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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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Barten DLJ, van Kesteren Z, Laan JJ, Dassen MG, Westerveld GH, Pieters BR, de Jonge CS, Stoker J, Bel A. Precision assessment of bowel motion quantification using 3D cine-MRI for radiotherapy. Phys Med Biol 2024; 69:04NT01. [PMID: 38232395 DOI: 10.1088/1361-6560/ad1f89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 01/17/2024] [Indexed: 01/19/2024]
Abstract
Objective. The bowel is an important organ at risk for toxicity during pelvic and abdominal radiotherapy. Identifying regions of high and low bowel motion with MRI during radiotherapy may help to understand the development of bowel toxicity, but the acquisition time of MRI is rather long. The aim of this study is to retrospectively evaluate the precision of bowel motion quantification and to estimate the minimum MRI acquisition time.Approach. We included 22 gynaecologic cancer patients receiving definitive radiotherapy with curative intent. The 10 min pre-treatment 3D cine-MRI scan consisted of 160 dynamics with an acquisition time of 3.7 s per volume. Deformable registration of consecutive images generated 159 deformation vector fields (DVFs). We defined two motion metrics, the 50th percentile vector lengths (VL50) of the complete set of DVFs was used to measure median bowel motion. The 95th percentile vector lengths (VL95) was used to quantify high motion of the bowel. The precision of these metrics was assessed by calculating their variation (interquartile range) in three different time frames, defined as subsets of 40, 80, and 120 consecutive images, corresponding to acquisition times of 2.5, 5.0, and 7.5 min, respectively.Main results. For the full 10 min scan, the minimum motion per frame of 50% of the bowel volume (M50%) ranged from 0.6-3.5 mm for the VL50 motion metric and 2.3-9.0 mm for the VL95 motion metric, across all patients. At 7.5 min scan time, the variation in M50% was less than 0.5 mm in 100% (VL50) and 95% (VL95) of the subsets. A scan time of 5.0 and 2.5 min achieved a variation within 0.5 mm in 95.2%/81% and 85.7%/57.1% of the subsets, respectively.Significance. Our 3D cine-MRI technique quantifies bowel loop motion with 95%-100% confidence with a precision of 0.5 mm variation or less, using a 7.5 min scan time.
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Affiliation(s)
- D L J Barten
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
| | - Z van Kesteren
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
| | - J J Laan
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
| | - M G Dassen
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - G H Westerveld
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus University Medical Center, Department of Radiation Oncology, Rotterdam, The Netherlands
| | - B R Pieters
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - C S de Jonge
- Amsterdam UMC location University of Amsterdam, Department of Radiology and Nuclear Medicine, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
| | - J Stoker
- Amsterdam UMC location University of Amsterdam, Department of Radiology and Nuclear Medicine, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
| | - A Bel
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
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Nenoff L, Amstutz F, Murr M, Archibald-Heeren B, Fusella M, Hussein M, Lechner W, Zhang Y, Sharp G, Vasquez Osorio E. Review and recommendations on deformable image registration uncertainties for radiotherapy applications. Phys Med Biol 2023; 68:24TR01. [PMID: 37972540 PMCID: PMC10725576 DOI: 10.1088/1361-6560/ad0d8a] [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: 04/11/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.
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Affiliation(s)
- Lena Nenoff
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
- OncoRay—National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden—Rossendorf, Dresden Germany
- Helmholtz-Zentrum Dresden—Rossendorf, Institute of Radiooncology—OncoRay, Dresden, Germany
| | - Florian Amstutz
- Department of Physics, ETH Zurich, Switzerland
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Mohammad Hussein
- Metrology for Medical Physics, National Physical Laboratory, Teddington, United Kingdom
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Greg Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
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Ding Y, Feng H, Yang Y, Holmes J, Liu Z, Liu D, Wong WW, Yu NY, Sio TT, Schild SE, Li B, Liu W. Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer. Med Phys 2023; 50:6864-6880. [PMID: 37289193 PMCID: PMC10704004 DOI: 10.1002/mp.16548] [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: 11/26/2022] [Revised: 04/20/2023] [Accepted: 05/24/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application. PURPOSE A deep-learning-based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. METHODS: A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre-trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per-voxel absolute CT-number-difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively. RESULTS The two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per-voxel absolute CT-number-difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5 , within ±0.06% for total lung V5 , ≤1.5cGy[RBE] for heart and esophagus Dmean , and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed. CONCLUSION A deep neural network-based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.
