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Gao Y, Chang CW, Mandava S, Marants R, Scholey JE, Goette M, Lei Y, Mao H, Bradley JD, Liu T, Zhou J, Sudhyadhom A, Yang X. MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study. Sci Rep 2024; 14:11166. [PMID: 38750148 PMCID: PMC11096170 DOI: 10.1038/s41598-024-61869-8] [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: 06/13/2023] [Accepted: 05/10/2024] [Indexed: 05/18/2024] Open
Abstract
Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.
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Affiliation(s)
- Yuan Gao
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | | | - Raanan Marants
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Jessica E Scholey
- Department of Radiation Oncology, The University of California, San Francisco, CA, 94143, USA
| | - Matthew Goette
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jeffrey D Bradley
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Tian Liu
- Radiation Oncology, Mount Sinai Medical Center, New York, NY, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA.
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2
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Meyer S, Alam S, Kuo L, Hu YC, Liu Y, Lu W, Yorke E, Li A, Cervino L, Zhang P. Creating patient-specific digital phantoms with a longitudinal atlas for evaluating deformable CT-CBCT registration in adaptive lung radiotherapy. Med Phys 2024; 51:1405-1414. [PMID: 37449537 PMCID: PMC10787815 DOI: 10.1002/mp.16606] [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: 10/03/2022] [Revised: 05/26/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Quality assurance of deformable image registration (DIR) is challenging because the ground truth is often unavailable. In addition, current approaches that rely on artificial transformations do not adequately resemble clinical scenarios encountered in adaptive radiotherapy. PURPOSE We developed an atlas-based method to create a variety of patient-specific serial digital phantoms with CBCT-like image quality to assess the DIR performance for longitudinal CBCT imaging data in adaptive lung radiotherapy. METHODS A library of deformations was created by extracting the longitudinal changes observed between a planning CT and weekly CBCT from an atlas of lung radiotherapy patients. The planning CT of an inquiry patient was first deformed by mapping the deformation pattern from a matched atlas patient, and subsequently appended with CBCT artifacts to imitate a weekly CBCT. Finally, a group of digital phantoms around an inquiry patient was produced to simulate a series of possible evolutions of tumor and adjacent normal structures. We validated the generated deformation vector fields (DVFs) to ensure numerically and physiologically realistic transformations. The proposed framework was applied to evaluate the performance of the DIR algorithm implemented in the commercial Eclipse treatment planning system in a retrospective study of eight inquiry patients. RESULTS The generated DVFs were inverse consistent within less than 3 mm and did not exhibit unrealistic folding. The deformation patterns adequately mimicked the observed longitudinal anatomical changes of the matched atlas patients. Worse Eclipse DVF accuracy was observed in regions of low image contrast or artifacts. The structure volumes exhibiting a DVF error magnitude of equal or more than 2 mm ranged from 24.5% (spinal cord) to 69.2% (heart) and the maximum DVF error exceeded 5 mm for all structures except the spinal cord. Contour-based evaluations showed a high degree of alignment with dice similarity coefficients above 0.8 in all cases, which underestimated the overall DVF accuracy within the structures. CONCLUSIONS It is feasible to create and augment digital phantoms based on a particular patient of interest using multiple series of deformation patterns from matched patients in an atlas. This can provide a semi-automated procedure to complement the quality assurance of CT-CBCT DIR and facilitate the clinical implementation of image-guided and adaptive radiotherapy that involve longitudinal CBCT imaging studies.
