetic images. Results on unpaired brain and abdomen MR-to-CT image synthesis show that our method produces better synthetic CT images in both accuracy and visual quality as compared to other unsupervised synthesis methods. We also show that an approximate affine pre-registration for unpaired training data can improve synthesis results.
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20
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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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21
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Thureau S, Briens A, Decazes P, Castelli J, Barateau A, Garcia R, Thariat J, de Crevoisier R. PET and MRI guided adaptive radiotherapy: Rational, feasibility and benefit. Cancer Radiother 2020; 24:635-644. [PMID: 32859466 DOI: 10.1016/j.canrad.2020.06.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023]
Abstract
Adaptive radiotherapy (ART) corresponds to various replanning strategies aiming to correct for anatomical variations occurring during the course of radiotherapy. The goal of the article was to report the rational, feasibility and benefit of using PET and/or MRI to guide this ART strategy in various tumor localizations. The anatomical modifications defined by scanner taking into account tumour mobility and volume variation are not always sufficient to optimise treatment. The contribution of functional imaging by PET or the precision of soft tissue by MRI makes it possible to consider optimized ART. Today, the most important data for both PET and MRI are for lung, head and neck, cervical and prostate cancers. PET and MRI guided ART appears feasible and safe, however in a very limited clinical experience. Phase I/II studies should be therefore performed, before proposing cost-effectiveness comparisons in randomized trials and before using the approach in routine practice.
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Affiliation(s)
- S Thureau
- Département de radiothérapie et de physique médicale, centre Henri-Becquerel, QuantIF EA 4108, université de Rouen, 76000 Rouen, France.
| | - A Briens
- Département de radiothérapie, centre Eugène-Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France
| | - P Decazes
- Département de médecine nucléaire, center Henri-Becquerel, QuantIF EA 4108, université de Rouen, Rouen, France
| | - J Castelli
- Département de radiothérapie, centre Eugène Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
| | - A Barateau
- Département de radiothérapie, centre Eugène Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
| | - R Garcia
- Service de physique médicale, institut Sainte-Catherine, 84918 Avignon, France
| | - J Thariat
- Department of radiation oncology, centre François-Baclesse, 14000 Caen, France; Laboratoire de physique corpusculaire IN2P3/ENSICAEN-UMR6534-Unicaen-Normandie université, 14000 Caen, France; ARCHADE Research Community, 14000 Caen, France
| | - R de Crevoisier
- Département de radiothérapie, centre Eugène-Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
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22
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Head-and-Neck MRI-only radiotherapy treatment planning: From acquisition in treatment position to pseudo-CT generation. Cancer Radiother 2020; 24:288-297. [DOI: 10.1016/j.canrad.2020.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/08/2020] [Accepted: 01/15/2020] [Indexed: 12/25/2022]
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23
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Kisivan K, Antal G, Gulyban A, Glavak C, Laszlo Z, Kalincsak J, Gugyeras D, Jenei T, Csima M, Lakosi F. Triggered Imaging With Auto Beam Hold and Pre-/Posttreatment CBCT During Prostate SABR: Analysis of Time Efficiency, Target Coverage, and Normal Volume Changes. Pract Radiat Oncol 2020; 11:e210-e218. [PMID: 32454177 DOI: 10.1016/j.prro.2020.04.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 04/17/2020] [Accepted: 04/24/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE Our purpose was to investigate time efficiency and target coverage for prostate stereotactic ablative radiation therapy (SABR) using triggered imaging (TI) and auto beam hold. METHODS AND MATERIALS A total of 20 patients were treated with volumetric modulated arc-based SABR. Treatment verification consisted of pre- and post-radiation therapy cone beam computed tomography (CBCT) with gold marker-based TI every 3 seconds. In case of ≥3 mm (deviation limit) displacement, the treatment was interrupted and imaging-based correction was performed. Beam interruptions, intrafractional shifts, and treatment times were recorded. Prostate, rectum, and bladder were delineated on each CBCT. Target coverage was evaluated by comparing the individual prostate delineations with 98% isodose contour volumes (% of the evaluated volumes exceeding the reference). Both inter- and intrafractional changes of bladder and rectal volumes were assessed. RESULTS The average overall treatment time (±standard deviation) was 18 ± 11 min, with a radiation delivery time of 6 ± 3 min if no intrafractional CBCT acquisitions were necessary (91% of fractions). On average, 1.2 beam interruptions per fraction were required with 0/1 correction in 71% of the fractions. The mean residual 3-dimensional shift was 1.6 mm, exceeding the deviation limit in 8%. In the case of intrafractional CBCT and/or ≥2 corrections the treatment time dramatically increased. The 98% isodose lines did not encompass the prostate in only 8/180 (4%) evaluations in 6 different patients, leading to a loss of D98 between 0.1%-6% as a worst case scenario. The bladder volumes showed significant increases during treatment (P < .01) while rectal volumes were stable. CONCLUSIONS Time efficiency of TI + auto beam hold with 3 mm/3 sec threshold during prostate SABR is comparable with competitive techniques, resulting in minimal 3-dimensional residual errors with maintained target coverage. Technical developments are necessary to further reduce radiation delivery time. Use of CBCT allowed full control of rectal volumes, while bladder volumes showed significant increases over time.
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Affiliation(s)
- Katalin Kisivan
- Department of Radiation Oncology, Somogy County Mor Kaposi Teaching Hospital, Dr Jozsef Baka Center, Kaposvar, Hungary
| | - Gergely Antal
- Department of Radiation Oncology, Somogy County Mor Kaposi Teaching Hospital, Dr Jozsef Baka Center, Kaposvar, Hungary
| | - Akos Gulyban
- Medical Physics Department, Institut Jules Bordet, Bruxelles, Belgium
| | - Csaba Glavak
- Department of Radiation Oncology, Somogy County Mor Kaposi Teaching Hospital, Dr Jozsef Baka Center, Kaposvar, Hungary
| | - Zoltan Laszlo
- Department of Radiation Oncology, Somogy County Mor Kaposi Teaching Hospital, Dr Jozsef Baka Center, Kaposvar, Hungary
| | - Judit Kalincsak
- Department of Radiation Oncology, Somogy County Mor Kaposi Teaching Hospital, Dr Jozsef Baka Center, Kaposvar, Hungary
| | - Daniel Gugyeras
- Department of Radiation Oncology, Somogy County Mor Kaposi Teaching Hospital, Dr Jozsef Baka Center, Kaposvar, Hungary
| | - Tibor Jenei
- Department of Urology, Somogy County Mor Kaposi Teaching Hospital, Kaposvar, Hungary
| | - Melinda Csima
- Faculty of Pedagogy, Kaposvar University, Kaposvar, Hungary
| | - Ferenc Lakosi
- Department of Radiation Oncology, Somogy County Mor Kaposi Teaching Hospital, Dr Jozsef Baka Center, Kaposvar, Hungary.
