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Adam DP, Grudzinski JJ, Marsh IR, Hill PM, Cho SY, Bradshaw TJ, Longcor J, Burr A, Bruce JY, Harari PM, Bednarz BP. Voxel-Level Dosimetry for Combined Iodine 131 Radiopharmaceutical Therapy and External Beam Radiation Therapy Treatment Paradigms for Head and Neck Cancer. Int J Radiat Oncol Biol Phys 2024; 119:1275-1284. [PMID: 38367914 DOI: 10.1016/j.ijrobp.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/20/2023] [Accepted: 02/08/2024] [Indexed: 02/19/2024]
Abstract
PURPOSE Targeted radiopharmaceutical therapy (RPT) in combination with external beam radiation therapy (EBRT) shows promise as a method to increase tumor control and mitigate potential high-grade toxicities associated with re-treatment for patients with recurrent head and neck cancer. This work establishes a patient-specific dosimetry framework that combines Monte Carlo-based dosimetry from the 2 radiation modalities at the voxel level using deformable image registration (DIR) and radiobiological constructs for patients enrolled in a phase 1 clinical trial combining EBRT and RPT. METHODS AND MATERIALS Serial single-photon emission computed tomography (SPECT)/computed tomography (CT) patient scans were performed at approximately 24, 48, 72, and 168 hours postinjection of 577.2 MBq/m2 (15.6 mCi/m2) CLR 131, an iodine 131-containing RPT agent. Using RayStation, clinical EBRT treatment plans were created with a treatment planning CT (TPCT). SPECT/CT images were deformably registered to the TPCT using the Elastix DIR module in 3D Slicer software and assessed by measuring mean activity concentrations and absorbed doses. Monte Carlo EBRT dosimetry was computed using EGSnrc. RPT dosimetry was conducted using RAPID, a GEANT4-based RPT dosimetry platform. Radiobiological metrics (biologically effective dose and equivalent dose in 2-Gy fractions) were used to combine the 2 radiation modalities. RESULTS The DIR method provided good agreement for the activity concentrations and calculated absorbed dose in the tumor volumes for the SPECT/CT and TPCT images, with a maximum mean absorbed dose difference of -11.2%. Based on the RPT absorbed dose calculations, 2 to 4 EBRT fractions were removed from patient EBRT treatments. For the combined treatment, the absorbed dose to target volumes ranged from 57.14 to 75.02 Gy. When partial volume corrections were included, the mean equivalent dose in 2-Gy fractions to the planning target volume from EBRT + RPT differed -3.11% to 1.40% compared with EBRT alone. CONCLUSIONS This work demonstrates the clinical feasibility of performing combined EBRT + RPT dosimetry on TPCT scans. Dosimetry guides treatment decisions for EBRT, and this work provides a bridge for the same paradigm to be implemented within the rapidly emerging clinical RPT space.
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Affiliation(s)
- David P Adam
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Joseph J Grudzinski
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Ian R Marsh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick M Hill
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Steve Y Cho
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin; University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
| | - Tyler J Bradshaw
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Adam Burr
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin; University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
| | - Justine Y Bruce
- University of Wisconsin Carbone Cancer Center, Madison, Wisconsin; Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Paul M Harari
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin; University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
| | - Bryan P Bednarz
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin.