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Affiliation(s)
- Sebastian Meyer
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sadegh Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - LiCheng Kuo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yilin Liu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ellen Yorke
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Anyi Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Ebadi N, Li R, Das A, Roy A, Nikos P, Najafirad P. CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation. Med Image Anal 2023; 86:102800. [PMID: 37003101 DOI: 10.1016/j.media.2023.102800] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 01/29/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023]
Abstract
Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that incorporates progressive changes in patient anatomy into active plan/dose adaption during the fractionated treatment. However, the clinical application relies on the accurate segmentation of cancer tumors on low-quality on-board images, which has posed challenges for both manual delineation and deep learning-based models. In this paper, we propose a novel sequence transduction deep neural network with an attention mechanism to learn the shrinkage of the cancer tumor based on patients' weekly cone-beam computed tomography (CBCT). We design a self-supervised domain adaption (SDA) method to learn and adapt the rich textural and spatial features from pre-treatment high-quality computed tomography (CT) to CBCT modality in order to address the poor image quality and lack of labels. We also provide uncertainty estimation for sequential segmentation, which aids not only in the risk management of treatment planning but also in the calibration and reliability of the model. Our experimental results based on a clinical non-small cell lung cancer (NSCLC) dataset with sixteen patients and ninety-six longitudinal CBCTs show that our model correctly learns weekly deformation of the tumor over time with an average dice score of 0.92 on the immediate next step, and is able to predict multiple steps (up to 5 weeks) for future patient treatments with an average dice score reduction of 0.05. By incorporating the tumor shrinkage predictions into a weekly re-planning strategy, our proposed method demonstrates a significant decrease in the risk of radiation-induced pneumonitis up to 35% while maintaining the high tumor control probability.
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Affiliation(s)
- Nima Ebadi
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, United States of America.
| | - Ruiqi Li
- Department of Radiation Oncology, UT Health San Antonio, San Antonio, TX 78229, United States of America.
| | - Arun Das
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, United States of America; Department of Medicine, The University of Pittsburgh, Pittsburgh, PA 15260, United States of America.
| | - Arkajyoti Roy
- Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX 78249, United States of America.
| | - Papanikolaou Nikos
- Department of Radiation Oncology, UT Health San Antonio, San Antonio, TX 78229, United States of America.
| | - Peyman Najafirad
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, United States of America.
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Lallement A, Noblet V, Antoni D, Meyer P. Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs). Technol Health Care 2023:THC220519. [PMID: 36776082 DOI: 10.3233/thc-220519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
BACKGROUND Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. OBJECTIVE This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). METHODS The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. RESULTS The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. CONCLUSION The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.
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Affiliation(s)
| | | | - Delphine Antoni
- Department of Radiation Therapy, Institut de Cancérologie de Strasbourg, Strasbourg, France
| | - Philippe Meyer
- ICube-UMR 7357, Strasbourg, France.,Department of Medical Physics, Institut de Cancérologie de Strasbourg, Strasbourg, France
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Hrinivich WT, Chernavsky NE, Morcos M, Li T, Wu P, Wong J, Siewerdsen JH. Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT-guided radiotherapy. Med Phys 2022; 49:6840-6855. [PMID: 35880711 DOI: 10.1002/mp.15877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/22/2022] [Accepted: 07/03/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan-beam (FB) and cone-beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy. METHODS Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5 s/rotation (denoted FB-0.5), 1.9 s/rotation (FB-1.9), 16.6 s/rotation (CB-16.6), and 60.0 s/rotation (CB-60.0). A phantom presenting various tissue densities undergoing motion with 4-s period and ranging in amplitude from ±0.5 to ±10.0 mm was used to characterize motion artifacts (streaks), motion blur (edge-spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5-mm motion with 4-s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners. RESULTS At ±2.5-mm motion, phantom measurements demonstrated mean ± SD ESF widths of 0.6 ± 0.0, 1.3 ± 0.4, 2.0 ± 1.1, and 2.9 ± 2.0 mm and geometric inaccuracy (excursion) of 2.7 ± 0.4, 4.1 ± 1.2, 2.6 ± 0.7, and 2.0 ± 0.5 mm for the FB-0.5, FB-1.9, CB-16.6, and CB-60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB-60.0) scanner and similar for the three faster systems (CB-16.6, FB-1.9, and FB-0.5). CONCLUSIONS Clinically standard CB-60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB-0.5 and FB-1.9, such systems suffer from increased geometric distortion compared to CB-16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next-generation CT-guided radiotherapy systems.