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Affiliation(s)
- Yuzhen Ding
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Yunze Yang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Zhengliang Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - David Liu
- Athens Academy, Athens, GA 30602, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Nathan Y. Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Terence T. Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Steven E. Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Baoxin Li
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA 85281
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
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5
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Zhong H, Garcia-Alvarez JA, Kainz K, Tai A, Ahunbay E, Erickson B, Schultz CJ, Li XA. Development of a multi-layer quality assurance program to evaluate the uncertainty of deformable dose accumulation in adaptive radiotherapy. Med Phys 2023; 50:1766-1778. [PMID: 36434751 PMCID: PMC10033340 DOI: 10.1002/mp.16137] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 09/10/2022] [Accepted: 11/15/2022] [Indexed: 11/27/2022] Open
Abstract
PURPOSE Deformable dose accumulation (DDA) has uncertainties which impede the implementation of DDA-based adaptive radiotherapy (ART) in clinic. The purpose of this study is to develop a multi-layer quality assurance (MLQA) program to evaluate uncertainties in DDA. METHODS A computer program is developed to generate a pseudo-inverse displacement vector field (DVF) for each deformable image registration (DIR) performed in Accuray's PreciseART. The pseudo-inverse DVF is first used to calculate a pseudo-inverse consistency error (PICE) and then implemented in an energy and mass congruent mapping (EMCM) method to reconstruct a deformed dose. The PICE is taken as a metric to estimate DIR uncertainties. A pseudo-inverse dose agreement rate (PIDAR) is used to evaluate the consequence of the DIR uncertainties in DDA and the principle of energy conservation is used to validate the integrity of dose mappings. The developed MLQA program was tested using the data collected from five representative cancer patients treated with tomotherapy. RESULTS DIRs were performed in PreciseART to generate primary DVFs for the five patients. The fidelity index and PICE of these DVFs on average are equal to 0.028 mm and 0.169 mm, respectively. With the criteria of 3 mm/3% and 5 mm/5%, the PIDARs of the PreciseART-reconstructed doses are 73.9 ± 4.4% and 87.2 ± 3.3%, respectively. The PreciseART and EMCM-based dose reconstructions have their deposited energy changed by 5.6 ± 3.9% and 2.6 ± 1.5% in five GTVs, and by 9.2 ± 7.8% and 4.7 ± 3.6% in 30 OARs, respectively. CONCLUSIONS A pseudo-inverse map-based EMCM program has been developed to evaluate DIR and dose mapping uncertainties. This program could also be used as a sanity check tool for DDA-based ART.
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Affiliation(s)
- Hualiang Zhong
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Kristofer Kainz
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - An Tai
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
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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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7
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A novel edge gradient distance metric for automated evaluation of deformable image registration quality. Phys Med 2022; 103:26-36. [DOI: 10.1016/j.ejmp.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 07/28/2022] [Accepted: 09/26/2022] [Indexed: 11/17/2022] Open
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Bierbrier J, Gueziri HE, Collins DL. Estimating medical image registration error and confidence: A taxonomy and scoping review. Med Image Anal 2022; 81:102531. [PMID: 35858506 DOI: 10.1016/j.media.2022.102531] [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: 10/28/2021] [Revised: 06/16/2022] [Accepted: 07/01/2022] [Indexed: 11/18/2022]
Abstract
Given that image registration is a fundamental and ubiquitous task in both clinical and research domains of the medical field, errors in registration can have serious consequences. Since such errors can mislead clinicians during image-guided therapies or bias the results of a downstream analysis, methods to estimate registration error are becoming more popular. To give structure to this new heterogenous field we developed a taxonomy and performed a scoping review of methods that quantitatively and automatically provide a dense estimation of registration error. The taxonomy breaks down error estimation methods into Approach (Image- or Transformation-based), Framework (Machine Learning or Direct) and Measurement (error or confidence) components. Following the PRISMA guidelines for scoping reviews, the 570 records found were reduced to twenty studies that met inclusion criteria, which were then reviewed according to the proposed taxonomy. Trends in the field, advantages and disadvantages of the methods, and potential sources of bias are also discussed. We provide suggestions for best practices and identify areas of future research.