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Lazzeroni M, Ureba A, Rosenberg V, Schäfer H, Rühle A, Baltas D, Toma-Dasu I, Grosu AL. Evaluating the impact of a rigid and a deformable registration method of pre-treatment images for hypoxia-based dose painting. Phys Med 2024; 122:103376. [PMID: 38772061 DOI: 10.1016/j.ejmp.2024.103376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/19/2024] [Accepted: 05/10/2024] [Indexed: 05/23/2024] Open
Abstract
PURPOSE To assess the impact of rigid and deformable image registration methods (RIR, DIR) on the outcome of a hypoxia-based dose painting strategy. MATERIALS AND METHODS Thirty head and neck cancer patients were imaged with [18F]FMISO-PET/CT before radiotherapy. [18F]FMISO-PET/CT images were registered to the planning-CT by RIR or DIR. The [18F]FMISO uptake was converted into oxygen partial pressure (pO2) maps. Hypoxic Target Volumes were contoured on pO2 maps for the deformed (HTVdef) and non-deformed (HTV) cases. A dose escalation strategy by contours, aiming at 95 % tumour control probability (TCP), was applied. HTVs were characterised based on geometry-related metrics, the underlying pO2 distribution, and the dose boost level. A dosimetric and radiobiological evaluation of selected treatment plans made considering RIR and DIR was performed. Moreover, the TCP of the RIR dose distribution was evaluated when considering the deformed [18F]FMISO-PET image as an indicator of the actual target radiosensitivity to determine the potential impact of an unalignment. RESULTS Statistically significant differences were found between HTV and HTVdef for volume-based metrics and underlying pO2 distribution. Eight out of nine treatment plans for HTV and HTVdef showed differences on the level 10 %/3 mm on a gamma analysis. The TCP difference, however, between RIR and the case when the RIR dose distribution was used with the deformed radiosensitivity map was below 2 pp. CONCLUSIONS Although the choice of the CTplan-to-PET registration method pre-treatment impacts the HTV localisation and morphology and the corresponding dose distribution, it negligibly affects the TCP in the proposed dose escalation strategy by contours.
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Affiliation(s)
- M Lazzeroni
- Department of Physics, Stockholm University, Sweden; Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden.
| | - A Ureba
- Department of Physics, Stockholm University, Sweden; Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden
| | - V Rosenberg
- Royal Institute of Technology (KTH), Stockholm, Sweden
| | - H Schäfer
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Germany
| | - A Rühle
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Germany; University of Leipzig Medical Center, Department of Radiation Oncology, Leipzig, Germany
| | - D Baltas
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Germany
| | - I Toma-Dasu
- Department of Physics, Stockholm University, Sweden; Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden
| | - A L Grosu
- Department of Radiation Oncology, Medical Center, Medical Faculty Freiburg, German Cancer Consortium (DKTK) Partner Site Freiburg, Germany
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Amstutz F, D'Almeida PG, Wu X, Albertini F, Bachtiary B, Weber DC, Unkelbach J, Lomax AJ, Zhang Y. Quantification of deformable image registration uncertainties for dose accumulation on head and neck cancer proton treatments. Phys Med 2024; 122:103386. [PMID: 38805762 DOI: 10.1016/j.ejmp.2024.103386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 03/11/2024] [Accepted: 05/21/2024] [Indexed: 05/30/2024] Open
Abstract
PURPOSE Head and neck cancer (HNC) patients in radiotherapy require adaptive treatment plans due to anatomical changes. Deformable image registration (DIR) is used in adaptive radiotherapy, e.g. for deformable dose accumulation (DDA). However, DIR's ill-posedness necessitates addressing uncertainties, often overlooked in clinical implementations. DIR's further clinical implementation is hindered by missing quantitative commissioning and quality assurance tools. This study evaluates one pathway for more quantitative DDA uncertainties. METHODS For five HNC patients, each with multiple repeated CTs acquired during treatment, a simultaneous-integrated boost (SIB) plan was optimized. Recalculated doses were warped individually using multiple DIRs from repeated to reference CTs, and voxel-by-voxel dose ranges determined an error-bar for DDA. Followed by evaluating, a previously proposed early-stage DDA uncertainty estimation method tested for lung cancer, which combines geometric DIR uncertainties, dose gradients and their directional dependence, in the context of HNC. RESULTS Applying multiple DIRs show dose differences, pronounced in high dose gradient regions. The patient with largest anatomical changes (-13.1 % in ROI body volume), exhibited 33 % maximum uncertainty in contralateral parotid, with 54 % of voxels presenting an uncertainty >5 %. Accumulation over multiple CTs partially mitigated uncertainties. The estimation approach predicted 92.6 % of voxels within ±5 % to the reference dose uncertainty across all patients. CONCLUSIONS DIR variations impact accumulated doses, emphasizing DDA uncertainty quantification's importance for HNC patients. Multiple DIR dose warping aids in quantifying DDA uncertainties. An estimation approach previously described for lung cancer was successfully validated for HNC, for SIB plans, presenting different dose gradients, and for accumulated treatments.