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Fetty L, Löfstedt T, Heilemann G, Furtado H, Nesvacil N, Nyholm T, Georg D, Kuess P. Investigating conditional GAN performance with different generator architectures, an ensemble model, and different MR scanners for MR-sCT conversion. Phys Med Biol 2020; 65:105004. [PMID: 32235074 DOI: 10.1088/1361-6560/ab857b] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional generative adversarial networks (cGANs). However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features. In this study, we explored the generalizability of the generator models, trained on a single field strength scanner, to data acquired with higher field strengths. T2-weighted 0.35T MRIs and CTs from 51 patients treated for prostate (40) and cervical cancer (11) were included. 25 of them were used to train four different generators (SE-ResNet, DenseNet, U-Net, and Embedded Net). Further, an ensemble model was created from the four network outputs. The models were validated on 16 patients from a 0.35T MR scanner. Further, the trained models were tested on the Gold Atlas dataset, containing T2-weighted MR scans of different field strengths; 1.5T(7) and 3T(12), and 10 patients from the 0.35T scanner. The sCTs were dosimetrically compared using clinical VMAT plans for all test patients. For the same scanner (0.35T), the results from the different models were comparable on the test set, with only minor differences in the mean absolute error (MAE) (35-51HU body). Similar results were obtained for conversions of 3T GE Signa and the 3T GE Discovery images (40-62HU MAE) for three of the models. However, larger differences were observed for the 1.5T images (48-65HU MAE). The overall best model was found to be the ensemble model. All dose differences were below 1%. This study shows that it is possible to generalize models trained on images of one scanner to other scanners and different field strengths. The best metric results were achieved by the combination of all networks.
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Affiliation(s)
- Lukas Fetty
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria. Medical Imaging Cluster, Medical University of Vienna, Vienna , Austria
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25
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Hu Y, Zhang L. MRI-only Radiation Therapy: Pseudo-CT Based on Cubic-Feature Extraction and Alternative Regression Forest. INT J PATTERN RECOGN 2020. [DOI: 10.1142/s0218001420540336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Despite the extensive attention attracted by magnetic resonance imaging (MRI) in the radiation therapy, computed tomography was reintroduced by the researchers. During the calculation process of the 3D dose distribution of tissues, there were some arguments about the electron density information obtained from the CT scan. However, the CT-provided bones are accurate for constructing a radiograph. Recently, the advantages boosted by the soft tissue contrast relying on MRI and as well as the advantages boosted by CT imaging have been combined by the using of MRI/CT. Unfortunately, disadvantages still exist in the MRI/CT workflow because the voxel-intensities are unbalanced in the MRI and the CT scan. Here, based on the mapping method of CT and MRI, the potential of pseudo-CT (PCT) instead of CT planning was studied. The estimated PCT only from the corresponding MRI was obtained by using the patch-based random forest regression. The CT voxel target was trained by 3D Gabor feature in the MRI cube and the Local Binary Pattern (LBP). Besides, the regression task was solved by the alternative regression forest. According to the experiment, the method performs better than the current dictionary learning-based (DLB) method or atlas-based (AB) method.
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Affiliation(s)
- Yongsheng Hu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, P. R. China
- School of Information Engineering, Binzhou University, Shandong, P. R. China
| | - Liyi Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, P. R. China
- School of Information Engineering, Tianjin University of Commerce, Tianjin, P. R. China
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26
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Kuisma A, Ranta I, Keyriläinen J, Suilamo S, Wright P, Pesola M, Warner L, Löyttyniemi E, Minn H. Validation of automated magnetic resonance image segmentation for radiation therapy planning in prostate cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2020; 13:14-20. [PMID: 33458302 PMCID: PMC7807774 DOI: 10.1016/j.phro.2020.02.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 12/23/2019] [Accepted: 02/24/2020] [Indexed: 01/06/2023]
Abstract
Background and purpose Magnetic resonance imaging (MRI) is increasingly used in radiation therapy planning of prostate cancer (PC) to reduce target volume delineation uncertainty. This study aimed to assess and validate the performance of a fully automated segmentation tool (AST) in MRI based radiation therapy planning of PC. Material and methods Pelvic structures of 65 PC patients delineated in an MRI-only workflow according to established guidelines were included in the analysis. Automatic vs manual segmentation by an experienced oncologist was compared with geometrical parameters, such as the dice similarity coefficient (DSC). Fifteen patients had a second MRI within 15 days to assess repeatability of the AST for prostate and seminal vesicles. Furthermore, we investigated whether hormonal therapy or body mass index (BMI) affected the AST results. Results The AST showed high agreement with manual segmentation expressed as DSC (mean, SD) for delineating prostate (0.84, 0.04), bladder (0.92, 0.04) and rectum (0.86, 0.04). For seminal vesicles (0.56, 0.17) and penile bulb (0.69, 0.12) the respective agreement was moderate. Performance of AST was not influenced by neoadjuvant hormonal therapy, although those on treatment had significantly smaller prostates than the hormone-naïve patients (p < 0.0001). In repeat assessment, consistency of prostate delineation resulted in mean DSC of 0.89, (SD 0.03) between the paired MRI scans for AST, while mean DSC of manual delineation was 0.82, (SD 0.05). Conclusion Fully automated MRI segmentation tool showed good agreement and repeatability compared with manual segmentation and was found clinically robust in patients with PC. However, manual review and adjustment of some structures in individual cases remain important in clinical use.