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Affiliation(s)
- Joshua Bierbrier
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada.
| | - Houssem-Eddine Gueziri
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - D Louis Collins
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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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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10
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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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11
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Barten DLJ, Laan JJ, Nelissen KJ, Visser J, Westerveld H, Bel A, de Jonge CS, Stoker J, van Kesteren Z. A 3D cine-MRI acquisition technique and image analysis framework to quantify bowel motion demonstrated in gynecological cancer patients. Med Phys 2021; 48:3109-3119. [PMID: 33738805 PMCID: PMC8360025 DOI: 10.1002/mp.14851] [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: 11/25/2020] [Revised: 02/01/2021] [Accepted: 03/05/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) is increasingly used in radiation oncology for target delineation and radiotherapy treatment planning, for example, in patients with gynecological cancers. As a consequence of pelvic radiotherapy, a part of the bowel is irradiated, yielding risk of bowel toxicity. Existing dose-effect models predicting bowel toxicity are inconclusive and bowel motion might be an important confounding factor. The exact motion of the bowel and dosimetric effects of its motion are yet uncharted territories in radiotherapy. In diagnostic radiology methods on the acquisition of dynamic MRI sequences were developed for bowel motility visualization and quantification. Our study aim was to develop an imaging technique based on three-dimensional (3D) cine-MRI to visualize and quantify bowel motion and demonstrate it in a cohort of gynecological cancer patients. METHODS We developed an MRI acquisition suitable for 3D bowel motion quantification, namely a balanced turbo field echo sequence (TE = 1.39 ms, TR = 2.8 ms), acquiring images in 3.7 s (dynamic) with a 1.25 × 1.25 × 2.5 mm3 resolution, yielding a field of view of 200 × 200 × 125 mm3 . These MRI bowel motion sequences were acquired in 22 gynecological patients. During a 10-min scan, 160 dynamics were acquired. Subsequent dynamics were deformably registered using a B-spline transformation model, resulting in 159 3D deformation vector fields (DVFs) per MRI set. From the 159 DVFs, the average vector length was calculated per voxel to generate bowel motion maps. Quality assurance was performed on all 159 DVFs per MRI, using the Jacobian Determinant and the Harmonic Energy as deformable image registration error metrics. In order to quantify bowel motion, we introduced the concept of cumulative motion-volume histogram (MVH) of the bowel bag volume. Finally, interpatient variation of bowel motion was analyzed using the MVH parameters M10%, M50%, and M90%. The M10%/M50%/M90% represents the minimum bowel motion per frame of 10%/50%/90% of the bowel bag volume. RESULTS The motion maps resulted in a visualization of areas with small and large movements within the bowel bag. After applying quality assurance, the M10%, M50%, and M90% were 4.4 (range 2.2-7.6) mm, 2.2 (range 0.9-4.1) mm, and 0.5 (range 0.2-1.4) mm per frame, on average over all patients, respectively. CONCLUSION We have developed a method to visualize and quantify 3D bowel motion with the use of bowel motion specific MRI sequences in 22 gynecological cancer patients. This 3D cine-MRI-based quantification tool and the concept of MVHs can be used in further studies to determine the effect of radiotherapy on bowel motion and to find the relation with dose effects to the small bowel. In addition, the developed technique can be a very interesting application for bowel motility assessment in diagnostic radiology.