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Affiliation(s)
- Florian Amstutz
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Peter G D'Almeida
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Information Technology & Electrical Engineering, ETH Zurich, Switzerland
| | - Xin Wu
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Information Technology & Electrical Engineering, ETH Zurich, Switzerland
| | | | | | - Damien C Weber
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Radiation Oncology, University Hospital Zurich, Switzerland; Department of Radiation Oncology, University Hospital Bern, Switzerland
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | - Antony J Lomax
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland.
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Meyer S, Alam S, Kuo L, Hu YC, Liu Y, Lu W, Yorke E, Li A, Cervino L, Zhang P. Creating patient-specific digital phantoms with a longitudinal atlas for evaluating deformable CT-CBCT registration in adaptive lung radiotherapy. Med Phys 2024; 51:1405-1414. [PMID: 37449537 PMCID: PMC10787815 DOI: 10.1002/mp.16606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 05/26/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Quality assurance of deformable image registration (DIR) is challenging because the ground truth is often unavailable. In addition, current approaches that rely on artificial transformations do not adequately resemble clinical scenarios encountered in adaptive radiotherapy. PURPOSE We developed an atlas-based method to create a variety of patient-specific serial digital phantoms with CBCT-like image quality to assess the DIR performance for longitudinal CBCT imaging data in adaptive lung radiotherapy. METHODS A library of deformations was created by extracting the longitudinal changes observed between a planning CT and weekly CBCT from an atlas of lung radiotherapy patients. The planning CT of an inquiry patient was first deformed by mapping the deformation pattern from a matched atlas patient, and subsequently appended with CBCT artifacts to imitate a weekly CBCT. Finally, a group of digital phantoms around an inquiry patient was produced to simulate a series of possible evolutions of tumor and adjacent normal structures. We validated the generated deformation vector fields (DVFs) to ensure numerically and physiologically realistic transformations. The proposed framework was applied to evaluate the performance of the DIR algorithm implemented in the commercial Eclipse treatment planning system in a retrospective study of eight inquiry patients. RESULTS The generated DVFs were inverse consistent within less than 3 mm and did not exhibit unrealistic folding. The deformation patterns adequately mimicked the observed longitudinal anatomical changes of the matched atlas patients. Worse Eclipse DVF accuracy was observed in regions of low image contrast or artifacts. The structure volumes exhibiting a DVF error magnitude of equal or more than 2 mm ranged from 24.5% (spinal cord) to 69.2% (heart) and the maximum DVF error exceeded 5 mm for all structures except the spinal cord. Contour-based evaluations showed a high degree of alignment with dice similarity coefficients above 0.8 in all cases, which underestimated the overall DVF accuracy within the structures. CONCLUSIONS It is feasible to create and augment digital phantoms based on a particular patient of interest using multiple series of deformation patterns from matched patients in an atlas. This can provide a semi-automated procedure to complement the quality assurance of CT-CBCT DIR and facilitate the clinical implementation of image-guided and adaptive radiotherapy that involve longitudinal CBCT imaging studies.
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Affiliation(s)
- Sebastian Meyer
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sadegh Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - LiCheng Kuo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yilin Liu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ellen Yorke
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Anyi Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Archawametheekul K, Puttanawarut C, Suphaphong S, Jiarpinitnun C, Sakulsingharoj S, Stansook N, Khachonkham S. The Investigating Image Registration Accuracy and Contour Propagation for Adaptive Radiotherapy Purposes in Line with the Task Group No. 132 Recommendation. J Med Phys 2024; 49:64-72. [PMID: 38828076 PMCID: PMC11141753 DOI: 10.4103/jmp.jmp_168_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/12/2024] [Accepted: 02/12/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose Image registration is a crucial component of the adaptive radiotherapy workflow. This study investigates the accuracy of the deformable image registration (DIR) and contour propagation features of SmartAdapt, an application in the Eclipse treatment planning system (TPS) version 16.1. Materials and Methods The registration accuracy was validated using the Task Group No. 132 (TG-132) virtual phantom, which features contour evaluation and landmark analysis based on the quantitative criteria recommended in the American Association of Physicists in Medicine TG-132 report. The target registration error, Dice similarity coefficient (DSC), and center of mass displacement were used as quantitative validation metrics. The performance of the contour propagation feature was evaluated using clinical datasets (head and neck, pelvis, and chest) and an additional four-dimensional computed tomography (CT) dataset from TG-132. The primary planning and the second CT images were appropriately registered and deformed. The DSC was used to find the volume overlapping between the deformed contours and the radiation oncologist (RO)-drawn contour. The clinical value of the DIR-generated structure was reviewed and scored by an experienced RO to make a qualitative assessment. Results The registration accuracy fell within the specified tolerances. SmartAdapt exhibited a reasonably propagated contour for the chest and head-and-neck regions, with DSC values of 0.80 for organs at risk. Misregistration is frequently observed in the pelvic region, which is specified as a low-contrast region. However, 78% of structures required no modification or minor modification, demonstrating good agreement between contour comparison and the qualitative analysis. Conclusions SmartAdapt has adequate efficiency for image registration and contour propagation for adaptive purposes in various anatomical sites. However, there should be concern about its performance in regions with low contrast and small volumes.