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Affiliation(s)
- Anna Kuisma
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
| | - Iiro Ranta
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland.,University of Turku, Department of Physics and Astronomy, Vesilinnantie 5, FI-20014 University of Turku, Finland
| | - Jani Keyriläinen
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland.,University of Turku, Department of Physics and Astronomy, Vesilinnantie 5, FI-20014 University of Turku, Finland
| | - Sami Suilamo
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
| | - Pauliina Wright
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
| | - Marko Pesola
- Philips MR Therapy Oy, Äyritie 4, FI-01510 Vantaa, Finland
| | - Lizette Warner
- Philips MR Oncology, 3000 Minuteman Road, Andover, MA 01810, United States
| | - Eliisa Löyttyniemi
- University of Turku, Department of Biostatistics, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland
| | - Heikki Minn
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
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Koike Y, Akino Y, Sumida I, Shiomi H, Mizuno H, Yagi M, Isohashi F, Seo Y, Suzuki O, Ogawa K. Feasibility of synthetic computed tomography generated with an adversarial network for multi-sequence magnetic resonance-based brain radiotherapy. JOURNAL OF RADIATION RESEARCH 2020; 61:92-103. [PMID: 31822894 PMCID: PMC6976735 DOI: 10.1093/jrr/rrz063] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 07/15/2019] [Indexed: 06/10/2023]
Abstract
The aim of this work is to generate synthetic computed tomography (sCT) images from multi-sequence magnetic resonance (MR) images using an adversarial network and to assess the feasibility of sCT-based treatment planning for brain radiotherapy. Datasets for 15 patients with glioblastoma were selected and 580 pairs of CT and MR images were used. T1-weighted, T2-weighted and fluid-attenuated inversion recovery MR sequences were combined to create a three-channel image as input data. A conditional generative adversarial network (cGAN) was trained using image patches. The image quality was evaluated using voxel-wise mean absolute errors (MAEs) of the CT number. For the dosimetric evaluation, 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans were generated using the original CT set and recalculated using the sCT images. The isocenter dose and dose-volume parameters were compared for 3D-CRT and VMAT plans, respectively. The equivalent path length was also compared. The mean MAEs for the whole body, soft tissue and bone region were 108.1 ± 24.0, 38.9 ± 10.7 and 366.2 ± 62.0 hounsfield unit, respectively. The dosimetric evaluation revealed no significant difference in the isocenter dose for 3D-CRT plans. The differences in the dose received by 2% of the volume (D2%), D50% and D98% relative to the prescribed dose were <1.0%. The overall equivalent path length was shorter than that for real CT by 0.6 ± 1.9 mm. A treatment planning study using generated sCT detected only small, clinically negligible differences. These findings demonstrated the feasibility of generating sCT images for MR-only radiotherapy from multi-sequence MR images using cGAN.
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Affiliation(s)
- Yuhei Koike
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuichi Akino
- Oncology Center, Osaka University Hospital, Osaka, Japan
| | - Iori Sumida
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hiroya Shiomi
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
- Miyakojima IGRT Clinic, Osaka, Japan
| | - Hirokazu Mizuno
- Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masashi Yagi
- Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Fumiaki Isohashi
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuji Seo
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Osamu Suzuki
- Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka, Japan
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Thapa R, Ahunbay E, Nasief H, Chen X, Allen Li X. Automated air region delineation on MRI for synthetic CT creation. Phys Med Biol 2020; 65:025009. [PMID: 31775128 DOI: 10.1088/1361-6560/ab5c5b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Automatically and accurately separating air from other low signal regions (especially bone, liver, etc) in an MRI is difficult because these tissues produce similar MR intensities, resulting in errors in synthetic CT generation for MRI-based radiation therapy planning. This work aims to develop a technique to accurately and automatically determine air-regions for MR-guided adaptive radiation therapy. CT and MRI scans (T2-weighted) of phantoms with fabricated air-cavities and abdominal cancer patients were used to establish an MR intensity threshold for air delineation. From the phantom data, air/tissue boundaries in MRI were identified by CT-MRI registration. A formula relating the MRI intensities of air and surrounding materials was established to auto-threshold air-regions. The air-regions were further refined by using quantitative image texture features. A naive Bayesian classifier was trained using the extracted features with a leave-one-out cross validation technique to differentiate air from non-air voxels. The multi-step air auto-segmentation method was tested against the manually segmented air-regions. The dosimetry impacts of the air-segmentation methods were studied. Air-regions in the abdomen can be segmented on MRI within 1 mm accuracy using a multi-step auto-segmentation method as compared to manually delineated contours. The air delineation based on the MR threshold formula was improved using the MRI texture differences between air and tissues, as judged by the area under the receiver operating characteristic curve of 81% when two texture features (autocorrelation and contrast) were used. The performance increased to 82% with using three features (autocorrelation, sum-variance, and contrast). Dosimetric analysis showed no significant difference between the auto-segmentation and manual MR air delineation on commonly used dose volume parameters. The proposed techniques consisting of intensity-based auto-thresholding and image texture-based voxel classification can automatically and accurately segment air-regions on MRI, allowing synthetic CT to be generated quickly and precisely for MR-guided adaptive radiation therapy.
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Affiliation(s)
- Ranjeeta Thapa
- Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States of America
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29
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Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, Greer PB, Dowling JA, Baxter J, Saint-Jalmes H, Acosta O, de Crevoisier R. Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning. Int J Radiat Oncol Biol Phys 2019; 105:1137-1150. [DOI: 10.1016/j.ijrobp.2019.08.049] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 08/16/2019] [Accepted: 08/22/2019] [Indexed: 12/25/2022]
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30
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Liu Y, Lei Y, Wang Y, Shafai-Erfani G, Wang T, Tian S, Patel P, Jani AB, McDonald M, Curran WJ, Liu T, Zhou J, Yang X. Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. Phys Med Biol 2019; 64:205022. [PMID: 31487698 DOI: 10.1088/1361-6560/ab41af] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The purpose of this work is to validate the application of a deep learning-based method for pelvic synthetic CT (sCT) generation that can be used for prostate proton beam therapy treatment planning. We propose to integrate dense block minimization into 3D cycle-consistent generative adversarial networks (cycleGAN) framework to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 17 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT generation method by leave-one-out cross-validation. Image quality between the sCT and CT images, gamma analysis passing rate, dose-volume metrics, distal range displacement, and the individual pencil beam Bragg peak shift between sCT- and CT-based proton plans were evaluated. The average mean absolute error (MAE) was 51.32 ± 16.91 HU. The relative differences of the statistics of the PTV dose-volume histogram (DVH) metrics in between sCT and CT were generally less than 1%. Mean values of dose difference, absolute dose difference (in percent of the prescribed dose) were -0.07% ± 0.07% and 0.23% ± 0.08%. Mean gamma analysis pass rate of 1 mm/1%, 2 mm/2%, 3 mm/3% criteria with 10% dose threshold were 92.39% ± 5.97%, 97.95% ± 2.95% and 98.97% ± 1.62% respectively. The median, mean and standard deviation of absolute maximum range differences were 0.09 cm and 0.23 ± 0.25 cm. The median and mean Bragg peak shifts among the 17 patients were 0.09 cm and 0.18 ± 0.07 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for prostate proton radiotherapy.