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Affiliation(s)
- Danique L J Barten
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Janna J Laan
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Koen J Nelissen
- Department of Radiation Oncology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Jorrit Visser
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Henrike Westerveld
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Arjan Bel
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Catharina S de Jonge
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Jaap Stoker
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Zdenko van Kesteren
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
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Juan-Cruz C, Fast MF, Sonke JJ. A multivariable study of deformable image registration evaluation metrics in 4DCT of thoracic cancer patients. Phys Med Biol 2021; 66:035019. [PMID: 33227717 DOI: 10.1088/1361-6560/abcd18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Deformable image registration (DIR) accuracy is often validated using manually identified landmarks or known deformations generated using digital or physical phantoms. In daily practice, the application of these approaches is limited since they are time-consuming or require additional equipment. An alternative is the use of metrics automatically derived from the registrations, but their interpretation is not straightforward. In this work we aim to determine the suitability of DIR-derived metrics to validate the accuracy of 4 commonly used DIR algorithms. First, we investigated the DIR accuracy using a landmark-based metric (target registration error (TRE)) and a digital phantom-based metric (known deformation recovery error (KDE)). 4DCT scans of 16 thoracic cancer patients along with corresponding pairwise anatomical landmarks (AL) locations were collected from two public databases. Digital phantoms with known deformations were generated by each DIR algorithm to test all other algorithms and compute KDE. TRE and KDE were evaluated at AL. KDE was additionally quantified in coordinates randomly sampled (RS) inside the lungs. Second, we investigated the associations of 5 DIR-derived metrics (distance discordance metric (DDM), inverse consistency error (ICE), transitivity (TE), spatial (SS) and temporal smoothness (TS)) with DIR accuracy through uni- and multivariable linear regression models. TRE values were found higher compared to KDE values and these varied depending on the phantom used. The algorithm with the best accuracy achieved average values of TRE = 1.1 mm and KDE ranging from 0.3 to 0.8 mm. DDM was the best predictor of DIR accuracy, with moderate correlations (R 2 < 0.61). Poor correlations were obtained at AL for algorithms with better accuracy, which improved when evaluated at RS. Only slight correlation improvement was obtained with a multivariable analysis (R 2 < 0.64). DDM can be a useful metric to identify inaccuracies for different DIR algorithms without employing landmarks or digital phantoms.
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Affiliation(s)
- Celia Juan-Cruz
- The Netherlands Cancer Institute, Radiotherapy Department, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Martin F Fast
- The Netherlands Cancer Institute, Radiotherapy Department, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- The Netherlands Cancer Institute, Radiotherapy Department, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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Barber J, Yuen J, Jameson M, Schmidt L, Sykes J, Gray A, Hardcastle N, Choong C, Poder J, Walker A, Yeo A, Archibald‐Heeren B, Harrison K, Haworth A, Thwaites D. Deforming to Best Practice: Key considerations for deformable image registration in radiotherapy. J Med Radiat Sci 2020; 67:318-332. [PMID: 32741090 PMCID: PMC7754021 DOI: 10.1002/jmrs.417] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 05/15/2020] [Accepted: 06/12/2020] [Indexed: 12/11/2022] Open
Abstract
Image registration is a process that underlies many new techniques in radiation oncology - from multimodal imaging and contour propagation in treatment planning to dose accumulation throughout treatment. Deformable image registration (DIR) is a subset of image registration subject to high levels of complexity in process and validation. A need for local guidance to assist in high-quality utilisation and best practice was identified within the Australian community, leading to collaborative activity and workshops. This report communicates the current limitations and best practice advice from early adopters to help guide those implementing DIR in the clinic at this early stage. They are based on the state of image registration applications in radiotherapy in Australia and New Zealand (ANZ), and consensus discussions made at the 'Deforming to Best Practice' workshops in 2018. The current status of clinical application use cases is presented, including multimodal imaging, automatic segmentation, adaptive radiotherapy, retreatment, dose accumulation and response assessment, along with uptake, accuracy and limitations. Key areas of concern and preliminary suggestions for commissioning, quality assurance, education and training, and the use of automation are also reported. Many questions remain, and the radiotherapy community will benefit from continued research in this area. However, DIR is available to clinics and this report is intended to aid departments using or about to use DIR tools now.
