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Li X, Bellotti R, Bachtiary B, Hrbacek J, Weber DC, Lomax AJ, Buhmann JM, Zhang Y. A unified generation-registration framework for improved MR-based CT synthesis in proton therapy. Med Phys 2024. [PMID: 39137294 DOI: 10.1002/mp.17338] [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: 01/22/2024] [Revised: 06/11/2024] [Accepted: 07/06/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND The use of magnetic resonance (MR) imaging for proton therapy treatment planning is gaining attention as a highly effective method for guidance. At the core of this approach is the generation of computed tomography (CT) images from MR scans. However, the critical issue in this process is accurately aligning the MR and CT images, a task that becomes particularly challenging in frequently moving body areas, such as the head-and-neck. Misalignments in these images can result in blurred synthetic CT (sCT) images, adversely affecting the precision and effectiveness of the treatment planning. PURPOSE This study introduces a novel network that cohesively unifies image generation and registration processes to enhance the quality and anatomical fidelity of sCTs derived from better-aligned MR images. METHODS The approach synergizes a generation network (G) with a deformable registration network (R), optimizing them jointly in MR-to-CT synthesis. This goal is achieved by alternately minimizing the discrepancies between the generated/registered CT images and their corresponding reference CT counterparts. The generation network employs a UNet architecture, while the registration network leverages an implicit neural representation (INR) of the displacement vector fields (DVFs). We validated this method on a dataset comprising 60 head-and-neck patients, reserving 12 cases for holdout testing. RESULTS Compared to the baseline Pix2Pix method with MAE 124.95 ± $\pm$ 30.74 HU, the proposed technique demonstrated 80.98 ± $\pm$ 7.55 HU. The unified translation-registration network produced sharper and more anatomically congruent outputs, showing superior efficacy in converting MR images to sCTs. Additionally, from a dosimetric perspective, the plan recalculated on the resulting sCTs resulted in a remarkably reduced discrepancy to the reference proton plans. CONCLUSIONS This study conclusively demonstrates that a holistic MR-based CT synthesis approach, integrating both image-to-image translation and deformable registration, significantly improves the precision and quality of sCT generation, particularly for the challenging body area with varied anatomic changes between corresponding MR and CT.
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Affiliation(s)
- Xia Li
- Center for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
| | - Renato Bellotti
- Center for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland
- Department of Physics, ETH Zürich, Zürich, Switzerland
| | - Barbara Bachtiary
- Center for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland
| | - Jan Hrbacek
- Center for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland
| | - Damien C Weber
- Center for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland
- Department of Radiation Oncology, University Hospital of Zürich, Zürich, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Antony J Lomax
- Center for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland
- Department of Physics, ETH Zürich, Zürich, Switzerland
| | | | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland
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Wong YM, Koh CWY, Lew KS, Chua CGA, Yeap PL, Zhang ET, Ong ALK, Tuan JKL, Ng BF, Lew WS, Lee JCL, Tan HQ. Deformable anthropomorphic pelvis phantom for dose accumulation verification. Phys Med Biol 2024; 69:12NT01. [PMID: 38821109 DOI: 10.1088/1361-6560/ad52e4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 05/31/2024] [Indexed: 06/02/2024]
Abstract
Objective.The validation of deformable image registration (DIR) for contour propagation is often done using contour-based metrics. Meanwhile, dose accumulation requires evaluation of voxel mapping accuracy, which might not be accurately represented by contour-based metrics. By fabricating a deformable anthropomorphic pelvis phantom, we aim to (1) quantify the voxel mapping accuracy for various deformation scenarios, in high- and low-contrast regions, and (2) identify any correlation between dice similarity coefficient (DSC), a commonly used contour-based metric, and the voxel mapping accuracy for each organ.Approach. Four organs, i.e. pelvic bone, prostate, bladder and rectum (PBR), were 3D printed using PLA and a Polyjet digital material, and assembled. The latter three were implanted with glass bead and CT markers within or on their surfaces. Four deformation scenarios were simulated by varying the bladder and rectum volumes. For each scenario, nine DIRs with different parameters were performed on RayStation v10B. The voxel mapping accuracy was quantified by finding the discrepancy between true and mapped marker positions, termed the target registration error (TRE). Pearson correlation test was done between the DSC and mean TRE for each organ.Main results. For the first time, we fabricated a deformable phantom purely from 3D printing, which successfully reproduced realistic anatomical deformations. Overall, the voxel mapping accuracy dropped with increasing deformation magnitude, but improved when more organs were used to guide the DIR or limit the registration region. DSC was found to be a good indicator of voxel mapping accuracy for prostate and rectum, but a comparatively poorer one for bladder. DSC > 0.85/0.90 was established as the threshold of mean TRE ⩽ 0.3 cm for rectum/prostate. For bladder, extra metrics in addition to DSC should be considered.Significance. This work presented a 3D printed phantom, which enabled quantification of voxel mapping accuracy and evaluation of correlation between DSC and voxel mapping accuracy.
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Affiliation(s)
- Yun Ming Wong
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
| | - Calvin Wei Yang Koh
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Kah Seng Lew
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Clifford Ghee Ann Chua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Ping Lin Yeap
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Ee Teng Zhang
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
- Singapore Centre for 3D Printing, Nanyang Technological University, Singapore, Singapore
| | - Ashley Li Kuan Ong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Jeffrey Kit Loong Tuan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Bing Feng Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Wen Siang Lew
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
| | - James Cheow Lei Lee
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Hong Qi Tan
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
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Yap LM, Jamalludin Z, Ng AH, Ung NM. A multi-center survey on adaptive radiation therapy for head and neck cancer in Malaysia. Phys Eng Sci Med 2023; 46:1331-1340. [PMID: 37470929 DOI: 10.1007/s13246-023-01303-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
The survey is to assess the current state of adaptive radiation therapy (ART) for head and neck (H&N) cases among radiotherapy centers in Malaysia and to identify any implementation limitations. An online questionnaire was sent to all radiotherapy centers in Malaysia. The 24-question questionnaire consists of general information about the center, ART practices, and limitations faced in implementing ART. 28 out of 36 radiotherapy centers responded, resulting in an overall response rate of 78%. About 52% of the responding centers rescanned and replanned less than 5% of their H&N patients. The majority (88.9%) of the respondents reported the use Cone Beam Computed Tomography alone or in combination with other modalities to trigger the ART process. The main reasons cited for adopting ART were weight loss, changes in the immobilization fitting, and anatomical variation. The adaptation process typically occurred during week 3 or week 4 of treatment. More than half of the respondents require three days or more from re-simulation to starting a new treatment plan. Both target and organ at risk delineation on new planning CT relied heavily on manual delineation by physicians and physicists, respectively. All centers perform patient-specific quality assurance for their new adaptive plans. Two main limitations in implementing ART are "limited financial resources or equipment" and "limitation on technical knowledge". There is a need for a common consensus to standardize the practice of ART and address these limitations to improve the implementation of ART in Malaysia.
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Affiliation(s)
- Lai Mun Yap
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
- Department of Radiotherapy, Aurelius Hospital Nilai, 71800, Nilai, Malaysia
| | - Zulaikha Jamalludin
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Aik Hao Ng
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Ngie Min Ung
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
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Delaby N, Barateau A, Chiavassa S, Biston MC, Chartier P, Graulières E, Guinement L, Huger S, Lacornerie T, Millardet-Martin C, Sottiaux A, Caron J, Gensanne D, Pointreau Y, Coutte A, Biau J, Serre AA, Castelli J, Tomsej M, Garcia R, Khamphan C, Badey A. Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view. Phys Med 2023; 109:102568. [PMID: 37015168 DOI: 10.1016/j.ejmp.2023.102568] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 02/15/2023] [Accepted: 03/18/2023] [Indexed: 04/05/2023] Open
Abstract
Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.
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Feasibility of delivered dose reconstruction for MR-guided SBRT of pancreatic tumors with fast, real-time 3D cine MRI. Radiother Oncol 2023; 182:109506. [PMID: 36736589 DOI: 10.1016/j.radonc.2023.109506] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND PURPOSE In MR-guided SBRT of pancreatic cancer, intrafraction motion is typically monitored with (interleaved) 2D cine MRI. However, tumor surroundings are often not fully captured in these images, and motion might be distorted by through-plane movement. In this study, the feasibility of highly accelerated 3D cine MRI to reconstruct the delivered dose during MR-guided SBRT was assessed. MATERIALS AND METHODS A 3D cine MRI sequence was developed for fast, time-resolved 4D imaging, featuring a low spatial resolution that allows for rapid volumetric imaging at 430 ms. The 3D cines were acquired during the entire beam-on time of 23 fractions of online adaptive MR-guided SBRT for pancreatic tumors on a 1.5 T MR-Linac. A 3D deformation vector field (DVF) was extracted for every cine dynamic using deformable image registration. Next, these DVFs were used to warp the partial dose delivered in the time interval between consecutive cine acquisitions. The warped dose plans were summed to obtain a total delivered dose. The delivered dose was also calculated under various motion correction strategies. Key DVH parameters of the GTV, duodenum, small bowel and stomach were extracted from the delivered dose and compared to the planned dose. The uncertainty of the calculated DVFs was determined with the inverse consistency error (ICE) in the high-dose regions. RESULTS The mean (SD) relative (ratio delivered/planned) D99% of the GTV was 0.94 (0.06), and the mean (SD) relative D0.5cc of the duodenum, small bowel, and stomach were respectively 0.98 (0.04), 1.00 (0.07), and 0.98 (0.06). In the fractions with the lowest delivered tumor coverage, it was found that significant lateral drifts had occurred. The DVFs used for dose warping had a low uncertainty with a mean (SD) ICE of 0.65 (0.07) mm. CONCLUSION We employed a fast, real-time 3D cine MRI sequence for dose reconstruction in the upper abdomen, and demonstrated that accurate DVFs, acquired directly from these images, can be used for dose warping. The reconstructed delivered dose showed only a modest degradation of tumor coverage, mostly attainable to baseline drifts. This emphasizes the need for motion monitoring and development of intrafraction treatment adaptation solutions, such as baseline drift corrections.