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Affiliation(s)
- William T Hrinivich
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Nicole E Chernavsky
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Marc Morcos
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Taoran Li
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Pengwei Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - John Wong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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Rathee S, Burke B, Heikal A. Comparison of Three Commercial Methods of Cone-Beam Computed Tomography-Based Dosimetric Analysis of Head-and-Neck Patients with Weight Loss. J Med Phys 2022; 47:344-351. [PMID: 36908500 PMCID: PMC9997542 DOI: 10.4103/jmp.jmp_7_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 08/27/2022] [Accepted: 09/02/2022] [Indexed: 01/11/2023] Open
Abstract
Purpose This investigation compares three commercial methods of cone-beam computed tomography (CBCT)-based dosimetric analysis to a method based on repeat computed tomography (CT). Materials and Methods Seventeen head-and-neck patients treated in 2020, and with a repeat CT, were included in the analyses. The planning CT was deformed to anatomy in repeat CT to generate a reference plan. Two of the CBCT-based methods generated test plans by deforming the planning CT to CBCT of fraction N using VelocityAI™ and SmartAdapt®. The third method compared directly calculated doses on the CBCT for fraction 1 and fraction N, using PerFraction™. Maximum dose to spinal cord (Cord_dmax) and dose to 95% volume (D95) of planning target volumes (PTVs) were used to assess "need to replan" criteria. Results The VelocityAI™ method provided results that most accurately matched the reference plan in "need to replan" criteria using either Cord_dmax or PTV D95. SmartAdapt® method overestimated the change in Cord_dmax (6.77% vs. 3.85%, P < 0.01) and change in cord volume (9.56% vs. 0.67%, P < 0.01) resulting in increased false positives in "need to replan" criteria, and performed similarly to VelocityAI™ for D95, but yielded more false negatives. PerFraction™ method underestimated Cord_dmax, did not perform any volume deformation, and missed all "need to replan" cases based on cord dose. It also yielded high false negatives using the D95 PTV criteria. Conclusions The VelocityAI™-based method using fraction N CBCT is most similar to the reference plan using repeat CT; the other two methods had significant differences.
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Affiliation(s)
- Satyapal Rathee
- Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
- Department of Medical Physics, Cross Cancer Institute, Edmonton, Alberta, Canada
| | - Benjamin Burke
- Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
- Department of Medical Physics, Cross Cancer Institute, Edmonton, Alberta, Canada
| | - Amr Heikal
- Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
- Department of Medical Physics, Cross Cancer Institute, Edmonton, Alberta, Canada
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7
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Velten C, Goddard L, Jeong K, Garg MK, Tomé WA. Clinical Assessment of a Novel Ring Gantry Linear Accelerator-Mounted Helical Fan-Beam kVCT System. Adv Radiat Oncol 2022; 7:100862. [PMID: 35036634 PMCID: PMC8749200 DOI: 10.1016/j.adro.2021.100862] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/23/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose To assess clinically relevant image quality metrics (IQMs) of helical fan beam kilovoltage (kV) fan beam computed tomography (CT). Methods and Materials kVCT IQMs were evaluated on an Accuray Radixact unit equipped with helical fan beam kVCT to assess the capabilities of this newly available modality. kVCT IQMs were evaluated and compared to a kVCT simulator and linear accelerator-based cone beam CTs (CBCT) using a commercial CBCT image quality phantom. kVCTs were acquired on the Accuray Radixact for all combinations of kVp and mAs in fine mode using a 440-mm field of view (FOV). Evaluated IQMs were spatial resolution, overall uniformity, subject contrast, contrast-to-noise ratio (CNR), and effective slice thickness. Imaging dose was assessed for planar kV imaging. Results On this kVCT system spatial resolution and contrast were consistent across all settings with 0.28 ± 0.03 lp/mm and 9.8% ± 0.7% (both 95% confidence interval). CNR strongly depended on selected mode (views per rotation) and body size (mA per view) and ranged between 7.9 and 34.9. Overall uniformity was greater