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Bosma LS, Hussein M, Jameson MG, Asghar S, Brock KK, McClelland JR, Poeta S, Yuen J, Zachiu C, Yeo AU. Tools and recommendations for commissioning and quality assurance of deformable image registration in radiotherapy. Phys Imaging Radiat Oncol 2024; 32:100647. [PMID: 39328928 PMCID: PMC11424976 DOI: 10.1016/j.phro.2024.100647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 09/09/2024] [Accepted: 09/10/2024] [Indexed: 09/28/2024] Open
Abstract
Multiple tools are available for commissioning and quality assurance of deformable image registration (DIR), each with their own advantages and disadvantages in the context of radiotherapy. The selection of appropriate tools should depend on the DIR application with its corresponding available input, desired output, and time requirement. Discussions were hosted by the ESTRO Physics Workshop 2021 on Commissioning and Quality Assurance for DIR in Radiotherapy. A consensus was reached on what requirements are needed for commissioning and quality assurance for different applications, and what combination of tools is associated with this. For commissioning, we recommend the target registration error of manually annotated anatomical landmarks or the distance-to-agreement of manually delineated contours to evaluate alignment. These should be supplemented by the distance to discordance and/or biomechanical criteria to evaluate consistency and plausibility. Digital phantoms can be useful to evaluate DIR for dose accumulation but are currently only available for a limited range of anatomies, image modalities and types of deformations. For quality assurance of DIR for contour propagation, we recommend at least a visual inspection of the registered image and contour. For quality assurance of DIR for warping quantitative information such as dose, Hounsfield units or positron emission tomography-data, we recommend visual inspection of the registered image together with image similarity to evaluate alignment, supplemented by an inspection of the Jacobian determinant or bending energy to evaluate plausibility, and by the dose (gradient) to evaluate relevance. We acknowledge that some of these metrics are still missing in currently available commercial solutions.
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Affiliation(s)
- Lando S Bosma
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mohammad Hussein
- Metrology for Medical Physics Centre, National Physical Laboratory, Teddington, UK
| | - Michael G Jameson
- GenesisCare, Sydney, Australia
- School of Clinical Medicine, Medicine and Health, University of New South Wales, Sydney, Australia
| | | | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jamie R McClelland
- Centre for Medical Image Computing and the Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Dept. Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Sara Poeta
- Medical Physics Department, Institut Jules Bordet - Université Libre de Bruxelles, Belgium
| | - Johnson Yuen
- School of Clinical Medicine, Medicine and Health, University of New South Wales, Sydney, Australia
- St. George Hospital Cancer Care Centre, Sydney NSW2217, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Cornel Zachiu
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Adam U Yeo
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, the University of Melbourne, Melbourne, VIC, Australia
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2
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Riou O, Prunaretty J, Michalet M. Personalizing radiotherapy with adaptive radiotherapy: Interest and challenges. Cancer Radiother 2024:S1278-3218(24)00133-1. [PMID: 39353797 DOI: 10.1016/j.canrad.2024.07.007] [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: 06/20/2024] [Accepted: 07/01/2024] [Indexed: 10/04/2024]
Abstract
Adaptive radiotherapy (ART) is a recent development in radiotherapy technology and treatment personalization that allows treatment to be tailored to the daily anatomical changes of patients. While it was until recently only performed "offline", i.e. between two radiotherapy sessions, it is now possible during ART to perform a daily online adaptive process for a given patient. Therefore, ART allows a daily customization to ensure optimal coverage of the treatment target volumes with minimized margins, taking into account only the uncertainties related to the adaptive process itself. This optimization appears particularly relevant in case of daily variations in the positioning of the target volume or of the organs at risk (OAR) associated with a proximity of these volumes and a tenuous therapeutic index. ART aims to minimize severe acute and late toxicity and allows tumor dose escalation. These new achievements have been possible thanks to technological development, the contribution of new multimodal and onboard imaging modalities and the integration of artificial intelligence tools for the contouring, planning and delivery of radiation therapy. Online ART is currently available on two types of radiotherapy machines: MR-linear accelerators and recently CBCT-linear accelerators. We will first describe the benefits, advantages, constraints and limitations of each of these two modalities, as well as the online adaptive process itself. We will then evaluate the clinical situations for which online adaptive radiotherapy is particularly indicated on MR- and CBCT-linear accelerators. Finally, we will detail some challenges and possible solutions in the development of online ART in the coming years.
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Affiliation(s)
- Olivier Riou
- Department of Radiation Oncology, Institut du cancer de Montpellier, Montpellier, France; Fédération universitaire d'oncologie radiothérapie de Méditerranée Occitanie, université de Montpellier, Montpellier, France; U1194, Inserm, Montpellier, France.
| | - Jessica Prunaretty
- Department of Radiation Oncology, Institut du cancer de Montpellier, Montpellier, France; Fédération universitaire d'oncologie radiothérapie de Méditerranée Occitanie, université de Montpellier, Montpellier, France; U1194, Inserm, Montpellier, France
| | - Morgan Michalet
- Department of Radiation Oncology, Institut du cancer de Montpellier, Montpellier, France; Fédération universitaire d'oncologie radiothérapie de Méditerranée Occitanie, université de Montpellier, Montpellier, France; U1194, Inserm, Montpellier, France
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Liu Y, Zhang P, Hong J, Alam S, Kuo L, Hu YC, Lu W, Cerviño L. Geometric evaluation and quantifying dosimetric impact of diverse deformable image registration algorithms on abdomen images with biomechanically modeled deformations. J Appl Clin Med Phys 2024:e14511. [PMID: 39258711 DOI: 10.1002/acm2.14511] [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: 07/26/2023] [Revised: 06/24/2024] [Accepted: 08/13/2024] [Indexed: 09/12/2024] Open
Abstract
PURPOSE Deformable image registration (DIR) has been increasingly used in radiation therapy (RT). The accuracy of DIR algorithms and how it impacts on the RT plan dosimetrically were examined in our study for abdominal sites using biomechanically modeled deformations. METHODS Five pancreatic cancer patients were enrolled in this study. Following the guidelines of AAPM TG-132, a patient-specific quality assurance (QA) workflow was developed to evaluate DIR for the abdomen using the TG-132 recommended virtual simulation software ImSimQA (Shrewsbury, UK). First, the planning CT was deformed to simulate respiratory motion using the embedded biomechanical model in ImSimQA. Additionally, 5 mm translational motion was added to the stomach, duodenum, and small bowel. The original planning CT and the deformed CT were then imported into Eclipse and MIM to perform DIR. The output displacement vector fields (DVFs) were compared with the ground truth from ImSimQA. Furthermore, the original treatment plan was recalculated on the ground-truth deformed CT and the deformed CT (with Eclipse and MIM DVF). The dose errors were calculated on a voxel-to-voxel basis. RESULTS Data analysis comparing DVF from Eclipse versus MIM show the average mean DVF magnitude errors of 2.8 ± 1.0 versus 1.1 ± 0.7 mm for stomach and duodenum, 5.2 ± 4.0 versus 2.5 ± 1.0 mm for small bowel, and 4.8 ± 4.1 versus 2.7 ± 1.1 mm for the gross tumor volume (GTV), respectively, across all patients. The mean dose error on stomach+duodenum and small bowel were 2.3 ± 0.6% for Eclipse, and 1.0 ± 0.3% for MIM. As the DIR magnitude error increases, the dose error range increase, for both Eclipse and MIM. CONCLUSION In our study, an initial assessment was conducted to evaluate the accuracy of DIR and its dosimetric impact on radiotherapy. A patient-specific DIR QA workflow was developed for pancreatic cancer patients. This workflow exhibits promising potential for future implementation as a clinical workflow.
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Affiliation(s)
- Yilin Liu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jun Hong
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Sadegh Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - LiCheng Kuo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Laura Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Wilson LJ, Davey A, Vasquez Osorio E, Faught AM, Green A, Bulbeck H, Thomson A, Goddard J, McCabe MG, Merchant TE, van Herk M, Aznar MC. CT- and MR-based image-based data mining are consistent in the brain. Phys Med 2024; 125:104503. [PMID: 39197263 DOI: 10.1016/j.ejmp.2024.104503] [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: 02/23/2024] [Revised: 06/17/2024] [Accepted: 08/22/2024] [Indexed: 09/01/2024] Open
Abstract
PURPOSE Image-based data mining (IBDM) is a voxel-based analysis technique to investigate dose-response. Most often, IBDM uses radiotherapy planning CTs because of their broad accessibility, however, it was unknown whether CT provided sufficient soft tissue contrast for brain IBDM. This study evaluates whether MR-based IBDM improves upon CT-based IBDM for studies of children with brain tumours. METHODS We compared IBDM pipelines using either CT- or MRI-based spatial normalisation in 128 children (ages 3.3-19.7 years) who received photon radiotherapy for primary brain tumours at a single institution. We quantified spatial-normalisation accuracy using contour comparison measures (centre-of-mass separation, average contour distance-to-agreement (DTavg), and Hausdorff distance) at multiple anatomic loci. We performed an end-to-end test of CT- and MRI-IBDM using modified clinical dose distributions and simulated effect labels to detect associations in pre-defined anatomic loci. Accuracy was assessed via sensitivity and specificity. RESULTS Spatial normalisation accuracy was comparable for both modalities, with a significant but small improvement for MRI compared to CT in all structures except the brainstem. The median (range) difference between the DTavg for the two pipelines was 0.37 (0.00-2.91) mm. The end-to-end test revealed no significant difference in sensitivity of the IBDM-identified regions for the two pipelines. Specificity slightly improved for MR-IBDM at the 99% significance level. CONCLUSION CT-based IBDM was comparable to MR-based IBDM, despite a small advantage in spatial normalisation accuracy with MRI. The use of CT-IBDM over MR-IBDM is useful for multi-institutional retrospective IBDM studies, where the availability of standardised MRI data can be limited.