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Cao Y, Zhu X, Yu C, Jiang L, Sun Y, Guo X, Zhang H. Dose evaluations of organs at risk and predictions of gastrointestinal toxicity after re-irradiation with stereotactic body radiation therapy for pancreatic cancer by deformable image registration. Front Oncol 2023; 12:1021058. [PMID: 36793343 PMCID: PMC9923872 DOI: 10.3389/fonc.2022.1021058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 12/07/2022] [Indexed: 01/31/2023] Open
Abstract
Purpose Re-irradiation of locally recurrent pancreatic cancer may be an optimal choice as a local ablative therapy. However, dose constraints of organs at risk (OARs) predictive of severe toxicity remain unknown. Therefore, we aim to calculate and identify accumulated dose distributions of OARs correlating with severe adverse effects and determine possible dose constraints regarding re-irradiation. Methods Patients receiving two courses of stereotactic body radiation therapy (SBRT) for the same irradiated regions (the primary tumors) due to local recurrence were included. All doses of the first and second plans were recalculated to an equivalent dose of 2 Gy per fraction (EQD2). Deformable image registration with the workflow "Dose Accumulation-Deformable" of the MIM® System (version: 6.6.8) was performed for dose summations. Dose-volume parameters predictive of grade 2 or more toxicities were identified, and the receiver operating characteristic (ROC) curve was used to determine optimal thresholds of dose constraints. Results Forty patients were included in the analysis. Only the V 10 of the stomach [hazard ratio (HR): 1.02 (95% CI:1.00-1.04), P = 0.035] and D mean of the intestine [HR: 1.78 (95% CI: 1.00-3.18), P = 0.049] correlated with grade 2 or more gastrointestinal toxicity. Hence, the equation of probability of such toxicity was P = 1 1 + e - ( - 4.155 + 0.579 D mean of the intestine + 0.021 V 10 of the stomach ) Additionally, the area under the ROC curve and threshold of dose constraints of V 10 of the stomach and D mean of the intestine were 0.779 and 77.575 cc, 0.769 and 4.22 Gy3 (α/β = 3), respectively. The area under the ROC curve of the equation was 0.821. Conclusion The V 10 of the stomach and D mean of the intestine may be vital parameters to predict grade 2 or more gastrointestinal toxicity, of which the threshold of dose constraints may be beneficial for the practice of re-irradiation of locally relapsed pancreatic cancer.
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McDonald BA, Zachiu C, Christodouleas J, Naser MA, Ruschin M, Sonke JJ, Thorwarth D, Létourneau D, Tyagi N, Tadic T, Yang J, Li XA, Bernchou U, Hyer DE, Snyder JE, Bubula-Rehm E, Fuller CD, Brock KK. Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation. Front Oncol 2023; 12:1086258. [PMID: 36776378 PMCID: PMC9909539 DOI: 10.3389/fonc.2022.1086258] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/21/2022] [Indexed: 01/27/2023] Open
Abstract
MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.
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Affiliation(s)
- Brigid A. McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Cornel Zachiu
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark Ruschin
- Department of Radiation Oncology, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Tuebingen, Germany
| | - Daniel Létourneau
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Uffe Bernchou
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Daniel E. Hyer
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | - Jeffrey E. Snyder
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | | | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kristy K. Brock
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Li N, Zhou X, Chen S, Dai J, Wang T, Zhang C, He W, Xie Y, Liang X. Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT. Front Oncol 2023; 13:1127866. [PMID: 36910636 PMCID: PMC9993856 DOI: 10.3389/fonc.2023.1127866] [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: 12/20/2022] [Accepted: 01/25/2023] [Indexed: 02/25/2023] Open
Abstract
Objective To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR). Methods This study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images via the proposed CLG model. We used the Sct images as the fixed images instead of the CBCT images to achieve the multi-modality image registration accurately. The deformation vector field is applied to propagate the target contour from the pCT to CBCT to realize the automatic target segmentation on CBCT images. We calculate the Dice similarity coefficient (DSC), 95 % Hausdorff distance (HD95), and average surface distance (ASD) between the prediction and reference segmentation to evaluate the proposed method. Results The DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region. Conclusion The CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.
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Affiliation(s)
- Na Li
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, Guangdong, China.,Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, Guangdong, China.,Songshan Lake Innovation Center of Medicine & Engineering, Guangdong Medical University, Dongguan, Guangdong, China
| | - Xuanru Zhou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.,Department of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Shupeng Chen
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States
| | - Jingjing Dai
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Tangsheng Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Chulong Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Wenfeng He
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xiaokun Liang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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9
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Teuwen J, Gouw ZA, Sonke JJ. Artificial Intelligence for Image Registration in Radiation Oncology. Semin Radiat Oncol 2022; 32:330-342. [DOI: 10.1016/j.semradonc.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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10
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Dossun C, Niederst C, Noel G, Meyer P. Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies. Phys Med 2022; 101:137-157. [PMID: 36007403 DOI: 10.1016/j.ejmp.2022.08.011] [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: 05/19/2022] [Revised: 07/21/2022] [Accepted: 08/16/2022] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The performance of deformable medical image registration (DIR) algorithms has become a major concern. METHODS We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines. RESULTS One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed. CONCLUSIONS This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.
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Affiliation(s)
- C Dossun
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - C Niederst
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - G Noel
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - P Meyer
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France; ICUBE, CNRS UMR 7357, Team IMAGES, Strasbourg, France.
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Jiang C, Huang Y, Ding S, Gong X, Yuan X, Wang S, Li J, Zhang Y. Comparison of an in-house hybrid DIR method to NiftyReg on CBCT and CT images for head and neck cancer. J Appl Clin Med Phys 2022; 23:e13540. [PMID: 35084081 PMCID: PMC8906219 DOI: 10.1002/acm2.13540] [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: 08/10/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 11/10/2022] Open
Abstract
An in-house hybrid deformable image registration (DIR) method, which combines free-form deformation (FFD) and the viscous fluid registration method, is proposed. Its results on the planning computed tomography (CT) and the day 1 treatment cone-beam CT (CBCT) image from 68 head and neck cancer patients are compared with the results of NiftyReg, which uses B-spline FFD alone. Several similarity metrics, the target registration error (TRE) of annotated points, as well as the Dice similarity coefficient (DSC) and Hausdorff distance (HD) of the propagated organs at risk are employed to analyze their registration accuracy. According to quantitative analysis on mutual information, normalized cross-correlation, and the absolute pixel value differences, the results of the proposed DIR are more similar to the CBCT images than the NiftyReg results. Smaller TRE of the annotated points is observed in the proposed method, and the overall mean TRE for the proposed method and NiftyReg was 2.34 and 2.98 mm, respectively (p < 0.001). The mean DSC in the larynx, spinal cord, oral cavity, mandible, and parotid given by the proposed method ranged from 0.78 to 0.91, significantly higher than the NiftyReg results (ranging from 0.77 to 0.90), and the HD was significantly lower compared to NiftyReg. Furthermore, the proposed method did not suffer from unrealistic deformations as the NiftyReg did in the visual evaluation. Meanwhile, the execution time of the proposed method was much higher than NiftyReg (96.98 ± 11.88 s vs. 4.60 ± 0.49 s). In conclusion, the in-house hybrid method gave better accuracy and more stable performance than NiftyReg.
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Affiliation(s)
- Chunling Jiang
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China.,Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Nanchang, P. R. China.,Medical College of Nanchang University, Nanchang, P. R. China
| | - Yuling Huang
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Shenggou Ding
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Xiaochang Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Xingxing Yuan
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Shaobin Wang
- MedMind Technology Co. Ltd., Beijing, P. R. China
| | - Jingao Li
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China.,Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Nanchang, P. R. China.,Medical College of Nanchang University, Nanchang, P. R. China
| | - Yun Zhang
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
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Gou S, Tong N, Qi S, Yang S, Chin R, Sheng K. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images. Phys Med Biol 2020; 65:245034. [PMID: 32097892 DOI: 10.1088/1361-6560/ab79c3] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning, but manual delineation is tedious, slow, and inconsistent. A self-channel-and-spatial-attention neural network (SCSA-Net) is developed for H&N OAR segmentation on CT images. To simultaneously ease the training and improve the segmentation performance, the proposed SCSA-Net utilizes the self-attention ability of the network. Spatial and channel-wise attention learning mechanisms are both employed to adaptively force the network to emphasize the meaningful features and weaken the irrelevant features simultaneously. The proposed network was first evaluated on a public dataset, which includes 48 patients, then on a separate serial CT dataset, which contains ten patients who received weekly diagnostic fan-beam CT scans. On the second dataset, the accuracy of using SCSA-Net to track the parotid and submandibular gland volume changes during radiotherapy treatment was quantified. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95% maximum surface distance (95SD) were calculated on the brainstem, optic chiasm, optic nerves, mandible, parotid glands, and submandibular glands to evaluate the proposed SCSA-Net. The proposed SCSA-Net consistently outperforms the state-of-the-art methods on the public dataset. Specifically, compared with Res-Net and SE-Net, which is constructed from squeeze-and-excitation block equipped residual blocks, the DSC of the optic nerves and submandibular glands is improved by 0.06, 0.03 and 0.05, 0.04 by the SCSA-Net. Moreover, the proposed method achieves statistically significant improvements in terms of DSC on all and eight of nine OARs over Res-Net and SE-Net, respectively. The trained network was able to achieve good segmentation results on the serial dataset, but the results were further improved after fine-tuning of the model using the simulation CT images. For the parotids and submandibular glands, the volume changes of individual patients are highly consistent between the automated and manual segmentation (Pearson's correlation 0.97-0.99). The proposed SCSA-Net is computationally efficient to perform segmentation (sim 2 s/CT).