than 97% for all settings. Large FOV was not found to substantially affect the IQMs whereas small FOV affected IQMs due to its effect on pitch. Technique-matched CT simulator scans were comparable for uniformity and contrast, while spatial resolution was higher (0.43 ± 0.06 lp/mm), and CNR was between 4% (140 kVp) and 51% (100 kVp) lower. For kV-CBCT, spatial resolutions ranging from 0.37 to 0.44 lp/mm were achieved with comparable contrast, CNR, and uniformity to kVCT. All kVCT scans exhibit imaging artifacts due to helical acquisition. Clinical acquisitions of megavoltage (MV) CT, kV-CBCT, and kVCT on the same patient showed improved and comparable image quality of kVCT compared to MVCT and kV-CBCT, respectively. Conclusions Helical fan beam kVCT allows for daily image guidance for localization and setup verification with comparable performance to existing kV-CBCT systems. Scan parameters must be selected carefully to maximize image quality for the desired tasks. Due to the large effective slice thicknesses for all parameter combinations, kVCT scans should not be used for simulation or planning of stereotactic procedures. Finally, improved image quality over MVCT has the potential to greatly improve manual and automated adaptive monitoring and planning.
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Affiliation(s)
- Christian Velten
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, New York.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, New York
| | - Lee Goddard
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, New York.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, New York
| | - Kyoungkeun Jeong
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, New York.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, New York
| | - Madhur K Garg
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, New York.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, New York
| | - Wolfgang A Tomé
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, New York.,Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, New York
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Dai Z, Zhang Y, Zhu L, Tan J, Yang G, Zhang B, Cai C, Jin H, Meng H, Tan X, Jian W, Yang W, Wang X. Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study. Front Oncol 2021; 11:725507. [PMID: 34858813 PMCID: PMC8630628 DOI: 10.3389/fonc.2021.725507] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/12/2021] [Indexed: 12/29/2022] Open
Abstract
Purpose We developed a deep learning model to achieve automatic multitarget delineation on planning CT (pCT) and synthetic CT (sCT) images generated from cone-beam CT (CBCT) images. The geometric and dosimetric impact of the model was evaluated for breast cancer adaptive radiation therapy. Methods We retrospectively analyzed 1,127 patients treated with radiotherapy after breast-conserving surgery from two medical institutions. The CBCT images for patient setup acquired utilizing breath-hold guided by optical surface monitoring system were used to generate sCT with a generative adversarial network. Organs at risk (OARs), clinical target volume (CTV), and tumor bed (TB) were delineated automatically with a 3D U-Net model on pCT and sCT images. The geometric accuracy of the model was evaluated with metrics, including Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). Dosimetric evaluation was performed by quick dose recalculation on sCT images relying on gamma analysis and dose-volume histogram (DVH) parameters. The relationship between ΔD95, ΔV95 and DSC-CTV was assessed to quantify the clinical impact of the geometric changes of CTV. Results The ranges of DSC and HD95 were 0.73–0.97 and 2.22–9.36 mm for pCT, 0.63–0.95 and 2.30–19.57 mm for sCT from institution A, 0.70–0.97 and 2.10–11.43 mm for pCT from institution B, respectively. The quality of sCT was excellent with an average mean absolute error (MAE) of 71.58 ± 8.78 HU. The mean gamma pass rate (3%/3 mm criterion) was 91.46 ± 4.63%. DSC-CTV down to 0.65 accounted for a variation of more than 6% of V95 and 3 Gy of D95. DSC-CTV up to 0.80 accounted for a variation of less than 4% of V95 and 2 Gy of D95. The mean ΔD90/ΔD95 of CTV and TB were less than 2Gy/4Gy, 4Gy/5Gy for all the patients. The cardiac dose difference in left breast cancer cases was larger than that in right breast cancer cases. Conclusions The accurate multitarget delineation is achievable on pCT and sCT via deep learning. The results show that dose distribution needs to be considered to evaluate the clinical impact of geometric variations during breast cancer radiotherapy.