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Affiliation(s)
- Lydia J Wilson
- St Jude Children's Research Hospital, Department of Radiation Oncology, Memphis, TN, USA
| | - Angela Davey
- Division of Cancer Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK.
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK
| | - Austin M Faught
- St Jude Children's Research Hospital, Department of Radiation Oncology, Memphis, TN, USA
| | - Andrew Green
- Division of Cancer Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK
| | | | - Adam Thomson
- Brainstrust - The Brain Cancer People, Cowes, UK
| | - Josh Goddard
- Brainstrust - The Brain Cancer People, Cowes, UK
| | - Martin G McCabe
- Division of Cancer Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Thomas E Merchant
- St Jude Children's Research Hospital, Department of Radiation Oncology, Memphis, TN, USA
| | - Marcel van Herk
- Division of Cancer Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK
| | - Marianne C Aznar
- Division of Cancer Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester, UK
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Cui X, Xu H, Liu J, Tian Z, Yang J. NCNet: Deformable medical image registration network based on neighborhood cross-attention combined with multi-resolution constraints. Biomed Phys Eng Express 2024; 10:055023. [PMID: 39084234 DOI: 10.1088/2057-1976/ad6992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/31/2024] [Indexed: 08/02/2024]
Abstract
Objective. Existing registration networks based on cross-attention design usually divide the image pairs to be registered into patches for input. The division and merging operations of a series of patches are difficult to maintain the topology of the deformation field and reduce the interpretability of the network. Therefore, our goal is to develop a new network architecture based on a cross-attention mechanism combined with a multi-resolution strategy to improve the accuracy and interpretability of medical image registration.Approach. We propose a new deformable image registration network NCNet based on neighborhood cross-attention combined with multi-resolution strategy. The network structure mainly consists of a multi-resolution feature encoder, a multi-head neighborhood cross-attention module and a registration decoder. The hierarchical feature extraction capability of our encoder is improved by introducing large kernel parallel convolution blocks; the cross-attention module based on neighborhood calculation is used to reduce the impact on the topology of the deformation field and double normalization is used to reduce its computational complexity.Main result. We performed atlas-based registration and inter-subject registration tasks on the public 3D brain magnetic resonance imaging datasets LPBA40 and IXI respectively. Compared with the popular VoxelMorph method, our method improves the average DSC value by 7.9% and 3.6% on LPBA40 and IXI. Compared with the popular TransMorph method, our method improves the average DSC value by 4.9% and 1.3% on LPBA40 and IXI.Significance. We proved the advantages of the neighborhood attention calculation method compared to the window attention calculation method based on partitioning patches, and analyzed the impact of the pyramid feature encoder and double normalization on network performance. This has made a valuable contribution to promoting the further development of medical image registration methods.
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Affiliation(s)
- Xinxin Cui
- School of Medical Information Engineering, Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, People's Republic of China
| | - Hao Xu
- School of Medical Information Engineering, Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, People's Republic of China
| | - Jing Liu
- School of Medical Information Engineering, Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, People's Republic of China
| | - Zhenyu Tian
- School of Medical Information Engineering, Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, People's Republic of China
| | - Jianlan Yang
- School of Medical Information Engineering, Gansu University of Traditional Chinese Medicine, Lanzhou, 730000, People's Republic of China
- Orthopedic Traumatology Hospital, Quanzhou, Fujian, 362000, People's Republic of China
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Darzi F, Bocklitz T. A Review of Medical Image Registration for Different Modalities. Bioengineering (Basel) 2024; 11:786. [PMID: 39199744 PMCID: PMC11351674 DOI: 10.3390/bioengineering11080786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 07/26/2024] [Accepted: 07/30/2024] [Indexed: 09/01/2024] Open
Abstract
Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis.
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Affiliation(s)
- Fatemehzahra Darzi
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany;
| | - Thomas Bocklitz
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany;
- Department of Photonic Data Science, Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
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7
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Brunaud C, Valable S, Ropars G, Dwiri FA, Naveau M, Toutain J, Bernaudin M, Freret T, Léger M, Touzani O, Pérès EA. Deformation-based morphometry: a sensitive imaging approach to detect radiation-induced brain injury? Cancer Imaging 2024; 24:95. [PMID: 39026377 PMCID: PMC11256482 DOI: 10.1186/s40644-024-00736-1] [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: 02/26/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Radiotherapy is a major therapeutic approach in patients with brain tumors. However, it leads to cognitive impairments. To improve the management of radiation-induced brain sequalae, deformation-based morphometry (DBM) could be relevant. Here, we analyzed the significance of DBM using Jacobian determinants (JD) obtained by non-linear registration of MRI images to detect local vulnerability of healthy cerebral tissue in an animal model of brain irradiation. METHODS Rats were exposed to fractionated whole-brain irradiation (WBI, 30 Gy). A multiparametric MRI (anatomical, diffusion and vascular) study was conducted longitudinally from 1 month up to 6 months after WBI. From the registration of MRI images, macroscopic changes were analyzed by DBM and microscopic changes at the cellular and vascular levels were evaluated by quantification of cerebral blood volume (CBV) and diffusion metrics including mean diffusivity (MD). Voxel-wise comparisons were performed on the entire brain and in specific brain areas identified by DBM. Immunohistology analyses were undertaken to visualize the vessels and astrocytes. RESULTS DBM analysis evidenced time-course of local macrostructural changes; some of which were transient and some were long lasting after WBI. DBM revealed two vulnerable brain areas, namely the corpus callosum and the cortex. DBM changes were spatially associated to microstructural alterations as revealed by both diffusion metrics and CBV changes, and confirmed by immunohistology analyses. Finally, matrix correlations demonstrated correlations between JD/MD in the early phase after WBI and JD/CBV in the late phase both in the corpus callosum and the cortex. CONCLUSIONS Brain irradiation induces local macrostructural changes detected by DBM which could be relevant to identify brain structures prone to radiation-induced tissue changes. The translation of these data in patients could represent an added value in imaging studies on brain radiotoxicity.
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Affiliation(s)
- Carole Brunaud
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Samuel Valable
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Gwenn Ropars
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Fatima-Azzahra Dwiri
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Mikaël Naveau
- Université de Caen Normandie, CNRS, INSERM, Normandie Université, UAR 3408/US50, GIP Cyceron, Caen, F-14000, France
| | - Jérôme Toutain
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Myriam Bernaudin
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Thomas Freret
- Université de Caen Normandie, INSERM, Normandie Université, COMETE UMR-S 1075, GIP Cyceron, Caen, F-14000, France
| | - Marianne Léger
- Université de Caen Normandie, INSERM, Normandie Université, COMETE UMR-S 1075, GIP Cyceron, Caen, F-14000, France
| | - Omar Touzani
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Elodie A Pérès
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France.
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Patrick HM, Kildea J. The use of dose surface maps as a tool to investigate spatial dose delivery accuracy for the rectum during prostate radiotherapy. J Appl Clin Med Phys 2024; 25:e14314. [PMID: 38425148 PMCID: PMC11244681 DOI: 10.1002/acm2.14314] [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: 09/12/2023] [Revised: 01/17/2024] [Accepted: 02/07/2024] [Indexed: 03/02/2024] Open
Abstract
PURPOSE This study aims to address the lack of spatial dose comparisons of planned and delivered rectal doses during prostate radiotherapy by using dose-surface maps (DSMs) to analyze dose delivery accuracy and comparing these results to those derived using DVHs. METHODS Two independent cohorts were used in this study: twenty patients treated with 36.25 Gy in five fractions (SBRT) and 20 treated with 60 Gy in 20 fractions (IMRT). Daily delivered rectum doses for each patient were retrospectively calculated using daily CBCT images. For each cohort, planned and average-delivered DVHs were generated and compared, as were planned and accumulated DSMs. Permutation testing was used to identify DVH metrics and DSM regions where significant dose differences occurred. Changes in rectal volume and position between planning and delivery were also evaluated to determine possible correlation to dosimetric changes. RESULTS For both cohorts, DVHs and DSMs reported conflicting findings on how planned and delivered rectum doses differed from each other. DVH analysis determined average-delivered DVHs were on average 7.1% ± 7.6% (p ≤ 0.002) and 5.0 ± 7.4% (p ≤ 0.021) higher than planned for the IMRT and SBRT cohorts, respectively. Meanwhile, DSM analysis found average delivered posterior rectal wall dose was 3.8 ± 0.6 Gy (p = 0.014) lower than planned in the IMRT cohort and no significant dose differences in the SBRT cohort. Observed dose differences were moderately correlated with anterior-posterior rectal wall motion, as well as PTV superior-inferior motion in the IMRT cohort. Evidence of both these relationships were discernable in DSMs. CONCLUSION DSMs enabled spatial investigations of planned and delivered doses can uncover associations with interfraction motion that are otherwise masked in DVHs. Investigations of dose delivery accuracy in radiotherapy may benefit from using DSMs over DVHs for certain organs such as the rectum.