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Affiliation(s)
- Shuiping Gou
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi 710071, People's Republic of China
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Computed Tomography/Magnetic Resonance Imaging (CT/MRI) Image Registration and Fusion Assessment for Accurate Glioblastoma Radiotherapy Treatment Planning. INTERNATIONAL JOURNAL OF CANCER MANAGEMENT 2020. [DOI: 10.5812/ijcm.103160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: In this study, computed tomography/magnetic resonance imaging (CT/MRI) image registration and fusion in the 3D conformal radiotherapy treatment planning of Glioblastoma brain tumor was investigated. Good CT/MRI image registration and fusion made a great impact on dose calculation and treatment planning accuracy. Indeed, the uncertainly associated with the registration and fusion methods must be well verified and communicated. Unfortunately, there is no standard procedure or mathematical formalism to perform this verification due to noise, distortion, and complicated anatomical situations. Objectives: This study aimed at assessing the effective contribution of MRI in Glioma radiotherapy treatment by improving the localization of target volumes and organs at risk (OARs). It is also a question to provide clinicians with some suitable metrics to evaluate the CT/MRI image registration and fusion results. Methods: Quantitative image registration and fusion evaluation were used in this study to compare Eclipse TPS tools and Elastix CT/MRI image registration fusion. Thus, Dice score coefficient (DSC), Jaccard similarity coefficient (JSC), and Hausdorff distance (HD) were found to be suitable metrics for the evaluation and comparison of the image registration and fusion methods of Eclipse TPS and Elastix. Results: The programmed tumor’s volumes (PTV) delineated on CT slices were approximately 1.38 times smaller than those delineated on CT/MRI fused images. Large differences were observed for the edema and the brainstem. It was also found that MRI considerably optimized the dose to be delivered to the optic nerve and brainstem. Conclusions: Image registration and fusion is a fundamental step for suitable and efficient Glioma treatment planning in 3D conformal radiotherapy that ensure accurate dose delivery and unnecessary OAR irradiation. MRI can provide accurate localization of targeted volumes leading to better irradiation control of Glioma tumor.
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Mixed-beam approach for high-risk prostate cancer: Carbon-ion boost followed by photon intensity-modulated radiotherapy. Dosimetric and geometric evaluations (AIRC IG-14300). Phys Med 2020; 76:327-336. [PMID: 32750548 DOI: 10.1016/j.ejmp.2020.07.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 07/03/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND AND PURPOSE The aim was to evaluate dosimetric uncertainties of a mixed beam approach for patients with high-risk prostate cancer (PCa). The treatment consists of a carbon ion radiotherapy (CIRT) boost followed by whole-pelvis intensity-modulated RT (IMRT). MATERIALS AND METHODS Patients were treated with a CIRT boost of 16.6 Gy/4 fractions followed by whole-pelvis IMRT of 50 Gy/25 fractions, with consequent long term androgen deprivation therapy. Deformable computed tomography image registration (DIR) was performed and corresponding doses were used for plan sum. A comparative IMRT photon plan was obtained as whole-pelvis IMRT of 50 Gy/25 fractions followed by a boost of 28 Gy/14 fractions. DIR performances were evaluated through structure-related and image characteristics parameters. RESULTS Until now, five patients out of ten total enrolled ended the treatment. Dosimetric parameters were lower in CIRT + IMRT than IMRT-only plans for all organs at risk (OARs) except femoral heads. Regarding DIR evaluation, femoral heads were the less deformed OAR. Penile bulb, bladder and anal canal showed intermediate deformation. Rectum was the most deformed. DIR algorithms were patient (P)-dependent, as performances were the highest for P3 and P4, intermediate for P2 and P5, and the lowest for P1. CONCLUSIONS CIRT allows better OARs sparing while increasing the efficacy due to the higher radio-biological effect of carbon ions. However, a mixed beam approach could introduce DIR problems in multi-centric treatments with different operative protocols. The development of this prospective trial will lead to more mature data concerning the clinical impact of implementing DIR procedures in dose accumulation applications for high-risk PCa treatments.
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Free-to-use DIR solutions in radiotherapy: Benchmark against commercial platforms through a contour-propagation study. Phys Med 2020; 74:110-117. [PMID: 32464468 DOI: 10.1016/j.ejmp.2020.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 05/08/2020] [Accepted: 05/17/2020] [Indexed: 11/22/2022] Open
Abstract
PURPOSE A contour propagation study has been conducted to benchmark three algorithms for Deformable Image Registration (DIR) freely available online against well-established commercial solutions. METHODS ElastiX, BRAINS and Plastimach, available as modules in the open source platform 3DSlicer, were tested as the recent AAPM Task group 132 guidelines proposes. The overlap of the DIR-mapped ROIs in four computational anthropomorphic phantoms was measured. To avoid bias every algorithm was left to run without any human interaction nor particular registration strategy. The accuracy of the algorithms was measured using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. The registration quality was compared to the recommended geometrical accuracy suggested by AAPM TG132 and to the results of a large population-based study performed with commercial DIR solutions. RESULTS The considered free-to-use DIR solutions generally meet acceptable accuracy and good overlap (DSC > 0.85). Mild failures (DSC < 0.75) were detected only for the smallest structures. In case of extremely severe deformations acceptable accuracy was not met (MDC > 3 mm). The morphing capability of the tested algorithms equals those of commercial systems when the user interaction is avoided. Underperformances were detected only in cases where a specific registration strategy is mandatory to obtain a satisfying match. CONCLUSIONS All of the considered algorithms show performances not inferior to previously published data and have the potential to be good candidates for use in the clinical routine. The results and conclusions only apply to the considered phantoms and should not be considered to be generally applicable and extendable to patient cases.
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Lowther NJ, Marsh SH, Louwe RJW. Quantifying the dose accumulation uncertainty after deformable image registration in head-and-neck radiotherapy. Radiother Oncol 2020; 143:117-125. [PMID: 32063377 DOI: 10.1016/j.radonc.2019.12.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 12/12/2019] [Accepted: 12/15/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE Deformable image registration (DIR) facilitated dose reconstruction and accumulation can be applied to assess delivered dose and verify the validity of the treatment plan during treatment. This retrospective study used in silico deformations based on clinically observed anatomical changes as ground truth to investigate the uncertainty of reconstructed and accumulated dose in head-and-neck radiotherapy (HNRT). MATERIALS AND METHODS A planning CT (pCT), cone beam CT (CBCT) from week one of treatment and three later CBCTs were selected for 12 HNRT patients. These images were used to generate in silico reference CBCTs and deformation vector fields (DVFs) as ground truth with B-spline DIR. Inverse consistency (IC) of voxels was assessed by determining their net displacement after successive application of the forward and backward DVF. The reconstructed dose based on demons DIR was compared to the ground truth to assess the structure-specific uncertainties of this DIR algorithm for inverse consistent and inverse inconsistent voxels. RESULTS Overall, 98.5% of voxels were inverse consistent with the 95% level of confidence range for dose reconstruction of a single fraction equal to [-2.3%; +2.1%], [-10.2%; +15.2%] and [-9.5%; +12.5%] relative to their planned dose for target structures, critical organs at risk (OARs) and non-critical OARs, respectively. Inverse inconsistent voxels generally showed a higher level of uncertainty. CONCLUSION The uncertainty in accumulated dose using DIR can be accurately quantified and incorporated in dose-volume histograms (DVHs). This method can be used to prospectively assess the adequacy of target coverage during treatment in an objective manner.
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Affiliation(s)
- Nicholas J Lowther
- Wellington Blood and Cancer Centre, Department of Radiation Oncology, Wellington, New Zealand; University of Canterbury, School of Physical and Chemical Sciences, Christchurch, New Zealand
| | - Steven H Marsh
- University of Canterbury, School of Physical and Chemical Sciences, Christchurch, New Zealand
| | - Robert J W Louwe
- Wellington Blood and Cancer Centre, Department of Radiation Oncology, Wellington, New Zealand.
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Mokri S, Saripan M, Nordin A, Marhaban M, Abd Rahni A. Thoracic hybrid PET/CT registration using improved hybrid feature intensity multimodal demon. Radiat Phys Chem Oxf Engl 1993 2020. [DOI: 10.1016/j.radphyschem.2019.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Yuan Z, Rong Y, Benedict SH, Daly ME, Qiu J, Yamamoto T. "Dose of the day" based on cone beam computed tomography and deformable image registration for lung cancer radiotherapy. J Appl Clin Med Phys 2019; 21:88-94. [PMID: 31816170 PMCID: PMC6964750 DOI: 10.1002/acm2.12793] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 02/04/2019] [Accepted: 11/17/2019] [Indexed: 12/25/2022] Open
Abstract
Purpose Adaptive radiotherapy (ART) has potential to reduce toxicity and facilitate safe dose escalation. Dose calculations with the planning CT deformed to cone beam CT (CBCT) have shown promise for estimating the “dose of the day”. The purpose of this study is to investigate the “dose of the day” calculation accuracy based on CBCT and deformable image registration (DIR) for lung cancer radiotherapy. Methods A total of 12 lung cancer patients were identified, for which daily CBCT imaging was performed for treatment positioning. A re‐planning CT (rCT) was acquired after 20 Gy for all patients. A virtual CT (vCT) was created by deforming initial planning CT (pCT) to the simulated CBCT that was generated from deforming CBCT to rCT acquired on the same day. Treatment beams from the initial plan were copied to the vCT and rCT for dose calculation. Dosimetric agreement between vCT‐based and rCT‐based accumulated doses was evaluated using the Bland‐Altman analysis. Results Mean differences in dose‐volume metrics between vCT and rCT were smaller than 1.5%, and most discrepancies fell within the range of ± 5% for the target volume, lung, esophagus, and heart. For spinal cord Dmax, a large mean difference of −5.55% was observed, which was largely attributed to very limited CBCT image quality (e.g., truncation artifacts). Conclusion This study demonstrated a reasonable agreement in dose‐volume metrics between dose accumulation based on vCT and rCT, with the exception for cases with poor CBCT image quality. These findings suggest potential utility of vCT for providing a reasonable estimate of the “dose of the day”, and thus facilitating the process of ART for lung cancer.