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Affiliation(s)
- Zhenhui Dai
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yiwen Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Lin Zhu
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Junwen Tan
- Department of Oncology, The Fourth Affiliated Hospital, Guangxi Medical University, Liuzhou, China
| | - Geng Yang
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bailin Zhang
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chunya Cai
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huaizhi Jin
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haoyu Meng
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiang Tan
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wanwei Jian
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xuetao Wang
- Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
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9
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Tajik M, Akhlaqi MM, Gholami S. Advances in anthropomorphic thorax phantoms for radiotherapy: a review. Biomed Phys Eng Express 2021; 8. [PMID: 34736235 DOI: 10.1088/2057-1976/ac369c] [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: 06/13/2021] [Accepted: 11/04/2021] [Indexed: 11/12/2022]
Abstract
A phantom is a highly specialized device, which mimic human body, or a part of it. There are three categories of phantoms: physical phantoms, physiological phantoms, and computational phantoms. The phantoms have been utilized in medical imaging and radiotherapy for numerous applications. In radiotherapy, the phantoms may be used for various applications such as quality assurance (QA), dosimetry, end-to-end testing, etc. In thoracic radiotherapy, unique QA problems including tumor motion, thorax deformation, and heterogeneities in the beam path have complicated the delivery of dose to both tumor and organ at risks (OARs). Also, respiratory motion is a major challenge in radiotherapy of thoracic malignancies, which can be resulted in the discrepancies between the planned and delivered doses to cancerous tissue. Hence, the overall treatment procedure needs to be verified. Anthropomorphic thorax phantoms, which are made of human tissue-mimicking materials, can be utilized to obtain the ground truth to validate these processes. Accordingly, research into new anthropomorphic thorax phantoms has accelerated. Therefore, the review is intended to summarize the current status of the commercially available and in-house-built anthropomorphic physical/physiological thorax phantoms in radiotherapy. The main focus is on anthropomorphic, deformable thorax motion phantoms. This review also discusses the applications of three-dimensional (3D) printing technology for the fabrication of thorax phantoms.
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Affiliation(s)
- Mahdieh Tajik
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Iran Tehran district 6 poursina st Tehran University of Medical Sciences, Tehran, 1416753955, Iran (the Islamic Republic of)
| | - Mohammad Mohsen Akhlaqi
- Shahid Beheshti University of Medical Sciences, Iran,Tehran,Shahid Bahonar roundabout, Darabad Avenue,Masih Daneshvari Hospital, Tehran, 19839-63113, Iran (the Islamic Republic of)
| | - Somayeh Gholami
- Radiotherapy, Tehran University of Medical Sciences, Bolvarekeshavarz AVN, Tehran, Iran, Tehran, 1416753955, Iran (the Islamic Republic of)
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10
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Dai X, Lei Y, Wynne J, Janopaul-Naylor J, Wang T, Roper J, Curran WJ, Liu T, Patel P, Yang X. Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy. Med Phys 2021; 48:7063-7073. [PMID: 34609745 PMCID: PMC8595847 DOI: 10.1002/mp.15264] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 09/15/2021] [Accepted: 09/17/2021] [Indexed: 12/19/2022] Open
Abstract
PURPOSE The delineation of organs at risk (OARs) is fundamental to cone-beam CT (CBCT)-based adaptive radiotherapy treatment planning, but is time consuming, labor intensive, and subject to interoperator variability. We investigated a deep learning-based rapid multiorgan delineation method for use in CBCT-guided adaptive pancreatic radiotherapy. METHODS To improve the accuracy of OAR delineation, two innovative solutions have been proposed in this study. First, instead of directly segmenting organs on CBCT images, a pretrained cycle-consistent generative adversarial network (cycleGAN) was applied to generating synthetic CT images given CBCT images. Second, an advanced deep learning model called mask-scoring regional convolutional neural network (MS R-CNN) was applied on those synthetic CT to detect the positions and shapes of multiple organs simultaneously for final segmentation. The