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Affiliation(s)
- Jeffrey Barber
- Sydney West Radiation Oncology NetworkBlacktown and WestmeadNSWAustralia
- Institute of Medical PhysicsUniversity of SydneySydneyNSWAustralia
| | - Johnson Yuen
- St George Cancer Care CentreSydneyNSWAustralia
- Ingham Institute for Applied Medical ResearchSydneyNSWAustralia
- South Western Clinical SchoolThe University of New South WalesSydneyNSWAustralia
| | - Michael Jameson
- Liverpool and Macarthur Cancer Therapy CentresSydneyNSWAustralia
- Ingham Institute for Applied Medical ResearchSydneyNSWAustralia
- South Western Clinical SchoolThe University of New South WalesSydneyNSWAustralia
| | | | - Jonathan Sykes
- Sydney West Radiation Oncology NetworkBlacktown and WestmeadNSWAustralia
- Institute of Medical PhysicsUniversity of SydneySydneyNSWAustralia
| | - Alison Gray
- Liverpool and Macarthur Cancer Therapy CentresSydneyNSWAustralia
- Ingham Institute for Applied Medical ResearchSydneyNSWAustralia
- South Western Clinical SchoolThe University of New South WalesSydneyNSWAustralia
| | - Nicholas Hardcastle
- Peter MacCallum Cancer CentreVictoriaAustralia
- Physical SciencesPeter MacCallum Cancer CentreVICAustralia
| | - Callie Choong
- Liverpool and Macarthur Cancer Therapy CentresSydneyNSWAustralia
| | - Joel Poder
- St George Cancer Care CentreSydneyNSWAustralia
- Physical SciencesPeter MacCallum Cancer CentreVICAustralia
| | - Amy Walker
- Liverpool and Macarthur Cancer Therapy CentresSydneyNSWAustralia
- Ingham Institute for Applied Medical ResearchSydneyNSWAustralia
- South Western Clinical SchoolThe University of New South WalesSydneyNSWAustralia
| | - Adam Yeo
- Peter MacCallum Cancer CentreVictoriaAustralia
- RMIT UniversityMelbourneVICAustralia
| | | | | | - Annette Haworth
- Institute of Medical PhysicsUniversity of SydneySydneyNSWAustralia
| | - David Thwaites
- Sydney West Radiation Oncology NetworkBlacktown and WestmeadNSWAustralia
- Institute of Medical PhysicsUniversity of SydneySydneyNSWAustralia
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14
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Bjarnason TA, Rees R, Kainz J, Le LH, Stewart EE, Preston B, Elbakri I, Fife IAJ, Lee T, Gagnon IMB, Arsenault C, Therrien P, Kendall E, Tonkopi E, Cottreau M, Aldrich JE. An international survey on the clinical use of rigid and deformable image registration in radiotherapy. J Appl Clin Med Phys 2020; 21:10-24. [PMID: 32915492 PMCID: PMC7075391 DOI: 10.1002/acm2.12957] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/13/2020] [Accepted: 05/14/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration. METHODS A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018. RESULTS There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively). CONCLUSIONS This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.
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Affiliation(s)
- Thorarin A. Bjarnason
- Medical ImagingInterior Health AuthorityKelownaBCCanada
- RadiologyUniversity of British ColumbiaVancouverBCCanada
- PhysicsUniversity of British Columbia OkanaganKelownaBCCanada
| | - Robert Rees
- Occupational Health & SafetyYukon Workers' Compensation Health and Safety BoardWhitehorseYKCanada
| | - Judy Kainz
- Workers' Safety and Compensation Commission for Northwest Territories and NunavutYellowknifeNTCanada
| | - Lawrence H. Le
- Diagnostic ImagingAlberta Health ServicesCalgaryABCanada
- Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABCanada
| | | | - Brent Preston
- Radiation Safety UnitGovernment of SaskatchewanSaskatoonSKCanada
| | - Idris Elbakri
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ingvar A. J. Fife
- Cancer Care ManitobaWinnipegMBCanada
- Physics and AstronomyUniversity of ManitobaWinnipegMBCanada
- RadiologyUniversity of ManitobaWinnipegMBCanada
| | - Ting‐Yim Lee
- St Joseph’s Health Care LondonLondonONCanada
- Lawson Research InstituteLondonONCanada
- Medical ImagingMedical Biophysics, OncologyRobarts Research InstituteUniversity of Western OntarioLondonONCanada
| | | | - Clément Arsenault
- Hôpital Dr Georges–L. DumontCentre d'Oncologie Dr Léon–RichardMonctonNBCanada
| | | | | | - Elena Tonkopi
- Nova Scotia Health AuthorityHalifaxNSCanada
- Diagnostic RadiologyDalhousie UniversityHalifaxNSCanada
- Radiation OncologyDalhousie UniversityHalifaxNSCanada
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Head and neck IMPT probabilistic dose accumulation: Feasibility of a 2 mm setup uncertainty setting. Radiother Oncol 2020; 154:45-52. [PMID: 32898561 DOI: 10.1016/j.radonc.2020.09.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 08/14/2020] [Accepted: 09/02/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To establish optimal robust optimization uncertainty settings for clinical head and neck cancer (HNC) patients undergoing 3D image-guided pencil beam scanning (PBS) proton therapy. METHODS We analyzed ten consecutive HNC patients treated with 70 and 54.25 GyRBE to the primary and prophylactic clinical target volumes (CTV) respectively using intensity-modulated proton therapy (IMPT). Clinical plans were generated using robust optimization with 5 mm/3% setup/range uncertainties (RayStation v6.1). Additional plans were created for 4, 3, 2 and 1 mm setup and 3% range uncertainty and for 3 mm setup and 3%, 2% and 1% range uncertainty. Systematic and random error distributions were determined for setup and range uncertainties based on our quality assurance program. From these, 25 treatment scenarios were sampled for each plan, each consisting of a systematic setup and range error and daily random setup errors. Fraction doses were calculated on the weekly verification CT closest to the date of treatment as this was considered representative of the daily patient anatomy. RESULTS Plans with a 2 mm/3% setup/range uncertainty setting adequately covered the primary and prophylactic CTV (V95 ≥ 99% in 98.8% and 90.8% of the treatment scenarios respectively). The average organ-at-risk dose decreased with 1.1 GyRBE/mm setup uncertainty reduction and 0.5 GyRBE/1% range uncertainty reduction. Normal tissue complication probabilities decreased by 2.0%/mm setup uncertainty reduction and by 0.9%/1% range uncertainty reduction. CONCLUSION The results of this study indicate that margin reduction below 3 mm/3% is possible but requires a larger cohort to substantiate clinical introduction.