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Affiliation(s)
- Kamonchanok Archawametheekul
- Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Chanon Puttanawarut
- Chakri Naruebodindra Medical Institute, Mahidol University, Samut Prakan, Thailand
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Sithiphong Suphaphong
- Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Chuleeporn Jiarpinitnun
- Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Siwaporn Sakulsingharoj
- Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Nauljun Stansook
- Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Suphalak Khachonkham
- Division of Radiation Oncology, Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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Bosma LS, Ries M, Denis de Senneville B, Raaymakers BW, Zachiu C. Integration of operator-validated contours in deformable image registration for dose accumulation in radiotherapy. Phys Imaging Radiat Oncol 2023; 27:100483. [PMID: 37664798 PMCID: PMC10472292 DOI: 10.1016/j.phro.2023.100483] [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: 04/17/2023] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 09/05/2023] Open
Abstract
Background and Purpose Deformable image registration (DIR) is a core element of adaptive radiotherapy workflows, integrating daily contour propagation and/or dose accumulation in their design. Propagated contours are usually manually validated and may be edited, thereby locally invalidating the registration result. This means the registration cannot be used for dose accumulation. In this study we proposed and evaluated a novel multi-modal DIR algorithm that incorporated contour information to guide the registration. This integrates operator-validated contours with the estimated deformation vector field and warped dose. Materials and Methods The proposed algorithm consisted of both a normalized gradient field-based data-fidelity term on the images and an optical flow data-fidelity term on the contours. The Helmholtz-Hodge decomposition was incorporated to ensure anatomically plausible deformations. The algorithm was validated for same- and cross-contrast Magnetic Resonance (MR) image registrations, Computed Tomography (CT) registrations, and CT-to-MR registrations for different anatomies, all based on challenging clinical situations. The contour-correspondence, anatomical fidelity, registration error, and dose warping error were evaluated. Results The proposed contour-guided algorithm considerably and significantly increased contour overlap, decreasing the mean distance to agreement by a factor of 1.3 to 13.7, compared to the best algorithm without contour-guidance. Importantly, the registration error and dose warping error decreased significantly, by a factor of 1.2 to 2.0. Conclusions Our contour-guided algorithm ensured that the deformation vector field and warped quantitative information were consistent with the operator-validated contours. This provides a feasible semi-automatic strategy for spatially correct warping of quantitative information even in difficult and artefacted cases.
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Affiliation(s)
- Lando S Bosma
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | - Mario Ries
- Imaging Division, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | - Baudouin Denis de Senneville
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
- Institut de Mathématiques de Bordeaux (IMB), UMR 5251 CNRS/University of Bordeaux, F-33400 Talence, France
| | - Bas W Raaymakers
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | - Cornel Zachiu
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
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Lallement A, Noblet V, Antoni D, Meyer P. Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs). Technol Health Care 2023:THC220519. [PMID: 36776082 DOI: 10.3233/thc-220519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
BACKGROUND Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. OBJECTIVE This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). METHODS The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. RESULTS The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. CONCLUSION The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.