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Affiliation(s)
- Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
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31
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Fu J, Yang Y, Singhrao K, Ruan D, Chu FI, Low DA, Lewis JH. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging. Med Phys 2019; 46:3788-3798. [PMID: 31220353 DOI: 10.1002/mp.13672] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 06/05/2019] [Accepted: 06/10/2019] [Indexed: 01/17/2023] Open
Abstract
PURPOSE The improved soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) makes it a useful imaging modality for radiotherapy treatment planning. Even when MR images are acquired for treatment planning, the standard clinical practice currently also requires a CT for dose calculation and x-ray-based patient positioning. This increases workloads, introduces uncertainty due to the required inter-modality image registrations, and involves unnecessary irradiation. While it would be beneficial to use exclusively MR images, a method needs to be employed to estimate a synthetic CT (sCT) for generating electron density maps and patient positioning reference images. We investigated 2D and 3D convolutional neural networks (CNNs) to generate a male pelvic sCT using a T1-weighted MR image and compare their performance. METHODS A retrospective study was performed using CTs and T1-weighted MR images of 20 prostate cancer patients. CTs were deformably registered to MR images to create CT-MR pairs for training networks. The proposed 2D CNN, which contained 27 convolutional layers, was modified from the state-of-the-art 2D CNN to save computational memory and prepare for building the 3D CNN. The proposed 2D and 3D models were trained from scratch to map intensities of T1-weighted MR images to CT Hounsfield Unit (HU) values. Each sCT was generated in a fivefold cross-validation framework and compared with the corresponding deformed CT (dCT) using voxel-wise mean absolute error (MAE). The sCT geometric accuracy was evaluated by comparing bone regions, defined by thresholding at 150 HU in the dCTs and the sCTs, using dice similarity coefficient (DSC), recall, and precision. To evaluate sCT patient positioning accuracy, bone regions in dCTs and sCTs were rigidly registered to the corresponding cone-beam CTs. The resulting paired Euler transformation vectors were compared by calculating translation vector distances and absolute differences of Euler angles. Statistical tests were performed to evaluate the differences among the proposed models and Han's model. RESULTS Generating a pelvic sCT required approximately 5.5 s using the proposed models. The average MAEs within the body contour were 40.5 ± 5.4 HU (mean ± SD) and 37.6 ± 5.1 HU for the 2D and 3D CNNs, respectively. The average DSC, recall, and precision for the bone region (thresholding the CT at 150 HU) were 0.81 ± 0.04, 0.85 ± 0.04, and 0.77 ± 0.09 for the 2D CNN, and 0.82 ± 0.04, 0.84 ± 0.04, and 0.80 ± 0.08 for the 3D CNN, respectively. For both models, mean translation vector distances are less than 0.6 mm with mean absolute differences of Euler angles less than 0.5°. CONCLUSIONS The 2D and 3D CNNs generated accurate pelvic sCTs for the 20 patients using T1-weighted MR images. Statistical tests indicated that the proposed 3D model was able to generate sCTs with smaller MAE and higher bone region precision compared to 2D models. Results of patient alignment tests suggested that sCTs generated by the proposed CNNs can provide accurate patient positioning. The accuracy of the dose calculation using generated sCTs will be tested and compared for the proposed models in the future.
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Affiliation(s)
- Jie Fu
- David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Ave, Los Angeles, 90095, CA, USA.,Department of Radiation Oncology, University of California, Los Angeles, 200 Suite B265, Medical Plaza Driveway, Los Angeles, 90095, CA, USA
| | - Yingli Yang
- Department of Radiation Oncology, University of California, Los Angeles, 200 Suite B265, Medical Plaza Driveway, Los Angeles, 90095, CA, USA
| | - Kamal Singhrao
- David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Ave, Los Angeles, 90095, CA, USA.,Department of Radiation Oncology, University of California, Los Angeles, 200 Suite B265, Medical Plaza Driveway, Los Angeles, 90095, CA, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California, Los Angeles, 200 Suite B265, Medical Plaza Driveway, Los Angeles, 90095, CA, USA
| | - Fang-I Chu
- Department of Radiation Oncology, University of California, Los Angeles, 200 Suite B265, Medical Plaza Driveway, Los Angeles, 90095, CA, USA
| | - Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, 200 Suite B265, Medical Plaza Driveway, Los Angeles, 90095, CA, USA
| | - John H Lewis
- Department of Radiation Oncology, University of California, Los Angeles, 200 Suite B265, Medical Plaza Driveway, Los Angeles, 90095, CA, USA
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32
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Liu Y, Lei Y, Wang Y, Wang T, Ren L, Lin L, McDonald M, Curran WJ, Liu T, Zhou J, Yang X. MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method. Phys Med Biol 2019; 64:145015. [PMID: 31146267 PMCID: PMC6635951 DOI: 10.1088/1361-6560/ab25bc] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-only treatment process is currently an active field of research since it could eliminate systematic MR-CT co-registration errors, reduce medical cost, avoid diagnostic radiation exposure, and simplify clinical workflow. The purpose of this work is to validate the application of a deep learning-based method for abdominal synthetic CT (sCT) generation by image evaluation and dosimetric assessment in a commercial proton pencil beam treatment planning system (TPS). This study proposes to integrate dense block into a 3D cycle-consistent generative adversarial networks (cycle GAN) framework in an effort to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 21 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT image quality by leave-one-out cross validation. The CT image quality, dosimetric accuracy and the distal range fidelity were rigorously checked, using side-by-side comparison against the corresponding original CT images. The average mean absolute error (MAE) was 72.87 ± 18.16 HU. The relative differences of the statistics of the PTV dose volume histogram (DVH) metrics between sCT and CT were generally less than 1%. Mean 3D gamma analysis passing rate of 1 mm/1%, 2 mm/2%, 3 mm/3% criteria with 10% dose threshold were 90.76% ± 5.94%, 96.98% ± 2.93% and 99.37% ± 0.99%, respectively. The median, mean and standard deviation of absolute maximum range differences were 0.170 cm, 0.186 cm and 0.155 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for liver proton radiotherapy.