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Affiliation(s)
- Zilong Yuan
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA.,Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Megan E Daly
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Jianfeng Qiu
- Medical Engineering and Technology Research Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
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Loi G, Fusella M, Vecchi C, Menna S, Rosica F, Gino E, Maffei N, Menghi E, Savini A, Roggio A, Radici L, Cagni E, Lucio F, Strigari L, Strolin S, Garibaldi C, Romanò C, Piovesan M, Franco P, Fiandra C. Computed Tomography to Cone Beam Computed Tomography Deformable Image Registration for Contour Propagation Using Head and Neck, Patient-Based Computational Phantoms: A Multicenter Study. Pract Radiat Oncol 2019; 10:125-132. [PMID: 31786233 DOI: 10.1016/j.prro.2019.11.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/22/2019] [Accepted: 11/12/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography. METHODS AND MATERIALS Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets. On-board images from 3 systems were used: megavoltage computed tomography from Tomotherapy and 2 kinds of CBCT from a clinical linear accelerator. Image quality of the system was evaluated. The algorithms' accuracy was assessed by comparing the DIR-mapped ROIs returned by each center with those of the reference, using the Dice similarity coefficient and mean distance to conformity metrics. Statistical inference on the validation results was carried out to identify the prognostic factors of DIR performance. RESULTS Analyzing 840 DIR-mapped ROIs returned by the centers, it was demonstrated that DVF intensity and image quality were significant prognostic factors of DIR performance. The accuracy of the propagated contours was generally high, and acceptable DIR performance can be obtained with lower-dose CBCT image protocols. CONCLUSIONS The performance of the systems proved to be image quality specific, depending on the DVF type and only partially on the platforms. All systems proved to be robust against image artifacts and noise, except the demon-based software.
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Affiliation(s)
- Gianfranco Loi
- Department of Medical Physics, University Hospital "Maggiore della Carità," Novara, Italy
| | - Marco Fusella
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy.
| | | | - Sebastiano Menna
- Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC di Fisica Sanitaria, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Rome, Italy
| | | | - Eva Gino
- SC Fisica Sanitaria, A.O. Ordine Mauriziano di Torino, Italy
| | - Nicola Maffei
- Department of Medical Physics, A.O. U. di Modena, Modena, Italy; University of Turin, Post Graduate School in Medical Physics, Turin, Italy
| | - Enrico Menghi
- Medical Physics Department, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Alessandro Savini
- Medical Physics Department, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Antonella Roggio
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Lorenzo Radici
- Ospedale regionale "Umberto Parini" Azienda USL VDA, Fisica Sanitaria, Italy
| | - Elisabetta Cagni
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy; School of Engineering, Cardiff University, Cardiff, Wales, UK
| | | | - Lidia Strigari
- Department of Medical Physics, St. Orsola-Malpighi Hospital, Bologna, Italy
| | | | - Cristina Garibaldi
- IEO, European Institute of Oncology IRCCS, Unit of Medical Physics, Milan, Italy
| | - Chiara Romanò
- IEO, European Institute of Oncology IRCCS, Unit of Medical Physics, Milan, Italy
| | | | | | - Christian Fiandra
- University of Turin, Department of Oncology, Turin, Italy; School of Bioengineering and Medical-Surgical Sciences, Politecnico di Torino, Turin, Italy
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Biau J, Moreau J, Blanchard P, Thariat J, Miroir J, Lapeyre M. Réirradiations des carcinomes épidermoïdes des voies aérodigestives supérieures : indications et résultats. Cancer Radiother 2019; 23:559-564. [DOI: 10.1016/j.canrad.2019.07.147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/08/2019] [Indexed: 01/12/2023]
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Rigaud B, Simon A, Castelli J, Lafond C, Acosta O, Haigron P, Cazoulat G, de Crevoisier R. Deformable image registration for radiation therapy: principle, methods, applications and evaluation. Acta Oncol 2019; 58:1225-1237. [PMID: 31155990 DOI: 10.1080/0284186x.2019.1620331] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.
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Affiliation(s)
- Bastien Rigaud
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Antoine Simon
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Joël Castelli
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Caroline Lafond
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Oscar Acosta
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Pascal Haigron
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center , Houston , TX , USA
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Nobnop W, Chitapanarux I, Wanwilairat S, Tharavichitkul E, Lorvidhaya V, Sripan P. Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT. Technol Cancer Res Treat 2019; 18:1533033818821186. [PMID: 30803375 PMCID: PMC6373993 DOI: 10.1177/1533033818821186] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. METHODS The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses. RESULTS The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration. CONCLUSIONS The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.
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Affiliation(s)
- Wannapha Nobnop
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Imjai Chitapanarux
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Somsak Wanwilairat
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Ekkasit Tharavichitkul
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Vicharn Lorvidhaya
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Patumrat Sripan
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
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Barateau A, Perichon N, Castelli J, Schick U, Henry O, Chajon E, Simon A, Lafond C, De Crevoisier R. A density assignment method for dose monitoring in head-and-neck radiotherapy. Strahlenther Onkol 2018; 195:175-185. [PMID: 30302507 DOI: 10.1007/s00066-018-1379-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 09/26/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND AND PURPOSE During head-and-neck (H&N) radiotherapy, the parotid glands (PGs) may be overdosed; thus, a tool is required to monitor the delivered dose. This study aimed to assess the dose accuracy of a patient-specific density assignment method (DAM) for dose calculation to monitor the dose to PGs during treatment. PATIENTS AND METHODS Forty patients with H&N cancer received an intensity modulated radiation therapy (IMRT), among whom 15 had weekly CTs. Dose distributions were calculated either on the CTs (CTref), on one-class CTs (1C-CT, water), or on three-class CTs (3C-CT, water-air-bone). The inter- and intra-patient DAM uncertainties were evaluated by the difference between doses calculated on CTref and 1C-CTs or 3C-CTs. PG mean dose (Dmean) and spinal cord maximum dose (D2%) were considered. The cumulated dose to the PGs was estimated by the mean Dmean of the weekly CTs. RESULTS The mean (maximum) inter-patient DAM dose uncertainties for the PGs (in cGy) were 23 (75) using 1C-CTs and 12 (50) using 3C-CTs (p ≤ 0.001). For the spinal cord, these uncertainties were 118 (245) and 15 (67; p ≤ 0.001). The mean (maximum) DAM dose uncertainty between cumulated doses calculated on CTs and 3C-CTs was 7 cGy (45 cGy) for the PGs. Considering the difference between the planned and cumulated doses, 53% of the ipsilateral and 80% of the contralateral PGs were overdosed by +3.6 Gy (up to 8.2 Gy) and +1.9 Gy (up to 5.2 Gy), respectively. CONCLUSION The uncertainty of the three-class DAM appears to be clinically non-significant (<0.5 Gy) compared with the PG overdose (up to 8.2 Gy). This DAM could therefore be used to monitor PG doses and trigger replanning.
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Affiliation(s)
- A Barateau
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France.
| | - N Perichon
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - J Castelli
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - U Schick
- Radiotherapy Department, CHU Brest, 29000, Brest, France
| | - O Henry
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - E Chajon
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - A Simon
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - C Lafond
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - R De Crevoisier
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
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Barillot I, Antoni D, Bellec J, Biau J, Giraud P, Jenny C, Lacornerie T, Lisbona A, Marchesi V, Mornex F, Supiot S, Thureau S, Noel G. Bases référentielles de la radiothérapie en conditions stéréotaxiques pour les tumeurs ou métastases bronchopulmonaires, hépatiques, prostatiques, des voies aérodigestives supérieures, cérébrales et osseuses. Cancer Radiother 2018; 22:660-681. [DOI: 10.1016/j.canrad.2018.08.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 07/26/2018] [Accepted: 08/01/2018] [Indexed: 12/12/2022]
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25
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Paganelli C, Meschini G, Molinelli S, Riboldi M, Baroni G. “Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats”. Med Phys 2018; 45:e908-e922. [DOI: 10.1002/mp.13162] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 07/30/2018] [Accepted: 08/24/2018] [Indexed: 12/26/2022] Open
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | - Giorgia Meschini
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | | | - Marco Riboldi
- Department of Medical Physics; Ludwig-Maximilians-Universitat Munchen; Munich 80539 Germany
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
- Centro Nazionale di Adroterapia Oncologica; Pavia 27100 Italy
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26
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Zachiu C, de Senneville BD, Moonen CTW, Raaymakers BW, Ries M. Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration. Phys Med Biol 2018; 63:155016. [PMID: 29972147 DOI: 10.1088/1361-6560/aad109] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Medical imaging is currently employed in the diagnosis, planning, delivery and response monitoring of cancer treatments. Due to physiological motion and/or treatment response, the shape and location of the pathology and organs-at-risk may change over time. Establishing their location within the acquired images is therefore paramount for an accurate treatment delivery and monitoring. A feasible solution for tracking anatomical changes during an image-guided cancer treatment is provided by image registration algorithms. Such methods are, however, often built upon elements originating from the computer vision/graphics domain. Since the original design of such elements did not take into consideration the material properties of particular biological tissues, the anatomical plausibility of the estimated deformations may not be guaranteed. In the current work we adapt two existing variational registration algorithms, namely Horn-Schunck and EVolution, to online soft tissue tracking. This is achieved by enforcing an incompressibility constraint on the estimated deformations during the registration process. The existing and the modified registration methods were comparatively tested against several quality assurance criteria on abdominal in vivo MR and CT data. These criteria included: the Dice similarity coefficient (DSC), the Jaccard index, the target registration error (TRE) and three additional criteria evaluating the anatomical plausibility of the estimated deformations. Results demonstrated that both the original and the modified registration methods have similar registration capabilities in high-contrast areas, with DSC and Jaccard index values predominantly in the 0.8-0.9 range and an average TRE of 1.6-2.0 mm. In contrast-devoid regions of the liver and kidneys, however, the three additional quality assurance criteria have indicated a considerable improvement of the anatomical plausibility of the deformations estimated by the incompressibility-constrained methods. Moreover, the proposed registration models maintain the potential of the original methods for online image-based guidance of cancer treatments.
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Affiliation(s)
- C Zachiu
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, Netherlands
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27
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Ger RB, Yang J, Ding Y, Jacobsen MC, Cardenas CE, Fuller CD, Howell RM, Li H, Stafford RJ, Zhou S, Court LE. Synthetic head and neck and phantom images for determining deformable image registration accuracy in magnetic resonance imaging. Med Phys 2018; 45:10.1002/mp.13090. [PMID: 30007075 PMCID: PMC6331282 DOI: 10.1002/mp.13090] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 05/07/2018] [Accepted: 05/15/2018] [Indexed: 02/01/2023] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images. ACQUISITION AND VALIDATION METHODS Two sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model. DATA FORMAT AND USAGE NOTES The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR. POTENTIAL APPLICATIONS The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.