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Affiliation(s)
| | | | - Delphine Antoni
- Department of Radiation Therapy, Institut de Cancérologie de Strasbourg, Strasbourg, France
| | - Philippe Meyer
- ICube-UMR 7357, Strasbourg, France.,Department of Medical Physics, Institut de Cancérologie de Strasbourg, Strasbourg, France
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Lee D, Alam S, Jiang J, Cervino L, Hu YC, Zhang P. Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy. Med Phys 2023; 50:970-979. [PMID: 36303270 PMCID: PMC10388694 DOI: 10.1002/mp.16026] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/25/2022] [Accepted: 10/02/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART). METHODS To address the unique needs of ART, we designed Seq2Morph, a novel deep learning-based deformable image registration (DIR) network. Seq2Morph was built upon VoxelMorph which is a general-purpose framework for learning-based image registration. The major upgrades are (1) expansion of inputs to all weekly cone-beam computed tomography (CBCTs) acquired for monitoring treatment responses throughout a radiotherapy course, for registration to their planning CT; (2) incorporation of 3D convolutional long short-term memory between the encoder and decoder of VoxelMorph, to parse the temporal patterns of anatomical changes; and (3) addition of bidirectional pathways to calculate and minimize inverse consistency errors (ICEs). Longitudinal image sets from 50 patients, including a planning CT and 6 weekly CBCTs per patient, were utilized for network training and cross-validation. The outputs were deformation vector fields for all the registration pairs. The loss function was composed of a normalized cross-correlation for image intensity similarity, a DICE for contour similarity, an ICE, and a deformation regularization term. For performance evaluation, DICE and Hausdorff distance (HD) for the manual versus predicted contours of tumor and esophagus on weekly basis were quantified and further compared with other state-of-the-art algorithms, including conventional VoxelMorph and large deformation diffeomorphic metric mapping (LDDMM). RESULTS Visualization of the hidden states of Seq2Morph revealed distinct spatiotemporal anatomy change patterns. Quantitatively, Seq2Morph performed similarly to LDDMM, but significantly outperformed VoxelMorph as measured by GTV DICE: (0.799±0.078, 0.798±0.081, and 0.773±0.078), and 50% HD (mm): (0.80±0.57, 0.88±0.66, and 0.95±0.60). The per-patient inference of Seq2Morph took 22 s, much less than LDDMM (∼30 min). CONCLUSIONS Seq2Morph can provide accurate and fast DIR for longitudinal image studies by exploiting spatial-temporal patterns. It closely matches the clinical workflow and has the potential to serve both online and offline ART.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Sadegh Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
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Wahlstedt I, George Smith A, Andersen CE, Behrens CP, Nørring Bekke S, Boye K, van Overeem Felter M, Josipovic M, Petersen J, Risumlund SL, Tascón-Vidarte JD, van Timmeren JE, Vogelius IR. Interfractional dose accumulation for MR-guided liver SBRT: Variation among algorithms is highly patient- and fraction-dependent. Radiother Oncol 2022; 182:109448. [PMID: 36566988 DOI: 10.1016/j.radonc.2022.109448] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/22/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND PURPOSE Daily plan adaptations could take the dose delivered in previous fractions into account. Due to high dose delivered per fraction, low number of fractions, steep dose gradients, and large interfractional organ deformations, this might be particularly important for liver SBRT. This study investigates inter-algorithm variation of interfractional dose accumulation for MR-guided liver SBRT. MATERIALS AND METHODS We assessed 27 consecutive MR-guided liver SBRT treatments of 67.5 Gy in three (n = 15) or 50 Gy in five fractions (n = 12), both prescribed to the GTV. We calculated fraction doses on daily patient anatomy, warped these doses to the simulation MRI using seven different algorithms, and accumulated the warped doses. Thus, we obtained differences in planned doses and warped or accumulated doses for each algorithm. This enabled us to calculate the inter-algorithm variations in warped doses per fraction and in accumulated doses per treatment course. RESULTS The four intensity-based algorithms were more consistent with planned PTV dose than affine or contour-based algorithms. The mean (range) variation of the dose difference for PTV D95% due to dose warping by these intensity-based algorithms was 10.4 percentage points (0.3 to 43.7) between fractions and 8.6 (0.3 to 24.9) between accumulated treatment doses. As seen by these ranges, the variation was very dependent on the patient and the fraction being analyzed. Nevertheless, no correlations between patient or plan characteristics on the one hand and inter-algorithm dose warping variation on the other hand was found. CONCLUSION Inter-algorithm dose accumulation variation is highly patient- and fraction-dependent for MR-guided liver SBRT. We advise against trusting a single algorithm for dose accumulation in liver SBRT.