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Affiliation(s)
- Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Yinan Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Lei Ren
- Department of Radiation Oncology, Duke University, Durham, NC 27708
| | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Mark McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322
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Dinkla AM, Florkow MC, Maspero M, Savenije MHF, Zijlstra F, Doornaert PAH, Stralen M, Philippens MEP, Berg CAT, Seevinck PR. Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network. Med Phys 2019; 46:4095-4104. [DOI: 10.1002/mp.13663] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 05/15/2019] [Accepted: 06/10/2019] [Indexed: 12/21/2022] Open
Affiliation(s)
- Anna M. Dinkla
- Department of Radiotherapy, Division of Imaging & Oncology University Medical Center Utrecht Utrecht The Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
| | - Mateusz C. Florkow
- Centre for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
| | - Matteo Maspero
- Department of Radiotherapy, Division of Imaging & Oncology University Medical Center Utrecht Utrecht The Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
| | - Mark H. F. Savenije
- Department of Radiotherapy, Division of Imaging & Oncology University Medical Center Utrecht Utrecht The Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
| | - Frank Zijlstra
- Centre for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
| | - Patricia A. H. Doornaert
- Department of Radiotherapy, Division of Imaging & Oncology University Medical Center Utrecht Utrecht The Netherlands
| | - Marijn Stralen
- Centre for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
- MRIguidance B.V Utrecht The Netherlands
| | - Marielle E. P. Philippens
- Department of Radiotherapy, Division of Imaging & Oncology University Medical Center Utrecht Utrecht The Netherlands
| | - Cornelis A. T. Berg
- Department of Radiotherapy, Division of Imaging & Oncology University Medical Center Utrecht Utrecht The Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
| | - Peter R. Seevinck
- Centre for Image Sciences University Medical Center Utrecht Utrecht The Netherlands
- MRIguidance B.V Utrecht The Netherlands
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[Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging]. Cancer Radiother 2019; 23:281-289. [PMID: 31151816 DOI: 10.1016/j.canrad.2018.09.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 09/10/2018] [Accepted: 09/27/2018] [Indexed: 11/21/2022]
Abstract
PURPOSE Magnetic resonance imaging (MRI) plays an increasing role in radiotherapy dose planning. Indeed, MRI offers superior soft tissue contrast compared to computerized tomography (CT) and therefore could provide a better delineation of target volumes and organs at risk than CT for radiotherapy. Furthermore, an MRI-only radiotherapy workflow would suppress registration errors inherent to the registration of MRI with CT. However, the estimation of the electronic density of tissues using MRI images is still a challenging issue. The purpose of this work was to design and evaluate a pseudo-CT generation method for prostate cancer treatments. MATERIALS AND METHODS A pseudo-CT was generated for ten prostate cancer patients using an elastic deformation based method. For each patient, dose delivered to the patient was calculated using both the planning CT and the pseudo-CT. Dose differences between CT and pseudo-CT were investigated. RESULTS Mean dose relative difference in the planning target volume is 0.9% on average and ranges from 0.1% to 1.7%. In organs at risks, this value is 1.8%, 0.8%, 0.8% and 1% on average in the rectum, the right and left femoral heads, and the bladder respectively. CONCLUSION The dose calculated using the pseudo-CT is very close to the dose calculated using the CT for both organs at risk and PTV. These results confirm that pseudo-CT images generated using the proposed method could be used to calculate radiotherapy treatment doses on MRI images.
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Liu Y, Lei Y, Wang T, Kayode O, Tian S, Liu T, Patel P, Curran WJ, Ren L, Yang X. MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method. Br J Radiol 2019; 92:20190067. [PMID: 31192695 DOI: 10.1259/bjr.20190067] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE The purpose of this work is to develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning. METHODS We proposed to integrate dense block into cycle generative adversarial network (GAN) to effectively capture the relationship between the CT and MRI for CT synthesis. A cohort of 21 patients with co-registered CT and MR pairs were used to evaluate our proposed method by the leave-one-out cross-validation. Mean absolute error, peak signal-to-noise ratio and normalized cross-correlation were used to quantify the imaging differences between the synthetic CT (sCT) and CT. The accuracy of Hounsfield unit (HU) values in sCT for dose calculation was evaluated by comparing the dose distribution in sCT-based and CT-based treatment planning. Clinically relevant dose-volume histogram metrics were then extracted from the sCT-based and CT-based plans for quantitative comparison. RESULTS The mean absolute error, peak signal-to-noise ratio and normalized cross-correlation of the sCT were 72.87 ± 18.16 HU, 22.65 ± 3.63 dB and 0.92 ± 0.04, respectively. No significant differences were observed in the majority of the planning target volume and organ at risk dose-volume histogram metrics ( p > 0.05). The average pass rate of γ analysis was over 99% with 1%/1 mm acceptance criteria on the coronal plane that intersects with isocenter. CONCLUSION The image similarity and dosimetric agreement between sCT and original CT warrant further development of an MRI-only workflow for liver stereotactic body radiation therapy. ADVANCES IN KNOWLEDGE This work is the first deep-learning-based approach to generating abdominal sCT through dense-cycle-GAN. This method can successfully generate the small bony structures such as the rib bones and is able to predict the HU values for dose calculation with comparable accuracy to reference CT images.
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Affiliation(s)
- Yingzi Liu
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Yang Lei
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Tonghe Wang
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Oluwatosin Kayode
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Sibo Tian
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Tian Liu
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Pretesh Patel
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Walter J Curran
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Lei Ren
- 2 Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Xiaofeng Yang
- 1 Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
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Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. Br J Radiol 2019; 92:20190001. [PMID: 31112393 PMCID: PMC6724618 DOI: 10.1259/bjr.20190001] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning interest in machine learning comes the significant risk of misaligned expectations as to what it can and cannot accomplish. This paper evaluates the role of machine learning and the problems it solves within the context of current clinical challenges in radiation oncology. The role of learning algorithms within the workflow for external beam radiation therapy are surveyed, considering simulation imaging, multimodal fusion, image segmentation, treatment planning, quality assurance, and treatment delivery and adaptation. For each aspect, the clinical challenges faced, the learning algorithms proposed, and the successes and limitations of various approaches are analyzed. It is observed that machine learning has largely thrived on reproducibly mimicking conventional human-driven solutions with more efficiency and consistency. On the other hand, since algorithms are generally trained using expert opinion as ground truth, machine learning is of limited utility where problems or ground truths are not well-defined, or if suitable measures of correctness are not available. As a result, machines may excel at replicating, automating and standardizing human behaviour on manual chores, meanwhile the conceptual clinical challenges relating to definition, evaluation, and judgement remain in the realm of human intelligence and insight.