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Affiliation(s)
- Rachel B. Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Ding
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Megan C. Jacobsen
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carlos E. Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Clifton D. Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rebecca M. Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - R. Jason Stafford
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shouhao Zhou
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Zhang L, Wang Z, Shi C, Long T, Xu XG. The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy. J Appl Clin Med Phys 2018; 19:185-194. [PMID: 29851267 PMCID: PMC6036371 DOI: 10.1002/acm2.12361] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 04/13/2018] [Accepted: 04/21/2018] [Indexed: 12/03/2022] Open
Abstract
Deformable image registration (DIR) is the key process for contour propagation and dose accumulation in adaptive radiation therapy (ART). However, currently, ART suffers from a lack of understanding of “robustness” of the process involving the image contour based on DIR and subsequent dose variations caused by algorithm itself and the presetting parameters. The purpose of this research is to evaluate the DIR caused variations for contour propagation and dose accumulation during ART using the RayStation treatment planning system. Ten head and neck cancer patients were selected for retrospective studies. Contours were performed by a single radiation oncologist and new treatment plans were generated on the weekly CT scans for all patients. For each DIR process, four deformation vector fields (DVFs) were generated to propagate contours and accumulate weekly dose by the following algorithms: (a) ANACONDA with simple presetting parameters, (b) ANACONDA with detailed presetting parameters, (c) MORFEUS with simple presetting parameters, and (d) MORFEUS with detailed presetting parameters. The geometric evaluation considered DICE coefficient and Hausdorff distance. The dosimetric evaluation included D95, Dmax, Dmean, Dmin, and Homogeneity Index. For geometric evaluation, the DICE coefficient variations of the GTV were found to be 0.78 ± 0.11, 0.96 ± 0.02, 0.64 ± 0.15, and 0.91 ± 0.03 for simple ANACONDA, detailed ANACONDA, simple MORFEUS, and detailed MORFEUS, respectively. For dosimetric evaluation, the corresponding Homogeneity Index variations were found to be 0.137 ± 0.115, 0.006 ± 0.032, 0.197 ± 0.096, and 0.006 ± 0.033, respectively. The coherent geometric and dosimetric variations also consisted in large organs and small organs. Overall, the results demonstrated that the contour propagation and dose accumulation in clinical ART were influenced by the DIR algorithm, and to a greater extent by the presetting parameters. A quality assurance procedure should be established for the proper use of a commercial DIR for adaptive radiation therapy.
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Affiliation(s)
- Lian Zhang
- Center of Radiological Medical Physics, University of Science and Technology of China, Hefei, Anhui Province, China
| | - Zhi Wang
- Center of Radiological Medical Physics, University of Science and Technology of China, Hefei, Anhui Province, China.,Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - Chengyu Shi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Tengfei Long
- Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
| | - X George Xu
- Center of Radiological Medical Physics, University of Science and Technology of China, Hefei, Anhui Province, China.,Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, NY, USA
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Monti S, Pacelli R, Cella L, Palma G. Inter-patient image registration algorithms to disentangle regional dose bioeffects. Sci Rep 2018; 8:4915. [PMID: 29559687 PMCID: PMC5861107 DOI: 10.1038/s41598-018-23327-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 03/06/2018] [Indexed: 12/25/2022] Open
Abstract
Radiation therapy (RT) technological advances call for a comprehensive reconsideration of the definition of dose features leading to radiation induced morbidity (RIM). In this context, the voxel-based approach (VBA) to dose distribution analysis in RT offers a radically new philosophy to evaluate local dose response patterns, as an alternative to dose-volume-histograms for identifying dose sensitive regions of normal tissue. The VBA relies on mapping patient dose distributions into a single reference case anatomy which serves as anchor for local dosimetric evaluations. The inter-patient elastic image registrations (EIRs) of the planning CTs provide the deformation fields necessary for the actual warp of dose distributions. In this study we assessed the impact of EIR on the VBA results in thoracic patients by identifying two state-of-the-art EIR algorithms (Demons and B-Spline). Our analysis demonstrated that both the EIR algorithms may be successfully used to highlight subregions with dose differences associated with RIM that substantially overlap. Furthermore, the inclusion for the first time of covariates within a dosimetric statistical model that faces the multiple comparison problem expands the potential of VBA, thus paving the way to a reliable voxel-based analysis of RIM in datasets with strong correlation of the outcome with non-dosimetric variables.
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Affiliation(s)
| | - Roberto Pacelli
- Department of Advanced Biomedical Sciences, "Federico II" University School of Medicine, Napoli, Italy
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy.
| | - Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy
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Rigaud B, Simon A, Gobeli M, Lafond C, Leseur J, Barateau A, Jaksic N, Castelli J, Williaume D, Haigron P, De Crevoisier R. CBCT-guided evolutive library for cervical adaptive IMRT. Med Phys 2018; 45:1379-1390. [DOI: 10.1002/mp.12818] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 12/29/2017] [Accepted: 02/02/2018] [Indexed: 11/09/2022] Open
Affiliation(s)
- Bastien Rigaud
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Antoine Simon
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Maxime Gobeli
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Caroline Lafond
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Julie Leseur
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Anais Barateau
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Nicolas Jaksic
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Joël Castelli
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Danièle Williaume
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
| | - Pascal Haigron
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
| | - Renaud De Crevoisier
- LTSI; Université de Rennes 1; Campus de Beaulieu Rennes F-35042 France
- INSERM; U1099, Campus de Beaulieu Rennes F-35042 France
- Radiotherapy Department; Centre Eugene Marquis; Rennes F-35000 France
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Barateau A, Céleste M, Lafond C, Henry O, Couespel S, Simon A, Acosta O, de Crevoisier R, Périchon N. Calcul de dose de radiothérapie à partir de tomographies coniques : état de l’art. Cancer Radiother 2018; 22:85-100. [PMID: 29276135 DOI: 10.1016/j.canrad.2017.07.050] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 06/06/2017] [Accepted: 07/07/2017] [Indexed: 01/26/2023]
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Chang CS, Shih R, Hwang JM, Chuang KS. Variation assessment of deformable registration in stereotactic radiosurgery. Radiography (Lond) 2018; 24:72-78. [PMID: 29306379 DOI: 10.1016/j.radi.2017.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 05/17/2017] [Accepted: 06/25/2017] [Indexed: 11/15/2022]
Abstract
INTRODUCTION The regular functions of CT-MRI registration include delineation of targets and organs-at-risk (OARs) in radiosurgery planning. The question of whether deformable image registration (DIR) could be applied to stereotactic radiosurgery (SRS) in its place remains a subject of debate. METHODS This study collected data regarding 16 patients who had undergone single-fraction SRS treatment. All lesions were located close to the brainstem. CT and MRI two image sets were registered by both rigid image registration (RIR) and DIR algorithms. The contours of the OARs were drawn individually on the rigid and deformable CT-MRI image sets by qualified radiation oncologists and dosimetrists. The evaluation metrics included volume overlapping (VO), Dice similarity coefficient (DSC), and dose. The modified demons deformable algorithm (VARIAN SmartAdapt) was used for evaluation in this study. RESULTS The mean range of VO for OARs was 0.84 ± 0.08, and DSC was 0.82 ± 0.07. The maximum average volume difference was at normal brain (17.18 ± 14.48 cm3) and the second highest was at brainstem (2.26 cm3 ± 1.18). Pearson correlation testing showed that all DIRs' OAR volumes were linearly and significantly correlated with RIRs' volume (0.679-0.992, two tailed, P << 0.001). The 100% dose was prescribed at gross tumor volume (GTV). The average maximum percent dose difference was observed in brainstem (26.54% ± 27.027), and the average mean dose difference has found at same organ (1.6% ± 1.66). CONCLUSION The change in image-registration method definitely produces dose variance, and is significantly more what depending on the target location. The volume size of OARs, however, was not statistical significantly correlated with dose variance.
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Affiliation(s)
- C-S Chang
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan; Department of Radiation Oncology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan.
| | - R Shih
- Department of Radiation Oncology, New York-Presbyterian Hospital, United States
| | - J-M Hwang
- Department of Radiation Oncology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan; College of Medicine, Tzu Chi University, Hualan, Taiwan
| | - K-S Chuang
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
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Zachiu C, de Senneville BD, Tijssen RHN, Kotte ANTJ, Houweling AC, Kerkmeijer LGW, Lagendijk JJW, Moonen CTW, Ries M. Non-rigid CT/CBCT to CBCT registration for online external beam radiotherapy guidance. ACTA ACUST UNITED AC 2017; 63:015027. [DOI: 10.1088/1361-6560/aa990e] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Nobnop W, Chitapanarux I, Neamin H, Wanwilairat S, Lorvidhaya V, Sanghangthum T. Evaluation of Deformable Image Registration (DIR) Methods for Dose Accumulation in Nasopharyngeal Cancer Patients during Radiotherapy. Radiol Oncol 2017; 51:438-446. [PMID: 29333123 PMCID: PMC5765321 DOI: 10.1515/raon-2017-0033] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Accepted: 07/16/2017] [Indexed: 11/15/2022] Open
Abstract
Introduction Deformable image registration (DIR) is used to modify structures according to anatomical changes for observing the dosimetric effect. In this study, megavoltage computed tomography (MVCT) images were used to generate cumulative doses for nasopharyngeal cancer (NPC) patients by various DIR methods. The performance of the multiple DIR methods was analysed, and the impact of dose accumulation was assessed. Patients and methods The study consisted of five NPC patients treated with a helical tomotherapy unit. The weekly MVCT images at the 1st, 6th, 11th, 16th, 21st, 26th, and 31st fractions were used to assess the dose accumulation by the four DIR methods. The cumulative dose deviations from the initial treatment plan were analysed, and correlations of these variations with the anatomic changes and DIR methods were explored. Results The target dose received a slightly different result from the initial plan at the end of the treatment. The organ dose differences increased as the treatment progressed to 6.8% (range: 2.2 to 10.9%), 15.2% (range: -1.7 to 36.3%), and 6.4% (range: -1.6 to 13.2%) for the right parotid, the left parotid, and the spinal cord, respectively. The mean uncertainty values to estimate the accumulated doses for all the DIR methods were 0.21 ± 0.11 Gy (target dose), 1.99 ± 0.76 Gy (right parotid), 1.19 ± 0.24 Gy (left parotid), and 0.41 ± 0.04 Gy (spinal cord). Conclusions Accuracy of the DIR methods affects the estimation of dose accumulation on both the target dose and the organ dose. The DIR methods provide an adequate dose estimation technique for observation as a result of inter-fractional anatomic changes and are beneficial for adaptive treatment strategies.