OAR contours delineated by the proposed method were validated and compared with expert-drawn contours for geometric agreement using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and residual mean square distance (RMS). RESULTS Across eight abdominal OARs including duodenum, large bowel, small bowel, left and right kidneys, liver, spinal cord, and stomach, the geometric comparisons between automated and expert contours are as follows: 0.92 (0.89-0.97) mean DSC, 2.90 mm (1.63-4.19 mm) mean HD95, 0.89 mm (0.61-1.36 mm) mean MSD, and 1.43 mm (0.90-2.10 mm) mean RMS. Compared to the competing methods, our proposed method had significant improvements (p < 0.05) in all the metrics for all the eight organs. Once the model was trained, the contours of eight OARs can be obtained on the order of seconds. CONCLUSIONS We demonstrated the feasibility of a synthetic CT-aided deep learning framework for automated delineation of multiple OARs on CBCT. The proposed method could be implemented in the setting of pancreatic adaptive radiotherapy to rapidly contour OARs with high accuracy.
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Affiliation(s)
- Xianjin Dai
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Jacob Wynne
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - James Janopaul-Naylor
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Pretesh Patel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
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11
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Zhang X. A Review of the Robust Optimization Process and Advances with Monte Carlo in the Proton Therapy Management of Head and Neck Tumors. Int J Part Ther 2021; 8:14-24. [PMID: 34285932 PMCID: PMC8270090 DOI: 10.14338/ijpt-20-00078.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 01/11/2021] [Indexed: 11/24/2022] Open
Abstract
In intensity-modulated proton therapy, robust optimization processes have been developed to manage uncertainties associated with (1) range, (2) setup, (3) anatomic changes, (4) dose calculation, and (5) biological effects. Here we review our experience using a robust optimization technique that directly incorporates range and setup uncertainties into the optimization process to manage those sources of uncertainty. We also review procedures for implementing adaptive planning to manage the anatomic uncertainties. Finally, we share some early experiences regarding the impact of uncertainties in dose calculation and biological effects, along with techniques to manage and potentially reduce these uncertainties.
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Affiliation(s)
- Xiaodong Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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12
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Vidal M, Moignier C, Patriarca A, Sotiropoulos M, Schneider T, De Marzi L. Future technological developments in proton therapy - A predicted technological breakthrough. Cancer Radiother 2021; 25:554-564. [PMID: 34272182 DOI: 10.1016/j.canrad.2021.06.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022]
Abstract
In the current spectrum of cancer treatments, despite high costs, a lack of robust evidence based on clinical outcomes or technical and radiobiological uncertainties, particle therapy and in particular proton therapy (PT) is rapidly growing. Despite proton therapy being more than fifty years old (first proposed by Wilson in 1946) and more than 220,000 patients having been treated with in 2020, many technological challenges remain and numerous new technical developments that must be integrated into existing systems. This article presents an overview of on-going technical developments and innovations that we felt were most important today, as well as those that have the potential to significantly shape the future of proton therapy. Indeed, efforts have been done continuously to improve the efficiency of a PT system, in terms of cost, technology and delivery technics, and a number of different developments pursued in the accelerator field will first be presented. Significant developments are also underway in terms of transport and spatial resolution achievable with pencil beam scanning, or conformation of the dose to the target: we will therefore discuss beam focusing and collimation issues which are important parameters for the development of these techniques, as well as proton arc therapy. State of the art and alternative approaches to adaptive PT and the future of adaptive PT will finally be reviewed. Through these overviews, we will finally see how advances in these different areas will allow the potential for robust dose shaping in proton therapy to be maximised, probably foreshadowing a future era of maturity for the PT technique.