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Affiliation(s)
- Daniel Jarrett
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK.,2 Mirada Medical Ltd, Oxford, UK
| | - Eleanor Stride
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Katherine Vallis
- 3 Department of Oncology, Oxford Institute for Radiation Oncology, University of Oxford, UK
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Ahunbay EE, Thapa R, Chen X, Paulson E, Li XA. A Technique to Rapidly Generate Synthetic Computed Tomography for Magnetic Resonance Imaging–Guided Online Adaptive Replanning: An Exploratory Study. Int J Radiat Oncol Biol Phys 2019; 103:1261-1270. [DOI: 10.1016/j.ijrobp.2018.12.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 11/18/2018] [Accepted: 12/05/2018] [Indexed: 12/22/2022]
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Guerreiro F, Koivula L, Seravalli E, Janssens GO, Maduro JH, Brouwer CL, Korevaar EW, Knopf AC, Korhonen J, Raaymakers BW. Feasibility of MRI-only photon and proton dose calculations for pediatric patients with abdominal tumors. Phys Med Biol 2019; 64:055010. [PMID: 30669135 DOI: 10.1088/1361-6560/ab0095] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The purpose of this study was to develop a method enabling synthetic computed tomography (sCT) generation of the whole abdomen using magnetic resonance imaging (MRI) scans of pediatric patients with abdominal tumors. The proposed method relies on an automatic atlas-based segmentation of bone and lungs followed by an MRI intensity to synthetic Hounsfield unit conversion. Separate conversion algorithms were used for bone, lungs and soft-tissue. Rigidly registered CT and T2-weighted MR images of 30 patients in treatment position and with the same field of view were used for the evaluation of the atlas and the conversion algorithms. The dose calculation accuracy of the generated sCTs was verified for volumetric modulated arc therapy (VMAT) and pencil beam scanning (PBS). VMAT and PBS plans were robust optimized on an internal target volume (ITV) against a patient set-up uncertainty of 5 mm. Average differences between CT and sCT dose calculations for the ITV V 95% were 0.5% (min 0.0%; max 5.0%) and 0.0% (min -0.1%; max 0.1%) for VMAT and PBS dose distributions, respectively. Average differences for the mean dose to the organs at risk were <0.2% (min -0.6%; max 1.2%) and <0.2% (min -2.0%; max 2.6%) for VMAT and PBS dose distributions, respectively. Results show that MRI-only photon and proton dose calculations are feasible for children with abdominal tumors.
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Affiliation(s)
- Filipa Guerreiro
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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39
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Arabi H, Dowling JA, Burgos N, Han X, Greer PB, Koutsouvelis N, Zaidi H. Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region. Med Phys 2018; 45:5218-5233. [PMID: 30216462 DOI: 10.1002/mp.13187] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 07/29/2018] [Accepted: 09/06/2018] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI)-guided radiation therapy (RT) treatment planning is limited by the fact that the electron density distribution required for dose calculation is not readily provided by MR imaging. We compare a selection of novel synthetic CT generation algorithms recently reported in the literature, including segmentation-based, atlas-based and machine learning techniques, using the same cohort of patients and quantitative evaluation metrics. METHODS Six MRI-guided synthetic CT generation algorithms were evaluated: one segmentation technique into a single tissue class (water-only), four atlas-based techniques, namely, median value of atlas images (ALMedian), atlas-based local weighted voting (ALWV), bone enhanced atlas-based local weighted voting (ALWV-Bone), iterative atlas-based local weighted voting (ALWV-Iter), and a machine learning technique using deep convolution neural network (DCNN). RESULTS Organ auto-contouring from MR images was evaluated for bladder, rectum, bones, and body boundary. Overall, DCNN exhibited higher segmentation accuracy resulting in Dice indices (DSC) of 0.93 ± 0.17, 0.90 ± 0.04, and 0.93 ± 0.02 for bladder, rectum, and bones, respectively. On the other hand, ALMedian showed the lowest accuracy with DSC of 0.82 ± 0.20, 0.81 ± 0.08, and 0.88 ± 0.04, respectively. DCNN reached the best performance in terms of accurate derivation of synthetic CT values within each organ, with a mean absolute error within the body contour of 32.7 ± 7.9 HU, followed by the advanced atlas-based methods (ALWV: 40.5 ± 8.2 HU, ALWV-Iter: 42.4 ± 8.1 HU, ALWV-Bone: 44.0 ± 8.9 HU). ALMedian led to the highest error (52.1 ± 11.1 HU). Considering the dosimetric evaluation results, ALWV-Iter, ALWV, DCNN and ALWV-Bone led to similar mean dose estimation within each organ at risk and target volume with less than 1% dose discrepancy. However, the two-dimensional gamma analysis demonstrated higher pass rates for ALWV-Bone, DCNN, ALMedian and ALWV-Iter at 1%/1 mm criterion with 94.99 ± 5.15%, 94.59 ± 5.65%, 93.68 ± 5.53% and 93.10 ± 5.99% success, respectively, while ALWV and water-only resulted in 86.91 ± 13.50% and 80.77 ± 12.10%, respectively. CONCLUSIONS Overall, machine learning and advanced atlas-based methods exhibited promising performance by achieving reliable organ segmentation and synthetic CT generation. DCNN appears to have slightly better performance by achieving accurate automated organ segmentation and relatively small dosimetric errors (followed closely by advanced atlas-based methods, which in some cases achieved similar performance). However, the DCNN approach showed higher vulnerability to anatomical variation, where a greater number of outliers was observed with this method. Considering the dosimetric results obtained from the evaluated methods, the challenge of electron density estimation from MR images can be resolved with a clinically tolerable error.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, CH-1211, Switzerland
| | - Jason A Dowling
- CSIRO Australian e-Health Research Centre, Herston, QLD, Australia
| | - Ninon Burgos
- Inria Paris, Aramis Project-Team, Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, F-75013, France
| | - Xiao Han
- Elekta Inc., Maryland Heights, MO, 63043, USA
| | - Peter B Greer
- Calvary Mater Newcastle Hospital, Waratah, NSW, Australia.,University of Newcastle, Callaghan, NSW, Australia
| | - Nikolaos Koutsouvelis
- Division of Radiation Oncology, Geneva University Hospital, Geneva, CH-1211, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, CH-1211, Switzerland.,Geneva University Neurocenter, University of Geneva, Geneva, 1205, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, the Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, DK-500, Denmark
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40
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Kerkmeijer LGW, Maspero M, Meijer GJ, van der Voort van Zyp JRN, de Boer HCJ, van den Berg CAT. Magnetic Resonance Imaging only Workflow for Radiotherapy Simulation and Planning in Prostate Cancer. Clin Oncol (R Coll Radiol) 2018; 30:692-701. [PMID: 30244830 DOI: 10.1016/j.clon.2018.08.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 06/29/2018] [Accepted: 08/21/2018] [Indexed: 01/06/2023]
Abstract
Magnetic resonance imaging (MRI) is often combined with computed tomography (CT) in prostate radiotherapy to optimise delineation of the target and organs-at-risk (OAR) while maintaining accurate dose calculation. Such a dual-modality workflow requires two separate imaging sessions, and it has some fundamental and logistical drawbacks. Due to the availability of new MRI hardware and software solutions, CT examinations can be omitted for prostate radiotherapy simulations. All information for treatment planning, including electron density maps and bony anatomy, can nowadays be obtained with MRI. Such an MRI-only simulation workflow reduces delineation ambiguities, eases planning logistics, and improves patient comfort; however, careful validation of the complete MRI-only workflow is warranted. The first institutes are now adopting this MRI-only workflow for prostate radiotherapy. In this article, we will review technology and workflow requirements for an MRI-only prostate simulation workflow.