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Affiliation(s)
- Wannapha Nobnop
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Imjai Chitapanarux
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
- Imjai Chitapanarux, Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, 110 Intavaroros Rd., Sriphum 50200, Chiang Mai, Thailand. Phone: +66 539 354 56; +66 869 133 065; Fax: +66 539 354 91
| | - Hudsaleark Neamin
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Somsak Wanwilairat
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Vicharn Lorvidhaya
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Taweap Sanghangthum
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Vickress J, Battista J, Barnett R, Yartsev S. Representing the dosimetric impact of deformable image registration errors. Phys Med Biol 2017; 62:N391-N403. [PMID: 28800299 DOI: 10.1088/1361-6560/aa8133] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Deformable image registration (DIR) is emerging as a tool in radiation therapy for calculating the cumulative dose distribution across multiple fractions of treatment. Unfortunately, due to the variable nature of DIR algorithms and dependence of performance on image quality, registration errors can result in dose accumulation errors. In this study, landmarked images were used to characterize the DIR error throughout an image space and determine its impact on dosimetric analysis. Ten thoracic 4DCT images with 300 landmarks per image study matching the end-inspiration and end-expiration phases were obtained from 'dir-labs'. DIR was performed using commercial software MIM Maestro. The range of dose uncertainty (RDU) was calculated at each landmark pair as the maximum and minimum of the doses within a sphere around the landmark in the end-expiration phase. The radius of the sphere was defined by a measure of DIR error which included either the actual DIR error, mean DIR error per study, constant errors of 2 or 5 mm, inverse consistency error, transitivity error or the distance discordance metric (DDM). The RDUs were evaluated using the magnitude of dose uncertainty (MDU) and inclusion rate (IR) of actual error lying within the predicted RDU. The RDU was calculated for 300 landmark pairs on each 4DCT study for all measures of DIR error. The most representative RDU was determined using the actual DIR error with a MDU of 2.5 Gy and IR of 97%. Across all other measures of DIR error, the DDM was most predictive with a MDU of 2.5 Gy and IR of 86%, closest to the actual DIR error. The proposed method represents the range of dosimetric uncertainty of DIR error using either landmarks at specific voxels or measures of registration accuracy throughout the volume.
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Affiliation(s)
- Jason Vickress
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
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Monti S, Palma G, D'Avino V, Gerardi M, Marvaso G, Ciardo D, Pacelli R, Jereczek-Fossa BA, Alterio D, Cella L. Voxel-based analysis unveils regional dose differences associated with radiation-induced morbidity in head and neck cancer patients. Sci Rep 2017; 7:7220. [PMID: 28775281 PMCID: PMC5543173 DOI: 10.1038/s41598-017-07586-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 06/29/2017] [Indexed: 02/04/2023] Open
Abstract
The risk of radiation-induced toxicity in patients treated for head and neck (HN) cancer with radiation therapy (RT) is traditionally estimated by condensing the 3D dose distribution into a monodimensional cumulative dose-volume histogram which disregards information on dose localization. We hypothesized that a voxel-based approach would identify correlations between radiation-induced morbidity and local dose release, thus providing a new insight into spatial signature of radiation sensitivity in composite regions like the HN district. This methodology was applied to a cohort of HN cancer patients treated with RT at risk of radiation-induced acute dysphagia (RIAD). We implemented an inter-patient elastic image registration framework that proved robust enough to match even the most elusive HN structures and to provide accurate dose warping. A voxel-based statistical analysis was then performed to test regional dosimetric differences between patients with and without RIAD. We identified a significantly higher dose delivered to RIAD patients in two voxel clusters in correspondence of the cricopharyngeus muscle and cervical esophagus. Our study goes beyond the well-established organ-based philosophy exploring the relationship between radiation-induced morbidity and local dose differences in the HN region. This approach is generally applicable to different HN toxicity endpoints and is not specific to RIAD.
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Affiliation(s)
| | - Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council (CNR), Naples, Italy
| | - Vittoria D'Avino
- Institute of Biostructures and Bioimaging, National Research Council (CNR), Naples, Italy
| | - Marianna Gerardi
- Department of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Giulia Marvaso
- Department of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Delia Ciardo
- Department of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Roberto Pacelli
- Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy
| | - Barbara A Jereczek-Fossa
- Department of Radiotherapy, European Institute of Oncology, Milan, Italy.,Department of Oncology and Hemato-oncology, University of Milan, Milano, Italy
| | - Daniela Alterio
- Department of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council (CNR), Naples, Italy.
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Nobnop W, Neamin H, Chitapanarux I, Wanwilairat S, Lorvidhaya V, Sanghangthum T. Accuracy of eight deformable image registration (DIR) methods for tomotherapy megavoltage computed tomography (MVCT) images. J Med Radiat Sci 2017; 64:290-298. [PMID: 28755425 PMCID: PMC5715263 DOI: 10.1002/jmrs.236] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 06/07/2017] [Accepted: 06/20/2017] [Indexed: 02/05/2023] Open
Abstract
Introduction The application of deformable image registration (DIR) to megavoltage computed tomography (MVCT) images benefits adaptive radiotherapy. This study aims to quantify the accuracy of DIR for MVCT images when using different deformation methods assessed in a cubic phantom and nasopharyngeal carcinoma (NPC) patients. Methods In the control studies, the DIR accuracy in air‐tissue and tissue‐tissue interface areas was observed using twelve shapes of acrylic and tissue‐equivalent material inserted in the phantom. In the clinical studies, the 1st and 20th fraction MVCT images of seven NPC patients were used to evaluate application of DIR. The eight DIR methods used in the DIRART software varied in (i) transformation framework (asymmetric or symmetric), (ii) DIR registration algorithm (Demons or Optical Flow) and (iii) mapping direction (forward or backward). The accuracy of the methods was compared using an intensity‐based criterion (correlation coefficient, CC) and volume‐based criterion (Dice's similarity coefficient, DSC). Results The asymmetric transformation with Optical Flow showed the best performance for air‐tissue interface areas, with a mean CC and DSC of 0.97 ± 0.03 and 0.79 ± 0.11 respectively. The symmetric transformation with Optical Flow showed good agreement for tissue‐tissue interface areas with a CC of (0.99 ± 0.01) and DSC of (0.89 ± 0.03). The sequences of target domains were significantly different in tissue‐tissue interface areas. Conclusions The deformation method and interface area affected the accuracy of DIR. The validation techniques showed satisfactory volume matching of greater than 0.7 with DSC analysis. The methods can yield acceptable results for clinical applications.
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Affiliation(s)
- Wannapha Nobnop
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand.,Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Hudsaleark Neamin
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Imjai Chitapanarux
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Somsak Wanwilairat
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Vicharn Lorvidhaya
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Taweap Sanghangthum
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Ger RB, Yang J, Ding Y, Jacobsen MC, Fuller CD, Howell RM, Li H, Jason Stafford R, Zhou S, Court LE. Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms. Med Phys 2017. [PMID: 28622410 DOI: 10.1002/mp.12406] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error. METHODS The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error. RESULTS The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, and 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images. CONCLUSIONS The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI.
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Affiliation(s)
- Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Yao Ding
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Megan C Jacobsen
- UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Clifton D Fuller
- UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - R Jason Stafford
- UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Shouhao Zhou
- UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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Penjweini R, Kim MM, Zhu TC. Three-dimensional finite-element based deformable image registration for evaluation of pleural cavity irradiation during photodynamic therapy. Med Phys 2017; 44:3767-3775. [PMID: 28426148 DOI: 10.1002/mp.12284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 04/03/2017] [Accepted: 04/11/2017] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Photodynamic therapy (PDT) is used after surgical resection to treat the microscopic disease for malignant pleural mesothelioma and to increase survival rates. As accurate light delivery is imperative to PDT efficacy, the deformation of the pleural volume during the surgery is studied on its impact on the delivered light fluence. In this study, a three-dimensional finite element-based (3D FEM) deformable image registration is proposed to directly match the volume of lung to the volume of pleural cavity obtained during PDT to have accurate representation of the light fluence accumulated in the lung, heart and liver (organs-at-risk) during treatment. METHODS A wand, comprised of a modified endotrachial tube filled with Intralipid and an optical fiber inside the tube, is used to deliver the treatment light. The position of the treatment is tracked using an optical tracking system with an attachment comprised of nine reflective passive markers that are seen by an infrared camera-based navigation system. This information is used to obtain the surface contours of the plural cavity and the cumulative light fluence on every point of the cavity surface that is being treated. The lung, heart, and liver geometry are also reconstructed from a series of computed tomography (CT) scans of the organs acquired in the same patient before and after the surgery. The contours obtained with the optical tracking system and CTs are imported into COMSOL Multiphysics, where the 3D FEM-based deformable image registration is obtained. The delivered fluence values are assigned to the respective positions (x, y, and z) on the optical tracking contour. The optical tracking contour is considered as the reference, and the CT contours are used as the target, which will be deformed. The data from three patients formed the basis for this study. RESULTS The physical correspondence between the CT and optical tracking geometries, taken at different times, from different imaging devices was established using the 3D FEM-based image deformable registration. The volume of lung was matched to the volume of pleural cavity and the distribution of light fluence on the surface of the heart, liver and deformed lung volumes was obtained. CONCLUSION The method used is appropriate for analyzing problems over complicated domains, such as when the domain changes (as in a solid-state reaction with a moving boundary), when the desired precision varies over the entire domain, or when the solution lacks smoothness. Implementing this method in real-time for clinical applications and in situ monitoring of the under- or over- exposed regions to light during PDT can significantly improve the treatment for mesothelioma.