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16
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Nenoff L, Ribeiro CO, Matter M, Hafner L, Josipovic M, Langendijk JA, Persson GF, Walser M, Weber DC, Lomax AJ, Knopf AC, Albertini F, Zhang Y. Deformable image registration uncertainty for inter-fractional dose accumulation of lung cancer proton therapy. Radiother Oncol 2020; 147:178-185. [DOI: 10.1016/j.radonc.2020.04.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 04/22/2020] [Accepted: 04/25/2020] [Indexed: 12/25/2022]
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17
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van der Laan HP, Anakotta RM, Korevaar EW, Dieters M, Ubbels JF, Wijsman R, Sijtsema NM, Both S, Langendijk JA, Muijs CT, Knopf AC. Organ sparing potential and inter-fraction robustness of adaptive intensity modulated proton therapy for lung cancer. Acta Oncol 2019; 58:1775-1782. [PMID: 31556764 DOI: 10.1080/0284186x.2019.1669818] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background: The aim of this study was to compare adaptive intensity modulated proton therapy (IMPT) robustness and organ sparing capabilities with that of adaptive volumetric arc photon therapy (VMAT).Material and methods: Eighteen lung cancer patients underwent a planning 4DCT (p4DCT) and 5 weekly repeated 4DCT (r4DCT) scans. Target volumes and organs at risk were manually delineated on the three-dimensional (3D) average scans of the p4DCT (av_p4DCT) and of the r4DCT scans (av_r4DCT). Planning target volume (PTV)-based VMAT plans and internal clinical target volume (ICTV)-based robust IMPT plans were optimized in 3D on the av_p4DCT and re-calculated on the av_r4DCTs. Re-planning on av_r4DCTs was performed when indicated and accumulated doses were evaluated on the av_p4DCT.Results: Adaptive VMAT and IMPT resulted in adequate ICTV coverage on av_r4DCT in all patients and adequate accumulated-dose ICTV coverage on av_p4DCT in 17/18 patients (due to a shrinking target in one patient). More frequent re-planning was needed for IMPT than for VMAT. The average mean heart dose reduction with IMPT compared with VMAT was 4.6 Gy (p = .001) and it was >5 Gy for five patients (6, 7, 8, 15, and 22 Gy). The average mean lung dose reduction was 3.2 Gy (p < .001). Significant reductions in heart and lung V5 Gy were observed with IMPT.Conclusion: Robust-planned IMPT required re-planning more often than VMAT but resulted in similar accumulated ICTV coverage. With IMPT, heart and lung mean dose values and low dose regions were significantly reduced. Substantial cardiac sparing was obtained in a subgroup of five patients (28%).