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Affiliation(s)
- Haley M Patrick
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada
| | - John Kildea
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada
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Szlag M, Stankiewicz M, Kellas-Ślęczka S, Stąpór-Fudzińska M, Cholewka A, Pruefer A, Wojcieszek P. Comparison of image registration methods in patients with non-melanoma skin cancer treated with superficial brachytherapy. Phys Imaging Radiat Oncol 2024; 31:100631. [PMID: 39262679 PMCID: PMC11387206 DOI: 10.1016/j.phro.2024.100631] [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: 02/27/2024] [Revised: 08/14/2024] [Accepted: 08/15/2024] [Indexed: 09/13/2024] Open
Abstract
The accumulated dose from sequential treatments of metachronous non-melanoma skin cancer can be assessed using image registration, although guidelines for selecting the appropriate algorithm are lacking. This study shows the impact of rigid (RIR), deformable (DIR) and deformable structure-based (SDIR) algorithms on the skin dose. DIR increased: the maximum dose (39.2 Gy vs 9.4 Gy), the dose to 0.1 cm3 (16.4 Gy vs 7.8 Gy) and the dose to 2 cm3 (7.6 Gy vs 5.7 Gy). RIR only affected the maximum dose, which increased to 17.0 Gy. SDIR correctly translated the dose maps, as none of the parameters changed significantly.
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Affiliation(s)
- Marta Szlag
- Maria Sklodowska-Curie National Research Institute of Oncology Gliwice branch Wybrzeże Armii Krajowej Street 15, 44-101 Gliwice, Poland
| | - Magdalena Stankiewicz
- Maria Sklodowska-Curie National Research Institute of Oncology Gliwice branch Wybrzeże Armii Krajowej Street 15, 44-101 Gliwice, Poland
| | - Sylwia Kellas-Ślęczka
- Maria Sklodowska-Curie National Research Institute of Oncology Gliwice branch Wybrzeże Armii Krajowej Street 15, 44-101 Gliwice, Poland
| | - Małgorzata Stąpór-Fudzińska
- Maria Sklodowska-Curie National Research Institute of Oncology Gliwice branch Wybrzeże Armii Krajowej Street 15, 44-101 Gliwice, Poland
| | - Agnieszka Cholewka
- Maria Sklodowska-Curie National Research Institute of Oncology Gliwice branch Wybrzeże Armii Krajowej Street 15, 44-101 Gliwice, Poland
| | - Agnieszka Pruefer
- Maria Sklodowska-Curie National Research Institute of Oncology Gliwice branch Wybrzeże Armii Krajowej Street 15, 44-101 Gliwice, Poland
| | - Piotr Wojcieszek
- Maria Sklodowska-Curie National Research Institute of Oncology Gliwice branch Wybrzeże Armii Krajowej Street 15, 44-101 Gliwice, Poland
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10
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Wong YM, Koh CWY, Lew KS, Chua CGA, Yeap PL, Zhang ET, Ong ALK, Tuan JKL, Ng BF, Lew WS, Lee JCL, Tan HQ. Deformable anthropomorphic pelvis phantom for dose accumulation verification. Phys Med Biol 2024; 69:12NT01. [PMID: 38821109 DOI: 10.1088/1361-6560/ad52e4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 05/31/2024] [Indexed: 06/02/2024]
Abstract
Objective.The validation of deformable image registration (DIR) for contour propagation is often done using contour-based metrics. Meanwhile, dose accumulation requires evaluation of voxel mapping accuracy, which might not be accurately represented by contour-based metrics. By fabricating a deformable anthropomorphic pelvis phantom, we aim to (1) quantify the voxel mapping accuracy for various deformation scenarios, in high- and low-contrast regions, and (2) identify any correlation between dice similarity coefficient (DSC), a commonly used contour-based metric, and the voxel mapping accuracy for each organ.Approach. Four organs, i.e. pelvic bone, prostate, bladder and rectum (PBR), were 3D printed using PLA and a Polyjet digital material, and assembled. The latter three were implanted with glass bead and CT markers within or on their surfaces. Four deformation scenarios were simulated by varying the bladder and rectum volumes. For each scenario, nine DIRs with different parameters were performed on RayStation v10B. The voxel mapping accuracy was quantified by finding the discrepancy between true and mapped marker positions, termed the target registration error (TRE). Pearson correlation test was done between the DSC and mean TRE for each organ.Main results. For the first time, we fabricated a deformable phantom purely from 3D printing, which successfully reproduced realistic anatomical deformations. Overall, the voxel mapping accuracy dropped with increasing deformation magnitude, but improved when more organs were used to guide the DIR or limit the registration region. DSC was found to be a good indicator of voxel mapping accuracy for prostate and rectum, but a comparatively poorer one for bladder. DSC > 0.85/0.90 was established as the threshold of mean TRE ⩽ 0.3 cm for rectum/prostate. For bladder, extra metrics in addition to DSC should be considered.Significance. This work presented a 3D printed phantom, which enabled quantification of voxel mapping accuracy and evaluation of correlation between DSC and voxel mapping accuracy.
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Affiliation(s)
- Yun Ming Wong
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
| | - Calvin Wei Yang Koh
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Kah Seng Lew
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Clifford Ghee Ann Chua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Ping Lin Yeap
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Ee Teng Zhang
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
- Singapore Centre for 3D Printing, Nanyang Technological University, Singapore, Singapore
| | - Ashley Li Kuan Ong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Jeffrey Kit Loong Tuan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
| | - Bing Feng Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Wen Siang Lew
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
| | - James Cheow Lei Lee
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Hong Qi Tan
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
- Ai3 Lab, National Cancer Centre Singapore, Singapore, Singapore
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11
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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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12
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Bayat F, Miller B, Park Y, Yu Z, Alexeev T, Thomas D, Stuhr K, Kavanagh B, Miften M, Altunbas C. 2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis. Med Phys 2024; 51:3053-3066. [PMID: 38043086 PMCID: PMC11008043 DOI: 10.1002/mp.16867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/31/2023] [Accepted: 11/15/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Online dose calculations before the delivery of radiation treatments have applications in dose delivery verification, online adaptation of treatment plans, and simulation-free treatment planning. While dose calculations by directly utilizing CBCT images are desired, dosimetric accuracy can be compromised due to relatively lower HU accuracy in CBCT images. PURPOSE In this work, we propose a novel CBCT imaging pipeline to enhance the accuracy of CBCT-based dose calculations in the pelvis region. Our approach aims to improve the HU accuracy in CBCT images, thereby improving the overall accuracy of CBCT-based dose calculations prior to radiation treatment delivery. METHODS An in-house developed quantitative CBCT pipeline was implemented to address the CBCT raw data contamination problem. The pipeline combines algorithmic data correction strategies and 2D antiscatter grid-based scatter rejection to achieve high CT number accuracy. To evaluate the effect of the quantitative CBCT pipeline on CBCT-based dose calculations, phantoms mimicking pelvis anatomy were scanned using a linac-mounted CBCT system, and a gold standard multidetector CT used for treatment planning (pCT). A total of 20 intensity-modulated treatment plans were generated for five targets, using 6 and 10 MV flattening filter-free beams, and utilizing small and large pelvis phantom images. For each treatment plan, four different dose calculations were performed using pCT images and three CBCT imaging configurations: quantitative CBCT, clinical CBCT protocol, and a high-performance 1D antiscatter grid (1D ASG). Subsequently, dosimetric accuracy was evaluated for both targets and organs at risk as a function of patient size, target location, beam energy, and CBCT imaging configuration. RESULTS When compared to the gold-standard pCT, dosimetric errors in quantitative CBCT-based dose calculations were not significant across all phantom sizes, beam energies, and treatment sites. The largest error observed was 0.6% among all dose volume histogram metrics and evaluated dose calculations. In contrast, dosimetric errors reached up to 7% and 97% in clinical CBCT and high-performance ASG CBCT-based treatment plans, respectively. The largest dosimetric errors were observed in bony targets in the large phantom treated with 6 MV beams. The trends of dosimetric errors in organs at risk were similar to those observed in the targets. CONCLUSIONS The proposed quantitative CBCT pipeline has the potential to provide comparable dose calculation accuracy to the gold-standard planning CT in photon radiation therapy for the abdomen and pelvis. These robust dose calculations could eliminate the need for density overrides in CBCT images and enable direct utilization of CBCT images for dose delivery monitoring or online treatment plan adaptations before the delivery of radiation treatments.