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Affiliation(s)
- Isak Wahlstedt
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, Bygning 101A, 2800 Kongens Lyngby, Denmark; Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark.
| | - Abraham George Smith
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
| | - Claus Erik Andersen
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, Bygning 101A, 2800 Kongens Lyngby, Denmark
| | - Claus Preibisch Behrens
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, Bygning 101A, 2800 Kongens Lyngby, Denmark; Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark
| | - Susanne Nørring Bekke
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark
| | - Kristian Boye
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Mette van Overeem Felter
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte (HGH), Borgmester Ib Juuls Vej 7, 2730 Herlev, Denmark
| | - Mirjana Josipovic
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Jens Petersen
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
| | - Signe Lenora Risumlund
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - José David Tascón-Vidarte
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
| | | | - Ivan Richter Vogelius
- Department of Oncology, Centre for Cancer and Organ Diseases, Copenhagen University Hospital - Rigshospitalet (RH), Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
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10
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Delpon G, Barateau A, Beneux A, Bessières I, Latorzeff I, Welmant J, Tallet A. [What do we need to deliver "online" adapted radiotherapy treatment plans?]. Cancer Radiother 2022; 26:794-802. [PMID: 36028418 DOI: 10.1016/j.canrad.2022.06.024] [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: 06/22/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022]
Abstract
During the joint SFRO/SFPM session of the 2019 congress, a state of the art of adaptive radiotherapy announced a strong impact in our clinical practice, in particular with the availability of treatment devices coupled to an MRI system. Three years later, it seems relevant to take stock of adaptive radiotherapy in practice, and especially the "online" strategy because it is indeed more and more accessible with recent hardware and software developments, such as coupled accelerators to a three-dimensional imaging device and algorithms based on artificial intelligence. However, the deployment of this promising strategy is complex because it contracts the usual time scale and upsets the usual organizations. So what do we need to deliver adapted treatment plans with an "online" strategy?
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Affiliation(s)
- G Delpon
- Institut de cancérologie de l'Ouest, Saint-Herblain et IMT Atlantique, Nantes université, CNRS/IN2P3, Subatech, Nantes, France.
| | - A Barateau
- Université Rennes, CLCC Eugène-Marquis, Inserm, LTSI-UMR 1099, Rennes, France
| | - A Beneux
- Hospices Civils de Lyon, Lyon, France
| | - I Bessières
- Centre Georges-François Leclerc, Dijon, France
| | | | - J Welmant
- Institut du cancer de Montpellier, Montpellier, France
| | - A Tallet
- Institut Paoli-Calmettes, Marseille, France
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11
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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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12
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Archibald-Heeren B. ACPSEM position paper on the clinical implementation of image registration. Phys Eng Sci Med 2022; 45:419-420. [PMID: 35653035 DOI: 10.1007/s13246-022-01133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2022] [Indexed: 10/18/2022]
Affiliation(s)
- Ben Archibald-Heeren
- Icon Cancer Centres, Concord, NSW, 2139, Australia. .,Chair of MIRSIG, Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM), Sydney, Australia.
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13
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Lowther N, Louwe R, Yuen J, Hardcastle N, Yeo A, Jameson M. MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy. Phys Eng Sci Med 2022; 45:421-428. [PMID: 35522369 PMCID: PMC9239966 DOI: 10.1007/s13246-022-01125-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2022] [Indexed: 12/12/2022]
Abstract
The report of the American Association of Physicists in Medicine (AAPM) Task Group No. 132 published in 2017 reviewed rigid image registration and deformable image registration (DIR) approaches and solutions to provide recommendations for quality assurance and quality control of clinical image registration and fusion techniques in radiotherapy. However, that report did not include the use of DIR for advanced applications such as dose warping or warping of other matrices of interest. Considering that DIR warping tools are now readily available, discussions were hosted by the Medical Image Registration Special Interest Group (MIRSIG) of the Australasian College of Physical Scientists & Engineers in Medicine in 2018 to form a consensus on best practice guidelines. This position statement authored by MIRSIG endorses the recommendations of the report of AAPM task group 132 and expands on the best practice advice from the ‘Deforming to Best Practice’ MIRSIG publication to provide guidelines on the use of DIR for advanced applications.