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Affiliation(s)
- L G W Kerkmeijer
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands.
| | - M Maspero
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
| | - G J Meijer
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
| | | | - H C J de Boer
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
| | - C A T van den Berg
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
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41
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Chandarana H, Wang H, Tijssen RHN, Das IJ. Emerging role of MRI in radiation therapy. J Magn Reson Imaging 2018; 48:1468-1478. [PMID: 30194794 DOI: 10.1002/jmri.26271] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 07/08/2018] [Accepted: 07/09/2018] [Indexed: 12/12/2022] Open
Abstract
Advances in multimodality imaging, providing accurate information of the irradiated target volume and the adjacent critical structures or organs at risk (OAR), has made significant improvements in delivery of the external beam radiation dose. Radiation therapy conventionally has used computed tomography (CT) imaging for treatment planning and dose delivery. However, magnetic resonance imaging (MRI) provides unique advantages: added contrast information that can improve segmentation of the areas of interest, motion information that can help to better target and deliver radiation therapy, and posttreatment outcome analysis to better understand the biologic effect of radiation. To take advantage of these and other potential advantages of MRI in radiation therapy, radiologists and MRI physicists will need to understand the current radiation therapy workflow and speak the same language as our radiation therapy colleagues. This review article highlights the emerging role of MRI in radiation dose planning and delivery, but more so for MR-only treatment planning and delivery. Some of the areas of interest and challenges in implementing MRI in radiation therapy workflow are also briefly discussed. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1468-1478.
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Affiliation(s)
- Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hesheng Wang
- Department of Radiation Oncology, New York University School of Medicine & Laura and Isaac Perlmutter Cancer Center, New York, New York, USA
| | - R H N Tijssen
- Department of Radiotherapy, University Medical Center Utrecht, the Netherlands
| | - Indra J Das
- Department of Radiation Oncology, New York University School of Medicine & Laura and Isaac Perlmutter Cancer Center, New York, New York, USA
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42
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Assessment of electron density effects on dose calculation and optimisation accuracy for nasopharynx, for MRI only treatment planning. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:811-820. [DOI: 10.1007/s13246-018-0675-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 08/14/2018] [Indexed: 12/25/2022]
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43
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The technological basis for adaptive ion beam therapy at MedAustron: Status and outlook. Z Med Phys 2018; 28:196-210. [DOI: 10.1016/j.zemedi.2017.09.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 09/02/2017] [Accepted: 09/18/2017] [Indexed: 11/22/2022]
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44
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Harris W, Wang C, Yin FF, Cai J, Ren L. A Novel method to generate on-board 4D MRI using prior 4D MRI and on-board kV projections from a conventional LINAC for target localization in liver SBRT. Med Phys 2018; 45:3238-3245. [PMID: 29799620 DOI: 10.1002/mp.12998] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 04/10/2018] [Accepted: 05/21/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE On-board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on-board MRI in clinics is extremely limited. On the contrary, on-board kV imaging systems are widely available on radiotherapy machines, but its capability to localize tumors in soft tissue is limited due to its poor soft tissue contrast. This study aims to explore the feasibility of using an on-board kV imaging system and patient prior knowledge to generate on-board four-dimensional (4D)-MRI for target localization in liver SBRT. METHODS Prior 4D MRI volumes were separated into end of expiration (EOE) phase (MRIprior ) and all other phases. MRIprior was used to generate a synthetic CT at EOE phase (sCTprior ). On-board 4D MRI at each respiratory phase was considered a deformation of MRIprior . The deformation field map (DFM) was estimated by matching DRRs of the deformed sCTprior to on-board kV projections using a motion modeling and free-form deformation optimization algorithm. The on-board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated on-board 4D MRI was quantitatively evaluated using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center of Mass Shift (COMS). Effects of scan angle and number of projections were also evaluated. RESULTS In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16 ± 1.31%/0.95 ± 0.01/0.88 ± 0.15 mm using orthogonal-view 30° scan angles with 102 projections. The on-board 4D MRI method was robust against the various scan angles and projection numbers evaluated. In the patient study, estimated on-board 4D MRI was generated successfully when compared to the "reference on-board 4D MRI" for the liver patient case. CONCLUSIONS A method was developed to generate on-board 4D MRI using prior 4D MRI and on-board limited kV projections. Preliminary results demonstrated the potential for MRI-based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC.
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Affiliation(s)
- Wendy Harris
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC, 27705, USA
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, USA
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC, 27705, USA.,Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, USA.,Medical Physics Graduate Program, Duke Kunshan University, 8 Duke Avenue, Kunshan, Jiangsu, 215316, China
| | - Jing Cai
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC, 27705, USA.,Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, USA.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong
| | - Lei Ren
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC, 27705, USA.,Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, USA
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45
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Pathmanathan AU, van As NJ, Kerkmeijer LGW, Christodouleas J, Lawton CAF, Vesprini D, van der Heide UA, Frank SJ, Nill S, Oelfke U, van Herk M, Li XA, Mittauer K, Ritter M, Choudhury A, Tree AC. Magnetic Resonance Imaging-Guided Adaptive Radiation Therapy: A "Game Changer" for Prostate Treatment? Int J Radiat Oncol Biol Phys 2018; 100:361-373. [PMID: 29353654 DOI: 10.1016/j.ijrobp.2017.10.020] [Citation(s) in RCA: 118] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 10/09/2017] [Accepted: 10/12/2017] [Indexed: 01/25/2023]
Abstract
Radiation therapy to the prostate involves increasingly sophisticated delivery techniques and changing fractionation schedules. With a low estimated α/β ratio, a larger dose per fraction would be beneficial, with moderate fractionation schedules rapidly becoming a standard of care. The integration of a magnetic resonance imaging (MRI) scanner and linear accelerator allows for accurate soft tissue tracking with the capacity to replan for the anatomy of the day. Extreme hypofractionation schedules become a possibility using the potentially automated steps of autosegmentation, MRI-only workflow, and real-time adaptive planning. The present report reviews the steps involved in hypofractionated adaptive MRI-guided prostate radiation therapy and addresses the challenges for implementation.