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Affiliation(s)
- Farhang Bayat
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Brian Miller
- Department of Radiation Oncology, The University of Arizona, College of Medicine, Tucson, AZ 85719
| | - Yeonok Park
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Zhelin Yu
- Department of Computer Science and Engineering, University of Colorado Denver, 1200 Larimer Street, Denver, CO, 80204
| | - Timur Alexeev
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - David Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Kelly Stuhr
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
| | - Cem Altunbas
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Suite 1032, Mail stop F-706 Aurora, CO 80045
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13
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Kumar MA, Hajare R, Nath BD, Lakshmi KKS, Mahantshetty UM. Performance Evaluation of Deformable Image Registration Systems - SmartAdapt ® and Velocity™. J Med Phys 2024; 49:240-249. [PMID: 39131429 PMCID: PMC11309134 DOI: 10.4103/jmp.jmp_167_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: 12/06/2023] [Revised: 02/27/2024] [Accepted: 03/20/2024] [Indexed: 08/13/2024] Open
Abstract
Aim To commission and validate commercial deformable image registration (DIR) systems (SmartAdapt® and Velocity™) using task group 132 (TG-132) digital phantom datasets. Additionally, the study compares and verifies the DIR algorithms of the two systems. Materials and Methods TG-132 digital phantoms were obtained from the American Association of Physicists in Medicine website and imported into SmartAdapt® and Velocity™ systems for commissioning and validation. The registration results were compared with known shifts using rigid registrations and deformable registrations. Virtual head and neck phantoms obtained online (DIR Evaluation Project) and some selected clinical data sets from the department were imported into the two DIR systems. For both of these datasets, DIR was carried out between the source and target images, and the contours were then propagated from the source to the target image data set. The dice similarity coefficient (DSC), mean distance to agreement (MDA), and Jacobian determinant measures were utilised to evaluate the registration results. Results The recommended criteria for commissioning and validation of DIR system from TG-132 was error <0.5*voxel dimension (vd). Translation only registration: Both systems met TG-132 recommendations except computed tomography (CT)-positron emission tomography registration in both systems (Velocity ~1.1*vd, SmartAdapt ~1.6*vd). Translational and rotational registration: Both systems failed the criteria for all modalities (For velocity, error ranged from 0.6*vd [CT-CT registration] to 3.4*vd [CT-cone-beam CT (CBCT) registration]. For SmartAdapt® the range was 0.6*vd [CT-CBCT] to 3.6*vd [CT-CT]). Mean ± standard deviation for DSC, MDA and Jacobian metrics were used to compare the DIR results between SmartAdapt® and Velocity™. Conclusion The DIR algorithms of SmartAdapt® and Velocity™ were commissioned and their deformation results were compared. Both systems can be used for clinical purpose. While there were only minimal differences between the two systems, Velocity™ provided lower values for parotids, bladder, rectum, and prostate (soft tissue) compared to SmartAdapt. However, for mandible, spinal cord, and femoral heads (rigid structures), both systems showed nearly identical results.
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Affiliation(s)
- M. Anil Kumar
- Department of Radiation Oncology, Homi Bhabha Cancer Hospital and Research Centre, Visakhapatnam, Andhra Pradesh, India
| | - Raghavendra Hajare
- Department of Radiation Oncology, Homi Bhabha Cancer Hospital and Research Centre, Visakhapatnam, Andhra Pradesh, India
| | - Bhakti Dev Nath
- Department of Radiation Oncology, Tata Medical Centre, Kolkata, West Bengal, India
| | - K. K. Sree Lakshmi
- Department of Radiation Oncology, Homi Bhabha Cancer Hospital and Research Centre, Visakhapatnam, Andhra Pradesh, India
| | - Umesh M. Mahantshetty
- Department of Radiation Oncology, Homi Bhabha Cancer Hospital and Research Centre, Visakhapatnam, Andhra Pradesh, India
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Ghasemi Z, Samadi Miandoab P. Feasibility Study of Convolutional Long ShortTerm Memory Network for Pulmonary Movement Prediction in CT Images. J Biomed Phys Eng 2024; 14:55-66. [PMID: 38357602 PMCID: PMC10862113 DOI: 10.31661/jbpe.v0i0.2105-1339] [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: 05/19/2021] [Accepted: 06/10/2021] [Indexed: 02/16/2024]
Abstract
Background During X-ray imaging, pulmonary movements can cause many image artifacts. To tackle this issue, several studies, including mathematical algorithms and 2D-3D image registration methods, have been presented. Recently, the application of deep artificial neural networks has been considered for image generation and prediction. Objective In this study, a convolutional long short-term memory (ConvLSTM) neural network is used to predict spatiotemporal 4DCT images. Material and Methods In this analytical analysis study, two ConvLSTM structures, consisting of stacked ConvLSTM models along with the hyperparameter optimizer algorithm and a new design of the ConvLSTM model are proposed. The hyperparameter optimizer algorithm in the conventional ConvLSTM includes the number of layers, number of filters, kernel size, epoch number, optimizer, and learning rate. The two ConvLSTM structures were also evaluated through six experiments based on Root Mean Square Error (RMSE) and structural similarity index (SSIM). Results Comparing the two networks demonstrates that the new design of the ConvLSTM network is faster, more accurate, and more reliable in comparison to the tuned-stacked ConvLSTM model. For all patients, the estimated RMSE and SSIM were 3.17 and 0.988, respectively, and a significant improvement can be observed in comparison to the previous studies. Conclusion Overall, the results of the new design of the ConvLSTM network show excellent performances in terms of RMSE and SSIM. Also, the generated CT images with the new design of the ConvLSTM model show a good consistency with the corresponding references regarding registration accuracy and robustness.
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Affiliation(s)
- Zahra Ghasemi
- Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran
| | - Payam Samadi Miandoab
- Department of Medical Radiation Engineering, Amirkabir University of Technology, Tehran, Iran
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15
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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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16
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di Franco F, Baudier T, Pialat PM, Munoz A, Martinon M, Pommier P, Sarrut D, Biston MC. Ultra-hypofractionated prostate cancer radiotherapy: Dosimetric impact of real-time intrafraction prostate motion and daily anatomical changes. Phys Med 2024; 118:103207. [PMID: 38215607 DOI: 10.1016/j.ejmp.2024.103207] [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: 03/14/2023] [Revised: 11/28/2023] [Accepted: 01/04/2024] [Indexed: 01/14/2024] Open
Abstract
PURPOSE To retrospectively assess the differences between planned and delivered dose during ultra-hypofractionated (UHF) prostate cancer treatments, by evaluating the dosimetric impact of daily anatomical variations alone, and in combination with prostate intrafraction motion. METHODS Prostate intrafraction motion was recorded with a transperineal ultrasound probe in 15 patients treated by UHF radiotherapy (36.25 Gy/5 fractions). The dosimetric objective was to cover 99 % of the clinical target volume with the 100 % prescription isodose line. After treatment, planning CT (pCT) images were deformably registered onto daily Cone Beam CT to generate pseudo-CT for dose accumulation (accumulated CT, aCT). The interplay effect was accounted by synchronizing prostatic shifts and beam geometry. Finally, the shifted dose maps were accumulated (moved-accumulated CT, maCT). RESULTS No significant change in daily CTV volumes was observed. Conversely, CTV V100% was 98.2 ± 0.8 % and 94.7 ± 2.6 % on aCT and maCT, respectively, compared with 99.5 ± 0.2 % on pCT (p < 0.0001). Bladder volume was smaller than planned in 76 % of fractions and D5cc was 33.8 ± 3.2 Gy and 34.4 ± 3.4 Gy on aCT (p = 0.02) and maCT (p = 0.01) compared with the pCT (36.0 ± 1.1 Gy). The rectum was smaller than planned in 50.3 % of fractions, but the dosimetric differences were not statistically significant, except for D1cc, found smaller on the maCT (33.2 ± 3.2 Gy, p = 0.02) compared with the pCT (35.3 ± 0.7 Gy). CONCLUSIONS Anatomical variations and prostate movements had more important dosimetric impact than anatomical variations alone, although, in some cases, the two phenomena compensated. Therefore, an efficient IGRT protocol is required for treatment implementation to reduce setup errors and control intrafraction motion.
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Affiliation(s)
- Francesca di Franco
- Centre Léon Bérard, 28 rue Laennec 69373, LYON Cedex 08, France; CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne, France; Univ. Grenoble Alpes, CNRS, Grenoble INP, LPSC UMR5821, 38000 Grenoble, France.
| | - Thomas Baudier
- Centre Léon Bérard, 28 rue Laennec 69373, LYON Cedex 08, France; CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne, France
| | | | - Alexandre Munoz
- Centre Léon Bérard, 28 rue Laennec 69373, LYON Cedex 08, France
| | | | - Pascal Pommier
- Centre Léon Bérard, 28 rue Laennec 69373, LYON Cedex 08, France
| | - David Sarrut
- Centre Léon Bérard, 28 rue Laennec 69373, LYON Cedex 08, France; CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne, France
| | - Marie-Claude Biston
- Centre Léon Bérard, 28 rue Laennec 69373, LYON Cedex 08, France; CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne, France
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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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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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Smolders A, Lomax A, Weber DC, Albertini F. Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy. Phys Med Biol 2023; 68:245027. [PMID: 37820691 DOI: 10.1088/1361-6560/ad0282] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 10/11/2023] [Indexed: 10/13/2023]
Abstract
Objective.Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics.Approach.Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty.Main results.The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods anexpected calibration errorof less than 3%. Additionally, theprobabilisticly accumulated dose volume histograms(DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining.Significance.Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.