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Affiliation(s)
- M Vidal
- Centre Antoine-Lacassagne, Fédération Claude Lalanne, 227, avenue de la Lanterne, 06200 Nice, France
| | - C Moignier
- Centre François Baclesse, Department of Medical Physics, Centre de protonthérapie de Normandie, 14000 Caen, France
| | - A Patriarca
- Institut Curie, PSL Research University, Radiation oncology department, Centre de protonthérapie d'Orsay, Campus universitaire, bâtiment 101, 91898 Orsay, France
| | - M Sotiropoulos
- Institut Curie, Université PSL, CNRS UMR3347, Inserm U1021, Signalisation radiobiologie et cancer, 91400 Orsay, France
| | - T Schneider
- Institut Curie, Université PSL, CNRS UMR3347, Inserm U1021, Signalisation radiobiologie et cancer, 91400 Orsay, France
| | - L De Marzi
- Institut Curie, PSL Research University, Radiation oncology department, Centre de protonthérapie d'Orsay, Campus universitaire, bâtiment 101, 91898 Orsay, France; Institut Curie, PSL Research University, University Paris Saclay, Inserm LITO, Campus universitaire, 91898 Orsay, France.
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13
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Dai X, Lei Y, Wang T, Dhabaan AH, McDonald M, Beitler JJ, Curran WJ, Zhou J, Liu T, Yang X. Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy. Phys Med Biol 2021; 66:045021. [PMID: 33412527 DOI: 10.1088/1361-6560/abd953] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.
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Affiliation(s)
- Xianjin Dai
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
| | - Yang Lei
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
| | - Tonghe Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Anees H Dhabaan
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Mark McDonald
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Jonathan J Beitler
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Walter J Curran
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Jun Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
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14
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Xu Y, Diwanji T, Brovold N, Butkus M, Padgett KR, Schmidt RM, King A, Dal Pra A, Abramowitz M, Pollack A, Dogan N. Assessment of daily dose accumulation for robustly optimized intensity modulated proton therapy treatment of prostate cancer. Phys Med 2021; 81:77-85. [PMID: 33445124 DOI: 10.1016/j.ejmp.2020.11.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 11/02/2020] [Accepted: 11/28/2020] [Indexed: 12/30/2022] Open
Abstract
PURPOSE To implement a daily CBCT based dose accumulation technique in order to assess ideal robust optimization (RO) parameters for IMPT treatment of prostate cancer. METHODS Ten prostate cancer patients previously treated with VMAT and having daily CBCT were included. First, RO-IMPT plans were created with ± 3 mm and ± 5 mm patient setup and ± 3% proton range uncertainties, respectively. Second, the planning CT (pCT) was deformably registered to the CBCT to create a synthetic CT (sCT). Both daily and weekly sampling strategies were employed to determine optimal dose accumulation frequency. Doses were recalculated on sCTs for both ± 3 mm/±3% and ± 5 mm/±3% uncertainties and were accumulated back to the pCT. Accumulated doses generated from ± 3 mm/±3% and ± 5 mm/±3% RO-IMPT plans were evaluated using the clinical dose volume constraints for CTV, bladder, and rectum. RESULTS Daily accumulated dose based on both ± 3mm/±3% and ±5 mm/±3% uncertainties for RO-IMPT plans resulted in satisfactory CTV coverage (RO-IMPT3mm/3% CTVV95 = 99.01 ± 0.87% vs. RO-IMPT5mm/3% CTVV95 = 99.81 ± 0.2%, P = 0.002). However, the accumulated dose based on ± 3 mm/3% RO-IMPT plans consistently provided greater OAR sparing than ±5 mm/±3% RO-IMPT plans (RO-IMPT3mm/3% rectumV65Gy = 2.93 ± 2.39% vs. RO-IMPT5mm/3% rectumV65Gy = 4.38 ± 3%, P < 0.01; RO-IMPT3mm/3% bladderV65Gy = 5.2 ± 7.12% vs. RO-IMPT5mm/3% bladderV65Gy = 7.12 ± 9.59%, P < 0.01). The gamma analysis showed high dosimetric agreement between weekly and daily accumulated dose distributions. CONCLUSIONS This study demonstrated that for RO-IMPT optimization, ±3mm/±3% uncertainty is sufficient to create plans that meet desired CTV coverage while achieving superior sparing to OARs when compared with ± 5 mm/±3% uncertainty. Furthermore, weekly dose accumulation can accurately estimate the overall dose delivered to prostate cancer patients.