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Affiliation(s)
- Angela U Pathmanathan
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | - Nicholas J van As
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | | | | | | | - Danny Vesprini
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Steven J Frank
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Simeon Nill
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | - Uwe Oelfke
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | - Marcel van Herk
- Manchester Cancer Research Centre, University of Manchester, Manchester Academic Health Science Centre, The Christie National Health Service Foundation Trust, Manchester, United Kingdom; National Institute of Health Research, Manchester Biomedical Research Centre, Central Manchester University Hospitals National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - X Allen Li
- Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kathryn Mittauer
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Mark Ritter
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Ananya Choudhury
- Manchester Cancer Research Centre, University of Manchester, Manchester Academic Health Science Centre, The Christie National Health Service Foundation Trust, Manchester, United Kingdom; National Institute of Health Research, Manchester Biomedical Research Centre, Central Manchester University Hospitals National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.
| | - Alison C Tree
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
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46
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Hofmaier J, Haehnle J, Kurz C, Landry G, Maihoefer C, Schüttrumpf L, Süss P, Teichert K, Söhn M, Spahr N, Brachmann C, Weiler F, Thieke C, Küfer KH, Belka C, Parodi K, Kamp F. Multi-criterial patient positioning based on dose recalculation on scatter-corrected CBCT images. Radiother Oncol 2017; 125:464-469. [DOI: 10.1016/j.radonc.2017.09.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/18/2017] [Accepted: 09/19/2017] [Indexed: 10/18/2022]
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47
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Tanaka O, Komeda H, Hirose S, Taniguchi T, Ono K, Matsuo M. Visibility of an iron-containing fiducial marker in magnetic resonance imaging for high-precision external beam prostate radiotherapy. Asia Pac J Clin Oncol 2017; 14:e405-e411. [DOI: 10.1111/ajco.12830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 10/30/2017] [Indexed: 11/30/2022]
Affiliation(s)
- Osamu Tanaka
- Department of Radiation Oncology; Murakami Memorial Hospital; 3-23 Hashimoto-cho Gifu City Gifu Japan
| | - Hisao Komeda
- Department of Urology; Gifu Municipal Hospital; Gifu City Gifu Japan
| | - Shigeki Hirose
- Division of Radiation Service; Gifu Municipal Hospital; Gifu City Gifu Japan
| | - Takuya Taniguchi
- Department of Radiation Oncology; Murakami Memorial Hospital; 3-23 Hashimoto-cho Gifu City Gifu Japan
| | - Kousei Ono
- Department of Radiation Oncology; Murakami Memorial Hospital; 3-23 Hashimoto-cho Gifu City Gifu Japan
| | - Masayuki Matsuo
- Department of Radiology; Gifu University School of Medicine; Gifu City Gifu Japan
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48
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Koivula L, Kapanen M, Seppälä T, Collan J, Dowling JA, Greer PB, Gustafsson C, Gunnlaugsson A, Olsson LE, Wee L, Korhonen J. Intensity-based dual model method for generation of synthetic CT images from standard T2-weighted MR images - Generalized technique for four different MR scanners. Radiother Oncol 2017; 125:411-419. [PMID: 29097012 DOI: 10.1016/j.radonc.2017.10.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Revised: 10/09/2017] [Accepted: 10/10/2017] [Indexed: 11/17/2022]
Abstract
BACKGROUND AND PURPOSE Recent studies have shown that it is possible to conduct entire radiotherapy treatment planning (RTP) workflow using only MR images. This study aims to develop a generalized intensity-based method to generate synthetic CT (sCT) images from standard T2-weighted (T2w) MR images of the pelvis. MATERIALS AND METHODS This study developed a generalized dual model HU conversion method to convert standard T2w MR image intensity values to synthetic HU values, separately inside and outside of atlas-segmented bone volume contour. The method was developed and evaluated with 20 and 35 prostate cancer patients, respectively. MR images with scanning sequences in clinical use were acquired with four different MR scanners of three vendors. RESULTS For the generated synthetic CT (sCT) images of the 35 prostate patients, the mean (and maximal) HU differences in soft and bony tissue volumes were 16 ± 6 HUs (34 HUs) and -46 ± 56 HUs (181 HUs), respectively, against the true CT images. The average of the PTV mean dose difference in sCTs compared to those in true CTs was -0.6 ± 0.4% (-1.3%). CONCLUSIONS The study provides a generalized method for sCT creation from standard T2w images of the pelvis. The method produced clinically acceptable dose calculation results for all the included scanners and MR sequences.
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Affiliation(s)
- Lauri Koivula
- Department of Radiation Oncology, Cancer Center, Helsinki University Central Hospital, Finland; Department of Physics, University of Helsinki, Finland; Medical Imaging and Radiation Therapy, Kymenlaakso Central Hospital, Kymenlaakso Social and Health Services (Carea), Kotka, Finland.
| | - Mika Kapanen
- Department of Medical Physics, Medical Imaging Center, Tampere University Hospital, Finland
| | - Tiina Seppälä
- Department of Radiation Oncology, Cancer Center, Helsinki University Central Hospital, Finland
| | - Juhani Collan
- Department of Radiation Oncology, Cancer Center, Helsinki University Central Hospital, Finland
| | - Jason A Dowling
- CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, Australia
| | - Peter B Greer
- School of Mathematical and Physical Sciences, The University of Newcastle, Australia; Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Australia
| | - Christian Gustafsson
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden; Department of Medical Physics, Lund University, Malmö, Sweden
| | - Adalsteinn Gunnlaugsson
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Lars E Olsson
- Department of Medical Physics, Lund University, Malmö, Sweden; Department of Translational Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Leonard Wee
- MAASTRO Clinic, School of Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Medical Physics, Oncology Services, Vejle Hospital, Denmark
| | - Juha Korhonen
- Department of Radiation Oncology, Cancer Center, Helsinki University Central Hospital, Finland; Medical Imaging and Radiation Therapy, Kymenlaakso Central Hospital, Kymenlaakso Social and Health Services (Carea), Kotka, Finland; Department of Radiology, Helsinki University Central Hospital, Finland; Department of Nuclear Medicine, Helsinki University Central Hospital, Finland
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