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Affiliation(s)
- A Smolders
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - A Lomax
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - D C Weber
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - F Albertini
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
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20
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McWilliam A, Palma G, Abravan A, Acosta O, Appelt A, Aznar M, Monti S, Onjukka E, Panettieri V, Placidi L, Rancati T, Vasquez Osorio E, Witte M, Cella L. Voxel-based analysis: Roadmap for clinical translation. Radiother Oncol 2023; 188:109868. [PMID: 37683811 DOI: 10.1016/j.radonc.2023.109868] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/11/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023]
Abstract
Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.
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Affiliation(s)
- Alan McWilliam
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK.
| | - Giuseppe Palma
- Institute of Nanotechnology, National Research Council, Lecce, Italy.
| | - Azadeh Abravan
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Oscar Acosta
- University Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - Ane Appelt
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Marianne Aznar
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Serena Monti
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
| | - Eva Onjukka
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Sweden
| | - Vanessa Panettieri
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Eliana Vasquez Osorio
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Marnix Witte
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
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21
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Alborghetti L, Castriconi R, Sosa Marrero C, Tudda A, Ubeira-Gabellini MG, Broggi S, Pascau J, Cubero L, Cozzarini C, De Crevoisier R, Rancati T, Acosta O, Fiorino C. Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization. Phys Imaging Radiat Oncol 2023; 28:100488. [PMID: 37694264 PMCID: PMC10482897 DOI: 10.1016/j.phro.2023.100488] [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: 04/13/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/12/2023] Open
Abstract
Background and Purpose The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation. Materials and Methods Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). Results The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Conclusions Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.
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Affiliation(s)
- Lisa Alborghetti
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
| | | | - Carlos Sosa Marrero
- CLCC Eugène Marquis, INSERM, LTSI—UMR1099, F-35000, Univ Rennes, Rennes, France
| | - Alessia Tudda
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
| | | | - Sara Broggi
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
| | - Javier Pascau
- Universidad Carlos III de Madrid, Bioengineering Department, Madrid, Spain
| | - Lucia Cubero
- Universidad Carlos III de Madrid, Bioengineering Department, Madrid, Spain
| | - Cesare Cozzarini
- IRCCS San Raffaele Scientific Institute, Radiotherapy, Milano, Italy
| | | | - Tiziana Rancati
- Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Progetto Prostata, Milano, Italy
| | - Oscar Acosta
- CLCC Eugène Marquis, INSERM, LTSI—UMR1099, F-35000, Univ Rennes, Rennes, France
| | - Claudio Fiorino
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
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22
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Stevens S, Moloney S, Blackmore A, Hart C, Rixham P, Bangiri A, Pooler A, Doolan P. IPEM topical report: guidance for the clinical implementation of online treatment monitoring solutions for IMRT/VMAT. Phys Med Biol 2023; 68:18TR02. [PMID: 37531959 DOI: 10.1088/1361-6560/acecd0] [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: 03/24/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023]
Abstract
This report provides guidance for the implementation of online treatment monitoring (OTM) solutions in radiotherapy (RT), with a focus on modulated treatments. Support is provided covering the implementation process, from identification of an OTM solution to local implementation strategy. Guidance has been developed by a RT special interest group (RTSIG) working party (WP) on behalf of the Institute of Physics and Engineering in Medicine (IPEM). Recommendations within the report are derived from the experience of the WP members (in consultation with manufacturers, vendors and user groups), existing guidance or legislation and a UK survey conducted in 2020 (Stevenset al2021). OTM is an inclusive term representing any system capable of providing a direct or inferred measurement of the delivered dose to a RT patient. Information on each type of OTM is provided but, commensurate with UK demand, guidance is largely influenced byin vivodosimetry methods utilising the electronic portal imager device (EPID). Sections are included on the choice of OTM solutions, acceptance and commissioning methods with recommendations on routine quality control, analytical methods and tolerance setting, clinical introduction and staffing/resource requirements. The guidance aims to give a practical solution to sensitivity and specificity testing. Functionality is provided for the user to introduce known errors into treatment plans for local testing. Receiver operating characteristic analysis is discussed as a tool to performance assess OTM systems. OTM solutions can help verify the correct delivery of radiotherapy treatment. Furthermore, modern systems are increasingly capable of providing clinical decision-making information which can impact the course of a patient's treatment. However, technical limitations persist. It is not within the scope of this guidance to critique each available solution, but the user is encouraged to carefully consider workflow and engage with manufacturers in resolving compatibility issues.
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Affiliation(s)
| | - Stephen Moloney
- University Hospitals Dorset NHS Foundation Trust, Poole, United Kingdom
| | | | - Clare Hart
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Philip Rixham
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Anna Bangiri
- Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Alistair Pooler
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom
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23
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Yap LM, Jamalludin Z, Ng AH, Ung NM. A multi-center survey on adaptive radiation therapy for head and neck cancer in Malaysia. Phys Eng Sci Med 2023; 46:1331-1340. [PMID: 37470929 DOI: 10.1007/s13246-023-01303-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
The survey is to assess the current state of adaptive radiation therapy (ART) for head and neck (H&N) cases among radiotherapy centers in Malaysia and to identify any implementation limitations. An online questionnaire was sent to all radiotherapy centers in Malaysia. The 24-question questionnaire consists of general information about the center, ART practices, and limitations faced in implementing ART. 28 out of 36 radiotherapy centers responded, resulting in an overall response rate of 78%. About 52% of the responding centers rescanned and replanned less than 5% of their H&N patients. The majority (88.9%) of the respondents reported the use Cone Beam Computed Tomography alone or in combination with other modalities to trigger the ART process. The main reasons cited for adopting ART were weight loss, changes in the immobilization fitting, and anatomical variation. The adaptation process typically occurred during week 3 or week 4 of treatment. More than half of the respondents require three days or more from re-simulation to starting a new treatment plan. Both target and organ at risk delineation on new planning CT relied heavily on manual delineation by physicians and physicists, respectively. All centers perform patient-specific quality assurance for their new adaptive plans. Two main limitations in implementing ART are "limited financial resources or equipment" and "limitation on technical knowledge". There is a need for a common consensus to standardize the practice of ART and address these limitations to improve the implementation of ART in Malaysia.
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Affiliation(s)
- Lai Mun Yap
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
- Department of Radiotherapy, Aurelius Hospital Nilai, 71800, Nilai, Malaysia
| | - Zulaikha Jamalludin
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Aik Hao Ng
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Ngie Min Ung
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
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24
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Neylon J, Luximon DC, Ritter T, Lamb JM. Proof-of-concept study of artificial intelligence-assisted review of CBCT image guidance. J Appl Clin Med Phys 2023; 24:e14016. [PMID: 37165761 PMCID: PMC10476980 DOI: 10.1002/acm2.14016] [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: 01/09/2023] [Revised: 03/24/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023] Open
Abstract
PURPOSE Automation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup. METHODS An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day's registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance. RESULTS Following an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions. CONCLUSION In this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors' knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.
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Affiliation(s)
- Jack Neylon
- Department of Radiation Oncology, David Geffen School of MedicineUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Dishane C. Luximon
- Department of Radiation Oncology, David Geffen School of MedicineUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Timothy Ritter
- Department of Medical PhysicsVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - James M. Lamb
- Department of Radiation Oncology, David Geffen School of MedicineUniversity of CaliforniaLos AngelesCaliforniaUSA
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25
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Hemon C, Rigaud B, Barateau A, Tilquin F, Noblet V, Sarrut D, Meyer P, Bert J, De Crevoisier R, Simon A. Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer. J Appl Clin Med Phys 2023; 24:e13991. [PMID: 37232048 PMCID: PMC10445205 DOI: 10.1002/acm2.13991] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 02/16/2023] [Accepted: 03/17/2023] [Indexed: 05/27/2023] Open
Abstract
PURPOSE To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients. METHODS AND MATERIALS Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches. The VoxelMorph method was investigated using anatomical scans (VMorph_Sc) or label images (VMorph_Msk), or the combination of both (VMorph_Sc_Msk). Accumulated doses were compared with the planning dose. RESULTS The DSC ranges, averaged for prostate, rectum and bladder, were 0.60-0.71, 0.67-0.79, 0.93-0.98, and 0.89-0.96 for the FFD, VMorph_Sc, VMorph_Msk, and VMorph_Sc_Msk methods, respectively. When including both anatomical and label images, VoxelMorph estimated more complex deformations resulting in heterogeneous determinant of Jacobian and higher percentage of deformation vector field (DVF) folding (up to a mean value of 1.90% in the prostate). Large differences were observed between DL-based methods regarding estimation of the accumulated dose, showing systematic overdosage and underdosage of the bladder and rectum, respectively. The difference between planned mean dose and accumulated mean dose with VMorph_Sc_Msk reached a median value of +6.3 Gy for the bladder and -5.1 Gy for the rectum. CONCLUSION The estimation of the deformations using DL-based approach is feasible for male pelvic anatomy but requires the inclusion of anatomical contours to improve organ correspondence. High variability in the estimation of the accumulated dose depending on the deformable strategy suggests further investigation of DL-based techniques before clinical deployment.