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Affiliation(s)
- Rozhin Penjweini
- Department of Radiation Oncology, School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michele M Kim
- Department of Radiation Oncology, School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Timothy C Zhu
- Department of Radiation Oncology, School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Li X, Zhang YY, Shi YH, Zhou LH, Zhen X. Evaluation of deformable image registration for contour propagation between CT and cone-beam CT images in adaptive head and neck radiotherapy. Technol Health Care 2017; 24 Suppl 2:S747-55. [PMID: 27259084 DOI: 10.3233/thc-161204] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) to propagate contours between planning computerized tomography (CT) images and treatment CT/Cone-beam CT (CBCT) image to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contours mapping, seven intensity-based DIR strategies are tested on the planning CT and weekly CBCT images from six Head & Neck cancer patients who underwent a 6 ∼ 7 weeks intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e. the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), are employed to measure the agreement between the propagated contours and the physician delineated ground truths. It is found that the performance of all the evaluated DIR algorithms declines as the treatment proceeds. No statistically significant performance difference is observed between different DIR algorithms (p> 0.05), except for the double force demons (DFD) which yields the worst result in terms of DSC and PE. For the metric HD, all the DIR algorithms behaved unsatisfactorily with no statistically significant performance difference (p= 0.273). These findings suggested that special care should be taken when utilizing the intensity-based DIR algorithms involved in this study to deform OAR contours between CT and CBCT, especially for those organs with low contrast.
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Affiliation(s)
- X Li
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Y Y Zhang
- Department of Radiotherapy Oncology, the First Hospital of Jilin University, Changchun, Jilin, China
| | - Y H Shi
- Department of Radiotherapy Oncology, the First Hospital of Jilin University, Changchun, Jilin, China
| | - L H Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - X Zhen
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
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Li X, Zhang Y, Shi Y, Wu S, Xiao Y, Gu X, Zhen X, Zhou L. Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy. PLoS One 2017; 12:e0175906. [PMID: 28414799 PMCID: PMC5393623 DOI: 10.1371/journal.pone.0175906] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 04/02/2017] [Indexed: 01/16/2023] Open
Abstract
Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) for propagating contours between planning computerized tomography (CT) images and treatment CT/cone-beam CT (CBCT) images to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contour mapping, ten intensity-based DIR strategies, which were classified into four categories—optical flow-based, demons-based, level-set-based and spline-based—were tested on planning CT and fractional CBCT images acquired from twenty-one head & neck (H&N) cancer patients who underwent 6~7-week intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e., the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), were employed to measure the agreement between the propagated contours and the physician-delineated ground truths of four OARs, including the vertebra (VTB), the vertebral foramen (VF), the parotid gland (PG) and the submandibular gland (SMG). It was found that the evaluated DIRs in this work did not necessarily outperform rigid registration. DIR performed better for bony structures than soft-tissue organs, and the DIR performance tended to vary for different ROIs with different degrees of deformation as the treatment proceeded. Generally, the optical flow-based DIR performed best, while the demons-based DIR usually ranked last except for a modified demons-based DISC used for CT-CBCT DIR. These experimental results suggest that the choice of a specific DIR algorithm depends on the image modality, anatomic site, magnitude of deformation and application. Therefore, careful examinations and modifications are required before accepting the auto-propagated contours, especially for automatic re-planning ART systems.
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Affiliation(s)
- Xin Li
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuyu Zhang
- Department of Radiotherapy Oncology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yinghua Shi
- Department of Radiotherapy Oncology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Shuyu Wu
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yang Xiao
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Xuejun Gu
- Department of Radiotherapy Oncology, The University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Xin Zhen
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- * E-mail: (XZ); (LZ)
| | - Linghong Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- * E-mail: (XZ); (LZ)
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Teske H, Bartelheimer K, Meis J, Bendl R, Stoiber EM, Giske K. Construction of a biomechanical head and neck motion model as a guide to evaluation of deformable image registration. Phys Med Biol 2017; 62:N271-N284. [PMID: 28350540 DOI: 10.1088/1361-6560/aa69b6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The use of deformable image registration methods in the context of adaptive radiotherapy leads to uncertainties in the simulation of the administered dose distributions during the treatment course. Evaluation of these methods is a prerequisite to decide if a plan adaptation will improve the individual treatment. Current approaches using manual references limit the validity of evaluation, especially for low-contrast regions. In particular, for the head and neck region, the highly flexible anatomy and low soft tissue contrast in control images pose a challenge to image registration and its evaluation. Biomechanical models promise to overcome this issue by providing anthropomorphic motion modelling of the patient. We introduce a novel biomechanical motion model for the generation and sampling of different postures of the head and neck anatomy. Motion propagation behaviour of the individual bones is defined by an underlying kinematic model. This model interconnects the bones by joints and thus is capable of providing a wide range of motion. Triggered by the motion of the individual bones, soft tissue deformation is described by an extended heterogeneous tissue model based on the chainmail approach. This extension, for the first time, allows the propagation of decaying rotations within soft tissue without the necessity for explicit tissue segmentation. Overall motion simulation and sampling of deformed CT scans including a basic noise model is achieved within 30 s. The proposed biomechanical motion model for the head and neck site generates displacement vector fields on a voxel basis, approximating arbitrary anthropomorphic postures of the patient. It was developed with the intention of providing input data for the evaluation of deformable image registration.
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Affiliation(s)
- Hendrik Teske
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
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Simon A, Nassef M, Rigaud B, Cazoulat G, Castelli J, Lafond C, Acosta O, Haigron P, de Crevoisier R. Roles of Deformable Image Registration in adaptive RT: From contour propagation to dose monitoring. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5215-8. [PMID: 26737467 DOI: 10.1109/embc.2015.7319567] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Adaptive radiation therapy (ART) is based on the optimization of the treatment plan during the treatment delivery to compensate for anatomical deformations. Deformable Image Registration (DIR) then constitutes a key step in order to analyze the huge amount of daily or weekly images to provide clinically usefull information. Two main applications of DIR have been developped in ART: delineation propagation and dose accumulation. If delineation propagation is well validated and transfered in the clinic, some challenges remain to address for dose accumulation. In this paper, we review the recent developments of DIR in ART, particularly in prostate and head-and-neck (H&N), with a focus on their evaluation.
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Zhong H, Chetty IJ. Caution Must Be Exercised When Performing Deformable Dose Accumulation for Tumors Undergoing Mass Changes During Fractionated Radiation Therapy. Int J Radiat Oncol Biol Phys 2016; 97:182-183. [PMID: 27979447 DOI: 10.1016/j.ijrobp.2016.09.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 09/06/2016] [Accepted: 09/10/2016] [Indexed: 10/21/2022]
Affiliation(s)
- Hualiang Zhong
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan.
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Saleh Z, Thor M, Apte AP, Sharp G, Tang X, Veeraraghavan H, Muren L, Deasy J. A multiple-image-based method to evaluate the performance of deformable image registration in the pelvis. Phys Med Biol 2016; 61:6172-80. [PMID: 27469495 DOI: 10.1088/0031-9155/61/16/6172] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Deformable image registration (DIR) is essential for adaptive radiotherapy (RT) for tumor sites subject to motion, changes in tumor volume, as well as changes in patient normal anatomy due to weight loss. Several methods have been published to evaluate DIR-related uncertainties but they are not widely adopted. The aim of this study was, therefore, to evaluate intra-patient DIR for two highly deformable organs-the bladder and the rectum-in prostate cancer RT using a quantitative metric based on multiple image registration, the distance discordance metric (DDM). Voxel-by-voxel DIR uncertainties of the bladder and rectum were evaluated using DDM on weekly CT scans of 38 subjects previously treated with RT for prostate cancer (six scans/subject). The DDM was obtained from group-wise B-spline registration of each patient's collection of repeat CT scans. For each structure, registration uncertainties were derived from DDM-related metrics. In addition, five other quantitative measures, including inverse consistency error (ICE), transitivity error (TE), Dice similarity (DSC) and volume ratios between corresponding structures from pre- and post- registered images were computed and compared with the DDM. The DDM varied across subjects and structures; DDMmean of the bladder ranged from 2 to 13 mm and from 1 to 11 mm for the rectum. There was a high correlation between DDMmean of the bladder and the rectum (Pearson's correlation coefficient, R p = 0.62). The correlation between DDMmean and the volume ratios post-DIR was stronger (R p = 0.51; 0.68) than the correlation with the TE (bladder: R p = 0.46; rectum: R p = 0.47), or the ICE (bladder: R p = 0.34; rectum: R p = 0.37). There was a negative correlation between DSC and DDMmean of both the bladder (R p = -0.23) and the rectum (R p = -0.63). The DDM uncertainty metric indicated considerable DIR variability across subjects and structures. Our results show a stronger correlation with volume ratios and with the DSC using DDM compared to using ICE and TE. The DDM has the potential to quantitatively identify regions of large DIR uncertainties and consequently identify anatomical/scan outliers. The DDM can, thus, be applied to improve the adaptive RT process for tumor sites subject to motion.
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Affiliation(s)
- Ziad Saleh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave., New York, NY 10065, USA
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Zhang P, Simon A, Rigaud B, Castelli J, Ospina Arango JD, Nassef M, Henry O, Zhu J, Haigron P, Li B, Shu H, De Crevoisier R. Optimal adaptive IMRT strategy to spare the parotid glands in oropharyngeal cancer. Radiother Oncol 2016; 120:41-7. [PMID: 27372223 DOI: 10.1016/j.radonc.2016.05.028] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 05/21/2016] [Accepted: 05/25/2016] [Indexed: 10/21/2022]
Abstract
PURPOSE In oropharyngeal cancer adaptive radiation therapy (ART), this study aimed to quantify the dosimetric benefit of numerous replanning strategies, defined by various numbers and timings of replannings, with regard to parotid gland (PG) sparing. MATERIAL AND METHODS Thirteen oropharyngeal cancer patients had one planning and then six weekly CT scans during the seven weeks of IMRT. Weekly doses were recalculated without replanning or with replanning to spare the PG. Sixty-three ART scenarios were simulated by considering all the combinations of numbers and timings of replanning. The PG cumulated doses corresponding to "standard" IMRT and ART scenarios were estimated and compared, either by calculating the average of weekly doses or using deformable image registration (DIR). RESULTS Considering average weekly doses, the mean PG overdose using standard IMRT, compared to the planned dose, was 4.1Gy. The mean dosimetric benefit of 6 replannings was 3.3Gy. Replanning at weeks 1, 1-5, 1-2-5, 1-2-4-5 and 1-2-4-5-6 produced the lowest PG mean doses, 94% of the maximum benefit being obtained with 3 replannings. The percentage of patients who had a benefit superior to 5Gy for the contralateral PG was 31% for the three-replannings strategy. The same conclusions were found using DIR. CONCLUSION Early replannings proved the most beneficial for PG sparing, three replannings (weeks 1-2-5), representing an attractive combination for ART in oropharyngeal cancer.