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Affiliation(s)
- Yihang Xu
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Tejan Diwanji
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nellie Brovold
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael Butkus
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ryder M Schmidt
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Adam King
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matt Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.
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15
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Liang X, Nguyen D, Jiang SB. Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/abb214] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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16
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Wu RY, Liu AY, Yang J, Williamson TD, Wisdom PG, Bronk L, Gao S, Grosshan DR, Fuller DC, Gunn GB, Ronald Zhu X, Frank SJ. Evaluation of the accuracy of deformable image registration on MRI with a physical phantom. J Appl Clin Med Phys 2019; 21:166-173. [PMID: 31808307 PMCID: PMC6964753 DOI: 10.1002/acm2.12789] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/29/2019] [Accepted: 11/14/2019] [Indexed: 01/13/2023] Open
Abstract
Background and purpose Magnetic resonance imaging (MRI) has gained popularity in radiation therapy simulation because it provides superior soft tissue contrast, which facilitates more accurate target delineation compared with computed tomography (CT) and does not expose the patient to ionizing radiation. However, image registration errors in commercial software have not been widely reported. Here we evaluated the accuracy of deformable image registration (DIR) by using a physical phantom for MRI. Methods and materials We used the “Wuphantom” for end‐to‐end testing of DIR accuracy for MRI. This acrylic phantom is filled with water and includes several fillable inserts to simulate various tissue shapes and properties. Deformations and changes in anatomic locations are simulated by changing the rotations of the phantom and inserts. We used Varian Velocity DIR software (v4.0) and CT (head and neck protocol) and MR (T1‐ and T2‐weighted head protocol) images to test DIR accuracy between image modalities (MRI vs CT) and within the same image modality (MRI vs MRI) in 11 rotation deformation scenarios. Large inserts filled with Mobil DTE oil were used to simulate fatty tissue, and small inserts filled with agarose gel were used to simulate tissues slightly denser than water (e.g., prostate). Contours of all inserts were generated before DIR to provide a baseline for contour size and shape. DIR was done with the MR Correctable Deformable DIR method, and all deformed contours were compared with the original contours. The Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were used to quantitatively validate DIR accuracy. We also used large and small regions of interest (ROIs) during between‐modality DIR tests to simulate validation of DIR accuracy for organs at risk (OARs) and propagation of individual clinical target volume (CTV) contours. Results No significant differences in DIR accuracy were found for T1:T1 and T2:T2 comparisons (P > 0.05). DIR was less accurate for between‐modality comparisons than for same‐modality comparisons, and was less accurate for T1 vs CT than for T2 vs CT (P < 0.001). For between‐modality comparisons, use of a small ROI improved DIR accuracy for both T1 and T2 images. Conclusion The simple design of the Wuphantom allows seamless testing of DIR; here we validated the accuracy of MRI DIR in end‐to‐end testing. T2 images had superior DIR accuracy compared with T1 images. Use of small ROIs improves DIR accuracy for target contour propagation.
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Affiliation(s)
- Richard Y Wu
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amy Y Liu
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tyler D Williamson
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul G Wisdom
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lawrence Bronk
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Song Gao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David R Grosshan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David C Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gary B Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - X Ronald Zhu
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Steven J Frank
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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