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Affiliation(s)
- Cédric Hemon
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI – UMR 1099RennesFrance
| | - Bastien Rigaud
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI – UMR 1099RennesFrance
| | - Anais Barateau
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI – UMR 1099RennesFrance
| | - Florian Tilquin
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI – UMR 1099RennesFrance
| | - Vincent Noblet
- Laboratoire des sciences de l'ingénieurde l'informatique et de l'imagerieICube UMR 7357Illkirch‐GraffenstadenFrance
| | - David Sarrut
- Université de LyonCREATIS, CNRS UMR5220Inserm U1294INSA‐LyonUniversité Lyon 1LyonFrance
| | - Philippe Meyer
- Department of Medical PhysicsPaul Strauss CenterStrasbourgFrance
| | - Julien Bert
- Faculty of MedicineLaTIM, INSERM UMR 1101, IBRBS, Univ BrestBrestFrance
| | | | - Antoine Simon
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI – UMR 1099RennesFrance
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Thompson C, Pagett C, Lilley J, Svensson S, Eriksson K, Bokrantz R, Ödén J, Nix M, Murray L, Appelt A. Brain Re-Irradiation Robustly Accounting for Previously Delivered Dose. Cancers (Basel) 2023; 15:3831. [PMID: 37568647 PMCID: PMC10417278 DOI: 10.3390/cancers15153831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
(1) Background: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) planning pathway aims to facilitate anatomically appropriate and radiobiologically meaningful re-irradiation (reRT). This work evaluated the STRIDeR pathway for robustness compared to a more conservative manual pathway. (2) Methods: For ten high-grade glioma reRT patient cases, uncertainties were applied and cumulative doses re-summed. Geometric uncertainties of 3, 6 and 9 mm were applied to the background dose, and LQ model robustness was tested using α/β variations (values 1, 2 and 5 Gy) and the linear quadratic linear (LQL) model δ variations (values 0.1 and 0.2). STRIDeR robust optimised plans, incorporating the geometric and α/β uncertainties during optimisation, were also generated. (3) Results: The STRIDeR and manual pathways both achieved clinically acceptable plans in 8/10 cases but with statistically significant improvements in the PTV D98% (p < 0.01) for STRIDeR. Geometric and LQ robustness tests showed comparable robustness within both pathways. STRIDeR plans generated to incorporate uncertainties during optimisation resulted in a superior plan robustness with a minimal impact on PTV dose benefits. (4) Conclusions: Our results indicate that STRIDeR pathway plans achieved a similar robustness to manual pathways with improved PTV doses. Geometric and LQ model uncertainties can be incorporated into the STRIDeR pathway to facilitate robust optimisation.
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Affiliation(s)
- Christopher Thompson
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK; (C.T.)
| | - Christopher Pagett
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK; (C.T.)
| | - John Lilley
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK; (C.T.)
| | | | | | | | - Jakob Ödén
- RaySearch Laboratories, SE-104 30 Stockholm, Sweden
| | - Michael Nix
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK; (C.T.)
| | - Louise Murray
- Leeds Cancer Centre, Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK
- Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds LS2 9JT, UK
| | - Ane Appelt
- Leeds Cancer Centre, Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK; (C.T.)
- Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds LS2 9JT, UK
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27
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Murr M, Brock KK, Fusella M, Hardcastle N, Hussein M, Jameson MG, Wahlstedt I, Yuen J, McClelland JR, Vasquez Osorio E. Applicability and usage of dose mapping/accumulation in radiotherapy. Radiother Oncol 2023; 182:109527. [PMID: 36773825 DOI: 10.1016/j.radonc.2023.109527] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/26/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023]
Abstract
Dose mapping/accumulation (DMA) is a topic in radiotherapy (RT) for years, but has not yet found its widespread way into clinical RT routine. During the ESTRO Physics workshop 2021 on "commissioning and quality assurance of deformable image registration (DIR) for current and future RT applications", we built a working group on DMA from which we present the results of our discussions in this article. Our aim in this manuscript is to shed light on the current situation of DMA in RT and to highlight the issues that hinder consciously integrating it into clinical RT routine. As a first outcome of our discussions, we present a scheme where representative RT use cases are positioned, considering expected anatomical variations and the impact of dose mapping uncertainties on patient safety, which we have named the DMA landscape (DMAL). This tool is useful for future reference when DMA applications get closer to clinical day-to-day use. Secondly, we discussed current challenges, lightly touching on first-order effects (related to the impact of DIR uncertainties in dose mapping), and focusing in detail on second-order effects often dismissed in the current literature (as resampling and interpolation, quality assurance considerations, and radiobiological issues). Finally, we developed recommendations, and guidelines for vendors and users. Our main point include: Strive for context-driven DIR (by considering their impact on clinical decisions/judgements) rather than perfect DIR; be conscious of the limitations of the implemented DIR algorithm; and consider when dose mapping (with properly quantified uncertainties) is a better alternative than no mapping.
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Affiliation(s)
- Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany.
| | - Kristy K Brock
- Department of Imaging Physics and Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, USA
| | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre & Sir Peter MacCallum Department of Oncology, University of Melbourne, Australia
| | - Mohammad Hussein
- Metrology for Medical Physics Centre, National Physical Laboratory, Teddington, United Kingdom
| | - Michael G Jameson
- GenesisCare New South Wales, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Australia
| | - 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
| | - Johnson Yuen
- St George Hospital Cancer Care Centre, Kogarah, NSW 2217, Australia; South Western Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, NSW, Australia
| | - Jamie R McClelland
- Centre for Medical Image Computing and Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Dept of Medical Physics and Biomedical Engineering, UCL, United Kingdom
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, M20 4BX Manchester, United Kingdom
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28
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Zhao T, Chen Y, Qiu B, Zhang J, Liu H, Zhang X, Zhang R, Jiang P, Wang J. Evaluating the accumulated dose distribution of organs at risk in combined radiotherapy for cervical carcinoma based on deformable image registration. Brachytherapy 2023; 22:174-180. [PMID: 36336564 DOI: 10.1016/j.brachy.2022.09.001] [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: 05/19/2022] [Revised: 08/06/2022] [Accepted: 09/07/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE To evaluate the feasibility and value of deformable image registration (DIR) in calculating the cumulative doses of organs at risk (OARs) in the combined radiotherapy of cervical cancer. PATIENTS AND METHODS Thirty cervical cancer patients treated with external beam radiotherapy (EBRT) combined with intracavitary brachytherapy (ICBT) were reviewed. The simulation CT images of EBRT and ICBT were imported into Varian Velocity 4.1 for the DIR-based dose accumulation. Cumulative dose-volume parameters of D2cc for rectum and bladder were compared between the direct addition (DA) and DIR methods. The quantitative parameters were measured to evaluate the accuracy of DIR. RESULTS The three-dimensional cumulative dose distribution of the tumor and OARs were graphically well illustrated by composite isodose lines. In combined EBRT and ICBT, the mean cumulative bladder D2cc calculated by DIR and DA was 86.13 Gy and 86.27 Gy, respectively. The mean cumulative rectal D2cc calculated by DIR and DA was 72.97 Gy and 73.90 Gy, respectively. No significant differences were noted between these two methods (p > 0.05). As to the parameters used to evaluate the DIR accuracy, the mean DSC, Jacobian, MDA (mm) and Hausdorff distance (mm) were 0.79, 1.0, 3.84, and 22.01 respectively for the bladder and 0.53, 1.2, 7.31, and 29.58 respectively for the rectum. In this study, the DSC seemed to be slightly lower compared with previous studies. CONCLUSION Dose accumulation based on DIR might be an alternative method to illustrate and evaluate the cumulative doses of the OARs in combined radiotherapy for cervical cancer. However, DIR should be used with caution before overcoming the relevant limitations.
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Affiliation(s)
- Tiandi Zhao
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Yi Chen
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Bin Qiu
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Jiashuang Zhang
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Hao Liu
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Xile Zhang
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Ruilin Zhang
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Ping Jiang
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China
| | - Junjie Wang
- Department of Radiation Oncology, Peking University Third Hospital (100191), Beijing, China.