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Affiliation(s)
- Nicholas Lowther
- Department of Radiation Oncology, Wellington Blood and Cancer Centre, Wellington, New Zealand
| | - Rob Louwe
- Holland Proton Therapy Centre, Delft, Netherlands
| | - Johnson Yuen
- St George Hospital Cancer Care Centre, Kogarah, New South Wales, 2217, Australia.,South Western Clinical School, University of New South Wales, Sydney, Australia.,Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia.,The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Adam Yeo
- Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,School of Applied Sciences, RMIT University, Melbourne, VIC, Australia
| | - Michael Jameson
- GenesisCare, Sydney, NSW, 2015, Australia. .,St Vincent's Clinical School, University of New South Wales, Sydney, Australia.
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14
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Omari EA, Zhang Y, Ahunbay E, Paulson E, Amjad A, Chen X, Liang Y, Li XA. Multi parametric magnetic resonance imaging for radiation treatment planning. Med Phys 2022; 49:2836-2845. [PMID: 35170769 DOI: 10.1002/mp.15534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/05/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022] Open
Abstract
In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing and QA strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Eenas A Omari
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Xinfeng Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Liang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
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15
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Koide Y, Shimizu H, Miyauchi R, Haimoto S, Tanaka H, Watanabe Y, Adachi S, Kato D, Aoyama T, Kitagawa T, Tachibana H, Kodaira T. Fully automated rigid image registration versus human registration in postoperative spine stereotactic body radiation therapy: a multicenter non-inferiority study. JOURNAL OF RADIATION RESEARCH 2022; 63:115-121. [PMID: 34927197 PMCID: PMC8776699 DOI: 10.1093/jrr/rrab113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 10/07/2021] [Accepted: 11/01/2021] [Indexed: 06/14/2023]
Abstract
To confirm the fully automated rigid image registration (A-RIR) accuracy in postoperative spine stereotactic body radiation therapy (SBRT), we conducted a multicenter non-inferiority study compared to the human rigid image registration (H-RIR). Twenty-eight metastatic cancer patients who underwent postoperative spine SBRT are enrolled-image registration (IR) of planning computed tomography (CT) and CT-myelogram for delineating the spinal cord. The adopted A-RIR workflow is a contour-focused algorithm performing a rigid registration by maximizing normalized mutual information (NMI) restricted to the data contained within the automatically extracted contour. Three radiation oncologists (ROs) from multicenters were prompted to review two blinded registrations and choose one for clinical use. Indistinguishable cases were allowed to vote equivalent, counted A-RIR side. A-RIR is considered non-inferior to H-RIR if the lower limit of the 95% confidence interval (CI) of A-RIR preferable/equivalent is greater than 0.45. We also evaluated the NMI improvement from the baseline and the translational/rotational errors between A-RIR and H-RIR. The A-RIR preferable/equivalent was selected in 21 patients (0.75, 95% CI: 0.55-0.89), demonstrating non-inferiority to H-RIR. The A-RIR's NMI improvement was greater than H-RIR in 24 patients: the mean value ± SD was 0.225 ± 0.115 in A-RIR and 0.196 ± 0.114 in H-RIR (P < 0.001). The absolute translational error was 0.38 ± 0.31 mm. The rotational error was -0.03 ± 0.20, 0.05 ± 0.19, -0.04 ± 0.20 degrees in axial, coronal, and sagittal planes (range: -0.66-0.52). In conclusion, A-RIR shows non-inferior to H-RIR in CT and CT-myelogram registration for postoperative spine SBRT planning.