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Affiliation(s)
- Hans Paul van der Laan
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - R. Melissa Anakotta
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Erik W. Korevaar
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Margriet Dieters
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - J. Fred Ubbels
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robin Wijsman
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Nanna M. Sijtsema
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Johannes A. Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Christina T. Muijs
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Antje C. Knopf
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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McCulloch MM, Anderson BM, Cazoulat G, Peterson CB, Mohamed ASR, Volpe S, Elhalawani H, Bahig H, Rigaud B, King JB, Ford AC, Fuller CD, Brock KK. Biomechanical modeling of neck flexion for deformable alignment of the salivary glands in head and neck cancer images. Phys Med Biol 2019; 64:175018. [PMID: 31269475 DOI: 10.1088/1361-6560/ab2f13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
During head and neck (HN) cancer radiation therapy, analysis of the dose-response relationship for the parotid glands (PG) relies on the ability to accurately align soft tissue organs between longitudinal images. In order to isolate the response of the salivary glands to delivered dose, from deformation due to patient position, it is important to resolve the patient postural changes, mainly due to neck flexion. In this study we evaluate the use of a biomechanical model-based deformable image registration (DIR) algorithm to estimate the displacements and deformations of the salivary glands due to postural changes. A total of 82 pairs of CT images of HN cancer patients with varying angles of neck flexion were retrospectively obtained. The pairs of CTs of each patient were aligned using bone-based rigid registration. The images were then deformed using biomechanical model-based DIR method that focused on the mandible, C1 vertebrae, C3 vertebrae, and external contour. For comparison, an intensity-based DIR was also performed. The accuracy of the biomechanical model-based DIR was assessed using Dice similarity coefficient (DSC) for all images and for the subset of images where the PGs had a volume change within 20%. The accuracy was compared to the intensity-based DIR. The PG mean ± STD DSC were 0.63 ± 0.18, 0.80 ± 0.08, and 0.82 ± 0.15 for the rigid registration, biomechanical model-based DIR, and intensity based DIR, respectively, for patients with a PG volume change up to 20%. For the entire cohort of patients, where the PG volume change was up to 57%, the PG mean ± STD DSC were 0.60 ± 0.18, 0.78 ± 0.09, and 0.81 ± 0.14 for the rigid registration, biomechanical model-based DIR, and intensity based DIR, respectively. The difference in DSC of the intensity and biomechanical model-based DIR methods was not statistically significant when the volume change was less than 20% (two-sided paired t-test, p = 0.12). When all volume changes were considered, there was a significant difference between the two registration approaches, although the magnitude was small. These results demonstrate that the proposed biomechanical model with boundary conditions on the bony anatomy can serve to describe the varying angles of neck flexion appearing in images during radiation treatment and to align the salivary glands for proper analysis of dose-response relationships. It also motivates the need for dose response modeling following neck flexion for cases where parotid gland response is noted.
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Affiliation(s)
- Molly M McCulloch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America. Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48109, United States of America. Author to whom any correspondence should be addressed
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Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. Br J Radiol 2019; 92:20190001. [PMID: 31112393 PMCID: PMC6724618 DOI: 10.1259/bjr.20190001] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning interest in machine learning comes the significant risk of misaligned expectations as to what it can and cannot accomplish. This paper evaluates the role of machine learning and the problems it solves within the context of current clinical challenges in radiation oncology. The role of learning algorithms within the workflow for external beam radiation therapy are surveyed, considering simulation imaging, multimodal fusion, image segmentation, treatment planning, quality assurance, and treatment delivery and adaptation. For each aspect, the clinical challenges faced, the learning algorithms proposed, and the successes and limitations of various approaches are analyzed. It is observed that machine learning has largely thrived on reproducibly mimicking conventional human-driven solutions with more efficiency and consistency. On the other hand, since algorithms are generally trained using expert opinion as ground truth, machine learning is of limited utility where problems or ground truths are not well-defined, or if suitable measures of correctness are not available. As a result, machines may excel at replicating, automating and standardizing human behaviour on manual chores, meanwhile the conceptual clinical challenges relating to definition, evaluation, and judgement remain in the realm of human intelligence and insight.