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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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Sakulsingharoj S, Kadoya N, Tanaka S, Sato K, Nakamura M, Jingu K. Dosimetric impact of deformable image registration using radiophotoluminescent glass dosimeters with a physical geometric phantom. J Appl Clin Med Phys 2023; 24:e13890. [PMID: 36609786 PMCID: PMC10113686 DOI: 10.1002/acm2.13890] [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: 08/09/2022] [Revised: 11/04/2022] [Accepted: 12/15/2022] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To study the dosimetry impact of deformable image registration (DIR) using radiophotoluminescent glass dosimeter (RPLD) and custom developed phantom with various inserts. METHODS The phantom was developed to facilitate simultaneous evaluation of geometric and dosimetric accuracy of DIR. Four computed tomography (CT) images of the phantom were acquired with four different configurations. Four volumetric modulated arc therapy (VMAT) plans were computed for different phantom. Two different patterns were applied to combination of four phantom configurations. RPLD dose measurement was combined between corresponding two phantom configurations. DIR-based dose accumulation was calculated between corresponding two CT images with two commercial DIR software and various DIR parameter settings, and an open source software. Accumulated dose calculated using DIR was then compared with measured dose using RPLD. RESULTS The mean ± standard deviation (SD) of dose difference was 2.71 ± 0.23% (range, 2.22%-3.01%) for tumor-proxy and 3.74 ± 0.79% (range, 1.56%-4.83%) for rectum-proxy. The mean ± SD of target registration error (TRE) was 1.66 ± 1.36 mm (range, 0.03-4.43 mm) for tumor-proxy and 6.87 ± 5.49 mm (range, 0.54-17.47 mm) for rectum-proxy. These results suggested that DIR accuracy had wide range among DIR parameter setting. CONCLUSIONS The dose difference observed in our study was 3% for tumor-proxy and within 5% for rectum-proxy. The custom developed physical phantom with inserts showed potential for accurate evaluation of DIR-based dose accumulation. The prospect of simultaneous evaluation of geometric and dosimetric DIR accuracy in a single phantom may be useful for validation of DIR for clinical use.
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Affiliation(s)
- Siwaporn Sakulsingharoj
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.,Division of Radiation Oncology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kiyokazu Sato
- Department of Radiation Technology, Tohoku University Hospital, Sendai, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Kyoto University, Kyoto, Japan.,Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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He Y, Anderson BM, Cazoulat G, Rigaud B, Almodovar-Abreu L, Pollard-Larkin J, Balter P, Liao Z, Mohan R, Odisio B, Svensson S, Brock KK. Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration. Med Phys 2023; 50:323-329. [PMID: 35978544 PMCID: PMC467002 DOI: 10.1002/mp.15939] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/11/2022] [Accepted: 08/11/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow. PURPOSE To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes. METHODS Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis. RESULTS Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17 mm, respectively. CONCLUSION The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.
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Affiliation(s)
- Yulun He
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brian M. Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Julianne Pollard-Larkin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peter Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bruno Odisio
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Kristy K. Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Zou J, Gao B, Song Y, Qin J. A review of deep learning-based deformable medical image registration. Front Oncol 2022; 12:1047215. [PMID: 36568171 PMCID: PMC9768226 DOI: 10.3389/fonc.2022.1047215] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022] Open
Abstract
The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.
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Affiliation(s)
- Jing Zou
- Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
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Maraghechi B, Mazur T, Lam D, Price A, Henke L, Kim H, Hugo GD, Cai B. Phantom-based Quality Assurance of a Clinical Dose Accumulation Technique Used in an Online Adaptive Radiation Therapy Platform. Adv Radiat Oncol 2022; 8:101138. [PMID: 36691450 PMCID: PMC9860416 DOI: 10.1016/j.adro.2022.101138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 10/01/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose This study aimed to develop a routine quality assurance method for a dose accumulation technique provided by a radiation therapy platform for online treatment adaptation. Methods and Materials Two commonly used phantoms were selected for the dose accumulation QA: Electron density and anthropomorphic pelvis. On a computed tomography (CT) scan of the electron density phantom, 1 target (gross tumor volume [GTV]; insert at 6 o'clock), a subvolume within this target, and 7 organs at risk (OARs; other inserts) were contoured in the treatment planning system (TPS). Two adaptation sessions were performed in which the GTV was recontoured, first at 7 o'clock and then at 5 o'clock. The accumulated dose was exported from the TPS after delivery. Deformable vector fields were also exported to manually accumulate doses for comparison. For the pelvis phantom, synthetic Gaussian deformations were applied to the planning CT image to simulate organ changes. Two single-fraction adaptive plans were created based on the deformed planning CT and cone beam CT images acquired onboard the radiation therapy platform. A manual dose accumulation was performed after delivery using the exported deformable vector fields for comparison with the system-generated result. Results All plans were successfully delivered, and the accumulated dose was both manually calculated and derived from the TPS. For the electron density phantom, the average mean dose differences in the GTV, boost volume, and OARs 1 to 7 were 0.0%, -0.2%, 92.0%, 78.4%, 1.8%, 1.9%, 0.0%, 0.0%, and 2.3%, respectively, between the manually summed and platform-accumulated doses. The gamma passing rates for the 3-dimensional dose comparison between the manually generated and TPS-provided dose accumulations were >99% for both phantoms. Conclusions This study demonstrated agreement between manually obtained and TPS-generated accumulated doses in terms of both mean structure doses and local 3-dimensional dose distributions. Large disagreements were observed for OAR1 and OAR2 defined on the electron density phantom due to OARs having lower deformation priority over the target in addition to artificially large changes in position induced for these structures fraction-by-fraction. The tests applied in this study to a commercial platform provide a straightforward approach toward the development of routine quality assurance of dose accumulation in online adaptation.
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Affiliation(s)
- Borna Maraghechi
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Thomas Mazur
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Dao Lam
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Alex Price
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Lauren Henke
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Hyun Kim
- Department of Radiation Oncology, Washington University, St Louis, Missouri
| | - Geoffrey D. Hugo
- Department of Radiation Oncology, Washington University, St Louis, Missouri,Corresponding author: Geoffrey Hugo, PhD
| | - Bin Cai
- Department of Radiation Oncology, Washington University, St Louis, Missouri,Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
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Abbani N, Baudier T, Rit S, Franco FD, Okoli F, Jaouen V, Tilquin F, Barateau A, Simon A, de Crevoisier R, Bert J, Sarrut D. Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography. Med Phys 2022; 49:6930-6944. [PMID: 36000762 DOI: 10.1002/mp.15946] [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: 02/07/2022] [Revised: 07/28/2022] [Accepted: 08/05/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years. However, they are either sensitive to large organ deformations, or require to train a convolutional neural network (CNN) from a database of delineated CBCT images, which is difficult to do without improvement of image quality. In this work, we propose an alternative approach: to train a CNN (using a deep learning-based segmentation tool called nnU-Net) from a database of artificial CBCT images simulated from planning CT, for which it is easier to obtain the organ contours. METHODS Pseudo-CBCT (pCBCT) images were simulated from readily available segmented planning CT images, using the GATE Monte Carlo simulation. CT reference delineations were copied onto the pCBCT, resulting in a database of segmented images used to train the neural network. The studied segmentation contours were: bladder, rectum, and prostate contours. We trained multiple nnU-Net models using different training: (1) segmented real CBCT, (2) pCBCT, (3) segmented real CT and tested on pseudo-CT (pCT) generated from CBCT with cycleGAN, and (4) a combination of (2) and (3). The evaluation was performed on different datasets of segmented CBCT or pCT by comparing predicted segmentations with reference ones thanks to Dice similarity score and Hausdorff distance. A qualitative evaluation was also performed to compare DIR-based and nnU-Net-based segmentations. RESULTS Training with pCBCT was found to lead to comparable results to using real CBCT images. When evaluated on CBCT obtained from the same hospital as the CT images used in the simulation of the pCBCT, the model trained with pCBCT scored mean DSCs of 0.92 ± 0.05, 0.87 ± 0.02, and 0.85 ± 0.04 and mean Hausdorff distance 4.67 ± 3.01, 3.91 ± 0.98, and 5.00 ± 1.32 for the bladder, rectum, and prostate contours respectively, while the model trained with real CBCT scored mean DSCs of 0.91 ± 0.06, 0.83 ± 0.07, and 0.81 ± 0.05 and mean Hausdorff distance 5.62 ± 3.24, 6.43 ± 5.11, and 6.19 ± 1.14 for the bladder, rectum, and prostate contours, respectively. It was also found to outperform models using pCT or a combination of both, except for the prostate contour when tested on a dataset from a different hospital. Moreover, the resulting segmentations demonstrated a clinical acceptability, where 78% of bladder segmentations, 98% of rectum segmentations, and 93% of prostate segmentations required minor or no corrections, and for 76% of the patients, all structures of the patient required minor or no corrections. CONCLUSION We proposed to use simulated CBCT images to train a nnU-Net segmentation model, avoiding the need to gather complex and time-consuming reference delineations on CBCT images.