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Affiliation(s)
- Yutaro Koide
- Corresponding author. Department of Radiation Oncology, Aichi Cancer Center, Kanokoden 1–1, Chikusa-ku, Nagoya, Aichi, Japan. . Tel: (052) 762-6111
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16
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Cloutier E, Beaulieu L, Archambault L. Deformable scintillation dosimeter: II. Real-time simultaneous measurements of dose and tracking of deformation vector fields. Phys Med Biol 2021; 66. [PMID: 34380121 DOI: 10.1088/1361-6560/ac1ca2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 08/11/2021] [Indexed: 12/29/2022]
Abstract
Anatomical motion and deformation pose challenges to the understanding of the delivered dose distribution during radiotherapy treatments. Hence, deformable image registration (DIR) algorithms are increasingly used to map contours and dose distributions from one image set to another. However, the lack of validation tools slows their clinical adoption, despite their commercial availability. This work presents a novel water-equivalent deformable dosimeter that simultaneously measures the dose distribution and tracks deformation vector fields (DVF). The dosimeter in made of an array of 19 scintillating fiber detectors embedded in a cylindrical elastomer matrix. It is imaged by two pairs of stereoscopic cameras tracking the position and angulation of the scintillators, while measuring the dose. The resulting system provides a precision of 0.3 mm on DVF measurements. The dosimeter was irradiated with 5 × 3, 4 × 3 and 3 × 3 cm26 MV photon beams in both fixed and deformed conditions. The measured DVF was compared to the one computed with a DIR algorithm (Plastimatch). The deviations between the computed and measured DVFs was below 1.5 mm. As for dose measurements, the dosimeter acquired the dose distribution in fixed and deformed conditions within 1% of the treatment planning system calculation and complementary dose validation using the Hyperscint dosimetry system. Using the demonstrated qualities of scintillating detectors, we developed a real-time, water-equivalent deformable dosimeter. Given it's sensor tracking position precision and dose measurements accuracy, the developed detector is a promising tools for the validation of DIR algorithms as well as dose distribution measurements under fixed and deformed conditions.
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Affiliation(s)
- E Cloutier
- Service de physique médicale et Axe Oncologie du Centre de recherche, CHU de Québec-Université Laval, Canada.,Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Canada
| | - L Beaulieu
- Service de physique médicale et Axe Oncologie du Centre de recherche, CHU de Québec-Université Laval, Canada.,Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Canada
| | - L Archambault
- Service de physique médicale et Axe Oncologie du Centre de recherche, CHU de Québec-Université Laval, Canada.,Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Canada
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17
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Hussein M, Akintonde A, McClelland J, Speight R, Clark CH. Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools. Br J Radiol 2021; 94:20210001. [PMID: 33882253 PMCID: PMC8173691 DOI: 10.1259/bjr.20210001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/06/2021] [Accepted: 04/12/2021] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE The aim of this study was to evaluate the current status of the clinical use of deformable image registration (DIR) in radiotherapy and to gain an understanding of the challenges faced by centres in clinical implementation of DIR, including commissioning and quality assurance (QA), and to determine the barriers faced. The goal was to inform whether additional guidance and QA tools were needed. METHODS A survey focussed on clinical use, metrics used, how centres would like to use DIR in the future and challenges faced, was designed and sent to 71 radiotherapy centres in the UK. Data were gathered specifically on which centres we using DIR clinically, which applications were being used, what commissioning and QA tests were performed, and what barriers were preventing the integration of DIR into the clinical workflow. Centres that did not use DIR clinically were encouraged to fill in the survey and were asked if they have any future plans and in what timescale. RESULTS 51 out of 71 (70%) radiotherapy centres responded. 47 centres reported access to a commercial software that could perform DIR. 20 centres already used DIR clinically, and 22 centres had plans to implement an application of DIR within 3 years of the survey. The most common clinical application of DIR was to propagate contours from one scan to another (19 centres). In each of the applications, the types of commissioning and QA tests performed varied depending on the type of application and between centres. Some of the key barriers were determining when a DIR was satisfactory including which metrics to use, and lack of resources. CONCLUSION The survey results highlighted that there is a need for additional guidelines, training, better tools for commissioning DIR software and for the QA of registration results, which should include developing or recommending which quantitative metrics to use. ADVANCES IN KNOWLEDGE This survey has given a useful picture of the clinical use and lack of use of DIR in UK radiotherapy centres. The survey provided useful insight into how centres commission and QA DIR applications, especially the variability among centres. It was also possible to highlight key barriers to implementation and determine factors that may help overcome this which include the need for additional guidance specific to different applications, better tools and metrics.
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Affiliation(s)
- Mohammad Hussein
- Metrology for Medical Physics Centre, National Physical Laboratory, Teddington, UK
| | - Adeyemi Akintonde
- Centre for Medical Image Computing, University College London, London, UK
| | - Jamie McClelland
- Centre for Medical Image Computing, University College London, London, UK
| | - Richard Speight
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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