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Affiliation(s)
- Pengcheng Zhang
- National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, People's Republic of China; Université de Rennes 1, LTSI, France; INSERM, U1099, Rennes, France; Centre de Recherche en Information médicale sino-français (CRIBs), Rennes, France
| | - Antoine Simon
- Université de Rennes 1, LTSI, France; INSERM, U1099, Rennes, France; Centre de Recherche en Information médicale sino-français (CRIBs), Rennes, France
| | - Bastien Rigaud
- Université de Rennes 1, LTSI, France; INSERM, U1099, Rennes, France.
| | - Joël Castelli
- Université de Rennes 1, LTSI, France; INSERM, U1099, Rennes, France; Centre Eugene Marquis, Radiotherapy Department, Rennes, France
| | | | - Mohamed Nassef
- Université de Rennes 1, LTSI, France; INSERM, U1099, Rennes, France
| | - Olivier Henry
- Centre Eugene Marquis, Radiotherapy Department, Rennes, France
| | - Jian Zhu
- Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, People's Republic of China; Laboratory of Image Science and Technology, Southeast University, Nanjing, People's Republic of China
| | - Pascal Haigron
- Université de Rennes 1, LTSI, France; INSERM, U1099, Rennes, France; Centre de Recherche en Information médicale sino-français (CRIBs), Rennes, France
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, People's Republic of China; Laboratory of Image Science and Technology, Southeast University, Nanjing, People's Republic of China
| | - Huazhong Shu
- Centre de Recherche en Information médicale sino-français (CRIBs), Rennes, France; Laboratory of Image Science and Technology, Southeast University, Nanjing, People's Republic of China
| | - Renaud De Crevoisier
- Université de Rennes 1, LTSI, France; INSERM, U1099, Rennes, France; Centre de Recherche en Information médicale sino-français (CRIBs), Rennes, France; Centre Eugene Marquis, Radiotherapy Department, Rennes, France.
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Castelli J, Simon A, Rigaud B, Lafond C, Chajon E, Ospina JD, Haigron P, Laguerre B, Loubière AR, Benezery K, de Crevoisier R. A Nomogram to predict parotid gland overdose in head and neck IMRT. Radiat Oncol 2016; 11:79. [PMID: 27278960 PMCID: PMC4898383 DOI: 10.1186/s13014-016-0650-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 05/17/2016] [Indexed: 11/25/2022] Open
Abstract
Purposes To generate a nomogram to predict parotid gland (PG) overdose and to quantify the dosimetric benefit of weekly replanning based on its findings, in the context of intensity-modulated radiotherapy (IMRT) for locally-advanced head and neck carcinoma (LAHNC). Material and methods Twenty LAHNC patients treated with radical IMRT underwent weekly computed tomography (CT) scans during IMRT. The cumulated PG dose was estimated by elastic registration. Early predictors of PG overdose (cumulated minus planned doses) were identified, enabling a nomogram to be generated from a linear regression model. Its performance was evaluated using a leave-one-out method. The benefit of weekly replanning was then estimated for the nomogram-identified PG overdose patients. Results Clinical target volume 70 (CTV70) and the mean PG dose calculated from the planning and first weekly CTs were early predictors of PG overdose, enabling a nomogram to be generated. A mean PG overdose of 2.5Gy was calculated for 16 patients, 14 identified by the nomogram. All patients with PG overdoses >1.5Gy were identified. Compared to the cumulated delivered dose, weekly replanning of these 14 targeted patients enabled a 3.3Gy decrease in the mean PG dose. Conclusion Based on the planning and first week CTs, our nomogram allowed the identification of all patients with PG overdoses >2.5Gy to be identified, who then benefitted from a final 4Gy decrease in mean PG overdose by means of weekly replanning.
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Affiliation(s)
- J Castelli
- Centre Eugene Marquis, Radiotherapy, de la Bataille Flandre Dunkerque, F-35000, Rennes, France. .,Rennes University 1, LTSI, Campus de Beaulieu, Rennes, F-35000, France. .,INSERM, U1099, Campus de Beaulieu, Rennes, F-35000, France.
| | - A Simon
- Rennes University 1, LTSI, Campus de Beaulieu, Rennes, F-35000, France.,INSERM, U1099, Campus de Beaulieu, Rennes, F-35000, France
| | - B Rigaud
- Rennes University 1, LTSI, Campus de Beaulieu, Rennes, F-35000, France.,INSERM, U1099, Campus de Beaulieu, Rennes, F-35000, France
| | - C Lafond
- Centre Eugene Marquis, Radiotherapy, de la Bataille Flandre Dunkerque, F-35000, Rennes, France
| | - E Chajon
- Centre Eugene Marquis, Radiotherapy, de la Bataille Flandre Dunkerque, F-35000, Rennes, France
| | - J D Ospina
- Rennes University 1, LTSI, Campus de Beaulieu, Rennes, F-35000, France.,INSERM, U1099, Campus de Beaulieu, Rennes, F-35000, France
| | - P Haigron
- Rennes University 1, LTSI, Campus de Beaulieu, Rennes, F-35000, France.,INSERM, U1099, Campus de Beaulieu, Rennes, F-35000, France
| | - B Laguerre
- Centre Eugene Marquis, Medical oncology, Rennes, F-35000, France
| | | | - K Benezery
- Centre Antoine Lacassagne, Radiotherapy, Nice, F-06100, France
| | - R de Crevoisier
- Centre Eugene Marquis, Radiotherapy, de la Bataille Flandre Dunkerque, F-35000, Rennes, France.,Rennes University 1, LTSI, Campus de Beaulieu, Rennes, F-35000, France.,INSERM, U1099, Campus de Beaulieu, Rennes, F-35000, France
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Ward G, Ramasamy S, Sykes JR, Prestwich R, Chowdhury F, Scarsbrook A, Murray P, Harris K, Crellin A, Hatfield P, Sebag-Montefiore D, Spezi E, Crosby T, Radhakrishna G. Superiority of Deformable Image Co-registration in the Integration of Diagnostic Positron Emission Tomography-Computed Tomography to the Radiotherapy Treatment Planning Pathway for Oesophageal Carcinoma. Clin Oncol (R Coll Radiol) 2016; 28:655-62. [PMID: 27266819 DOI: 10.1016/j.clon.2016.05.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Revised: 05/02/2016] [Accepted: 05/04/2016] [Indexed: 11/26/2022]
Abstract
AIMS To investigate the use of image co-registration in incorporating diagnostic positron emission tomography-computed tomography (PET-CT) directly into the radiotherapy treatment planning pathway, and to describe the pattern of local recurrence relative to the PET-avid volume. MATERIALS AND METHODS Fourteen patients were retrospectively identified, six of whom had local recurrence. The accuracy of deformable image registration (DIR) and rigid registration of the diagnostic PET-CT and recurrence CT, to the planning CT, were quantitatively assessed by comparing co-registration of oesophagus, trachea and aorta contours. DIR was used to examine the correlation between PET-avid volumes, dosimetry and site of recurrence. RESULTS Positional metrics including the dice similarity coefficient (DSC) and conformity index (CI), showed DIR to be superior to rigid registration in the co-registration of diagnostic and recurrence imaging to the planning CT. For diagnostic PET-CT, DIR was superior to rigid registration in the transfer of oesophagus (DSC=0.75 versus 0.65, P<0.009 and CI=0.59 versus 0.48, P<0.003), trachea (DSC=0.88 versus 0.65, P<0.004 and CI=0.78 versus 0.51, P<0.0001) and aorta structures (DSC=0.93 versus 0.86, P<0.006 and CI=0.86 versus 0.76, P<0.006). For recurrence imaging, DIR was superior to rigid registration in the transfer of trachea (DSC=0.91 versus 0.66, P<0.03 and CI=0.83 versus 0.51, P<0.02) and oesophagus structures (DSC=0.74 versus 0.51, P<0.004 and CI=0.61 versus 0.37, P<0.006) with a non-significant trend for the aorta (DSC=0.91 versus 0.75, P<0.08 and CI=0.83 versus 0.63, P<0.06) structure. A mean inclusivity index of 0.93 (range 0.79-1) showed that the relapse volume was within the planning target volume (PTVPET-CT); all relapses occurred within the high dose region. CONCLUSION DIR is superior to rigid registration in the co-registration of PET-CT and recurrence CT to the planning CT, and can be considered in the direct integration of PET-CT to the treatment planning process. Local recurrences occur within the PTVPET-CT, suggesting that this is a suitable target for dose-escalation strategies.
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Affiliation(s)
- G Ward
- Medical Physics and Engineering, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - S Ramasamy
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - J R Sykes
- Radiation Oncology and Medical Physics, Blacktown Hospital, Blacktown, Australia
| | - R Prestwich
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - F Chowdhury
- Clinical Radiology and Nuclear Medicine, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - A Scarsbrook
- Clinical Radiology and Nuclear Medicine, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - P Murray
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - K Harris
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - A Crellin
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - P Hatfield
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - D Sebag-Montefiore
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - E Spezi
- Biomedical Engineering Research Group, School of Engineering, Cardiff University, Cardiff, UK
| | - T Crosby
- Velindre Cancer Centre, Velindre Hospital, Cardiff, UK
| | - G Radhakrishna
- Radiation Oncology, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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Nassef M, Simon A, Cazoulat G, Duménil A, Blay C, Lafond C, Acosta O, Balosso J, Haigron P, de Crevoisier R. Quantification of dose uncertainties in cumulated dose estimation compared to planned dose in prostate IMRT. Radiother Oncol 2016; 119:129-36. [DOI: 10.1016/j.radonc.2016.03.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 03/02/2016] [Accepted: 03/02/2016] [Indexed: 12/25/2022]
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50
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Gobbo M, Ottaviani G, Rupel K, Ciriello F, Beorchia A, Di Lenarda R, Zacchigna S, Biasotto M. Same strategy for pitfalls of radiotherapy in different anatomical districts. Lasers Med Sci 2016; 31:471-9. [DOI: 10.1007/s10103-015-1857-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 12/15/2015] [Indexed: 01/10/2023]
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