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Affiliation(s)
- Daniel Jarrett
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK.,2 Mirada Medical Ltd, Oxford, UK
| | - Eleanor Stride
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Katherine Vallis
- 3 Department of Oncology, Oxford Institute for Radiation Oncology, University of Oxford, UK
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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: 60] [Impact Index Per Article: 12.0] [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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den Otter LA, Kaza E, Kierkels RG, Meijers A, Ubbels FJ, Leach MO, Collins DJ, Langendijk JA, Knopf A. Reproducibility of the lung anatomy under active breathing coordinator control: Dosimetric consequences for scanned proton treatments. Med Phys 2018; 45:5525-5534. [PMID: 30229930 PMCID: PMC6334635 DOI: 10.1002/mp.13195] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 07/11/2018] [Accepted: 09/07/2018] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The treatment of moving targets with scanned proton beams is challenging. For motion mitigation, an Active Breathing Coordinator (ABC) can be used to assist breath-holding. The delivery of pencil beam scanning fields often exceeds feasible breath-hold durations, requiring high breath-hold reproducibility. We evaluated the robustness of scanned proton therapy against anatomical uncertainties when treating nonsmall-cell lung cancer (NSCLC) patients during ABC controlled breath-hold. METHODS Four subsequent MRIs of five healthy volunteers (3 male, 2 female, age: 25-58, BMI: 19-29) were acquired under ABC controlled breath-hold during two simulated treatment fractions, providing both intrafractional and interfractional information about breath-hold reproducibility. Deformation vector fields between these MRIs were used to deform CTs of five NSCLC patients. Per patient, four or five cases with different tumor locations were modeled, simulating a total of 23 NSCLC patients. Robustly optimized (3 and 5 mm setup uncertainty respectively and 3% density perturbation) intensity-modulated proton plans (IMPT) were created and split into subplans of 20 s duration (assumed breath-hold duration). A fully fractionated treatment was recalculated on the deformed CTs. For each treatment fraction the deformed CTs representing multiple breath-hold geometries were alternated to simulate repeated ABC breath-holding during irradiation. Also a worst-case scenario was simulated by recalculating the complete treatment plan on the deformed CT scan showing the largest deviation with the first deformed CT scan, introducing a systematic error. Both the fractionated breath-hold scenario and worst-case scenario were dosimetrically evaluated. RESULTS Looking at the deformation vector fields between the MRIs of the volunteers, up to 8 mm median intra- and interfraction displacements (without outliers) were found for all lung segments. The dosimetric evaluation showed a median difference in D98% between the planned and breath-hold scenarios of -0.1 Gy (range: -4.1 Gy to 2.0 Gy). D98% target coverage was more than 57.0 Gy for 22/23 cases. The D1 cc of the CTV increased for 21/23 simulations, with a median difference of 0.9 Gy (range: -0.3 to 4.6 Gy). For 14/23 simulations the increment was beyond the allowed maximum dose of 63.0 Gy, though remained under 66.0 Gy (110% of the prescribed dose of 60.0 Gy). Organs at risk doses differed little compared to the planned doses (difference in mean doses <0.9 Gy for the heart and lungs, <1.4% difference in V35 [%] and V20 [%] to the esophagus and lung). CONCLUSIONS When treating under ABC controlled breath-hold, robustly optimized IMPT plans show limited dosimetric consequences due to anatomical variations between repeated ABC breath-holds for most cases. Thus, the combination of robustly optimized IMPT plans and the delivery under ABC controlled breath-hold presents a safe approach for PBS lung treatments.
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Affiliation(s)
- Lydia A. den Otter
- Department of Radiation OncologyUniversity Medical Center GroningenUniversity of GroningenGroningen9713 GZThe Netherlands
| | - Evangelia Kaza
- CR‐UK Cancer Imaging CentreThe Institute of Cancer Research andThe Royal Marsden HospitalLondonSW7 3RPUK
| | - Roel G.J. Kierkels
- Department of Radiation OncologyUniversity Medical Center GroningenUniversity of GroningenGroningen9713 GZThe Netherlands
| | - Arturs Meijers
- Department of Radiation OncologyUniversity Medical Center GroningenUniversity of GroningenGroningen9713 GZThe Netherlands
| | - Fred J.F. Ubbels
- Department of Radiation OncologyUniversity Medical Center GroningenUniversity of GroningenGroningen9713 GZThe Netherlands
| | - Martin O. Leach
- CR‐UK Cancer Imaging CentreThe Institute of Cancer Research andThe Royal Marsden HospitalLondonSW7 3RPUK
| | - David J. Collins
- CR‐UK Cancer Imaging CentreThe Institute of Cancer Research andThe Royal Marsden HospitalLondonSW7 3RPUK
| | - Johannes A. Langendijk
- Department of Radiation OncologyUniversity Medical Center GroningenUniversity of GroningenGroningen9713 GZThe Netherlands
| | - Antje‐Christin Knopf
- Department of Radiation OncologyUniversity Medical Center GroningenUniversity of GroningenGroningen9713 GZThe Netherlands
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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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