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Affiliation(s)
- Nelly Abbani
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Thomas Baudier
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Francesca di Franco
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Franklin Okoli
- LaTIM, Université de Bretagne Occidentale, Inserm, Brest, France
| | - Vincent Jaouen
- LaTIM, Université de Bretagne Occidentale, Inserm, Brest, France
| | | | - Anaïs Barateau
- Univ Rennes, CLCC Eugène Marquis, Inserm, Rennes, France
| | - Antoine Simon
- Univ Rennes, CLCC Eugène Marquis, Inserm, Rennes, France
| | | | - Julien Bert
- LaTIM, Université de Bretagne Occidentale, Inserm, Brest, France
| | - David Sarrut
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
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Radiotherapy of tongue cancer using an intraoral stent: a pilot study. JOURNAL OF RADIOTHERAPY IN PRACTICE 2022. [DOI: 10.1017/s1460396921000078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AbstractAim:The aim was to evaluate the feasibility of an intraoral stent (10 and 20 mm thickness) in radiotherapy of tongue cancer, and to measure the reduction in acute mucositis in the palate.Materials and method:There were six patients in the intervention group, and seven patients in the control group. Target coverage was measured by the minimum dose covering 98% of the clinical target volume (CTV). Data were collected from the planning CT and daily cone-beam computer tomography (CBCT).Results:The 10 and 20 mm stent yielded a mean distance of 26 and 36 mm, respectively, between the tongue and the hard palate. We found comparable dose coverage of the CTV in the treatment plan, and on the CBCT. The stent reduced mean dose to the hard palate by 61.0% (p = 0.002). Dose to the soft palate was not reduced (p = 0.18). Average Common Terminology Criteria for Adverse Events (CTCAE) mucositis scores of the hard palate were 0 and 0.8 in the intervention and control group, respectively. The mucositis scores of the soft palate were 1.2 and 1.8.Findings:Use of an intraoral stent substantially reduced the dose to the hard palate. CTV coverage was maintained. We did not find any significant reduction in visually scored radiation-induced mucositis.
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Watkins WT, Qing K, Han C, Hui S, Liu A. Auto-segmentation for total marrow irradiation. Front Oncol 2022; 12:970425. [PMID: 36110933 PMCID: PMC9468379 DOI: 10.3389/fonc.2022.970425] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To evaluate the accuracy and efficiency of Artificial-Intelligence (AI) segmentation in Total Marrow Irradiation (TMI) including contours throughout the head and neck (H&N), thorax, abdomen, and pelvis. Methods An AI segmentation software was clinically introduced for total body contouring in TMI including 27 organs at risk (OARs) and 4 planning target volumes (PTVs). This work compares the clinically utilized contours to the AI-TMI contours for 21 patients. Structure and image dicom data was used to generate comparisons including volumetric, spatial, and dosimetric variations between the AI- and human-edited contour sets. Conventional volume and surface measures including the Sørensen-Dice coefficient (Dice) and the 95th% Hausdorff Distance (HD95) were used, and novel efficiency metrics were introduced. The clinical efficiency gains were estimated by the percentage of the AI-contour-surface within 1mm of the clinical contour surface. An unedited AI-contour has an efficiency gain=100%, an AI-contour with 70% of its surface<1mm from a clinical contour has an efficiency gain of 70%. The dosimetric deviations were estimated from the clinical dose distribution to compute the dose volume histogram (DVH) for all structures. Results A total of 467 contours were compared in the 21 patients. In PTVs, contour surfaces deviated by >1mm in 38.6% ± 23.1% of structures, an average efficiency gain of 61.4%. Deviations >5mm were detected in 12.0% ± 21.3% of the PTV contours. In OARs, deviations >1mm were detected in 24.4% ± 27.1% of the structure surfaces and >5mm in 7.2% ± 18.0%; an average clinical efficiency gain of 75.6%. In H&N OARs, efficiency gains ranged from 42% in optic chiasm to 100% in eyes (unedited in all cases). In thorax, average efficiency gains were >80% in spinal cord, heart, and both lungs. Efficiency gains ranged from 60-70% in spleen, stomach, rectum, and bowel and 75-84% in liver, kidney, and bladder. DVH differences exceeded 0.05 in 109/467 curves at any dose level. The most common 5%-DVH variations were in esophagus (86%), rectum (48%), and PTVs (22%). Conclusions AI auto-segmentation software offers a powerful solution for enhanced efficiency in TMI treatment planning. Whole body segmentation including PTVs and normal organs was successful based on spatial and dosimetric comparison.
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Affiliation(s)
- William Tyler Watkins
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
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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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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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Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
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Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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Park JW, Yea JW, Park J, Oh SA. Setup uncertainties and appropriate setup margins in the head-tilted supine position of whole-brain radiotherapy (WBRT). PLoS One 2022; 17:e0271077. [PMID: 35925916 PMCID: PMC9352041 DOI: 10.1371/journal.pone.0271077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/22/2022] [Indexed: 11/18/2022] Open
Abstract
Various applications of head-tilting techniques in whole-brain radiotherapy (WBRT) have been introduced. However, a study on the setup uncertainties and margins in head-tilting techniques has not been reported. This study evaluated the setup uncertainties and determined the appropriate planning target volume (PTV) margins for patients treated in the head-tilted supine (ht-SP) and conventional supine position (c-SP) in WBRT. Thirty patients who received WBRT at our institution between October 2020 and May 2021 in the c-SP and ht-SP were investigated. The DUON head mask (60124, Orfit Industries, Wijnegem, Belgium) was used in the c-SP, and a thermoplastic U-Frame Mask (R420U, Klarity Medical & Equipment Co. Ltd., Lan Yu, China) was used in the ht-SP. Daily setup verification using planning computed tomography (CT) and cone-beam CT was corrected for translational (lateral, longitudinal, and vertical) and rotational (yaw) errors. In the c-SP, the means of systematic errors were -0.80, 0.79, and 0.37 mm and random errors were 0.27, 0.54, and 0.39 mm in the lateral, longitudinal, and vertical translational dimensions, respectively. Whereas, for the ht-SP, the means of systematic errors were -0.07, 0.73, and -0.63 mm, and random errors were 0.75, 1.39, 1.02 mm in the lateral, longitudinal, and vertical translational dimensions, respectively. The PTV margins were calculated using Stroom et al.’s [2Σ+0.7σ] and van Herk et al.’s recipe [2.5Σ+0.7σ]. Appropriate PTV margins with van Herk et al.’s recipe in WBRT were <2 mm and 1.5° in the c-SP and <3 mm and 2° in the ht-SP in the translational and rotational directions, respectively. Although the head tilt in the supine position requires more margin, it can be applied as a sufficiently stable and effective position in radiotherapy.
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Affiliation(s)
- Jae Won Park
- Department of Radiation Oncology, Yeungnam University Medical Center, Daegu, Korea
- Department of Radiation Oncology, Yeungnam University College of Medicine, Daegu, Korea
| | - Ji Woon Yea
- Department of Radiation Oncology, Yeungnam University Medical Center, Daegu, Korea
- Department of Radiation Oncology, Yeungnam University College of Medicine, Daegu, Korea
| | - Jaehyeon Park
- Department of Radiation Oncology, Yeungnam University Medical Center, Daegu, Korea
- Department of Radiation Oncology, Yeungnam University College of Medicine, Daegu, Korea
| | - Se An Oh
- Department of Radiation Oncology, Yeungnam University Medical Center, Daegu, Korea
- Department of Radiation Oncology, Yeungnam University College of Medicine, Daegu, Korea
- * E-mail:
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Ghosh S, Punithakumar K, Huang F, Menon G, Boulanger P. Deep Learning using Pre-Brachytherapy MRI to Automatically Predict Applicator Induced Complex Uterine Deformation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3826-3829. [PMID: 36086328 DOI: 10.1109/embc48229.2022.9871157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This novel deep-learning (DL) algorithm addresses the challenging task of predicting uterine shape and location when deformed from its natural anatomy by the presence of an intrauterine (tandem)/intravaginal (ring) applicator during brachytherapy (BT) treatment for locally advanced cervical cancer. Paired pelvic MRI datasets from 92 subjects, acquired without (pre-BT) and with (at-BT) applicators, were used. We propose a novel automated algorithm to segment the uterus in pre-BT MR images using a deep convolutional neural network (CNN) incorporated with autoencoders. The proposed neural net is based on a pre-trained CNN Inception V4 architecture. It predicts a compressed vector by applying a multi-layer autoencoder, which is then back-projected into the segmentation contour of the uterus. Following this, another transfer learning approach using a modified U-net model is employed to predict the at-BT uterus shape from pre-BT MRI. The complex and large deformations of the uterus are quantified using free form deformation method. The proposed algorithm yielded an average Dice Coefficient (DC) of 94.1±3.3 and an average Hausdorff Distance (HD) of 4.0±3.1 mm compared to the manually defined ground truth by expert clinicians. Further, the modified U-net prediction of the at-BT uterus resulted in a DC accuracy of 88.1±3.8 and HD of 5.8±3.6 mm. The mean uterine surface point-to-point displacement was 25.0 [10.0-62.5] mm from the pre-BT position. Our unique DL method can thus successfully predict tandem-deformed uterine shape and position from MR images taken before the BT implant procedure i.e. without the applicator in place. Clinical relevance-The proposed DL-based framework can be incorporated as an automatic prediction tool of uterine deformation due to applicator insertion for personalized BT treatments. It holds promise for more streamlined clinical/technical decision-making before BT applicator insertion resulting in improved dosimetric outcomes.
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Evaluation and risk factors of volume and dose differences of selected structures in patients with head and neck cancer treated on Helical TomoTherapy by using Deformable Image Registration tool. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2022. [DOI: 10.2478/pjmpe-2022-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
Introduction: The aim of this study was the evaluation of volume and dose differences in selected structures in patients with head and neck cancer during treatment on Helical TomoTherapy (HT) using a commercially available deformable image registration (DIR) tool. We attempted to identify anatomical and clinical predictive factors for significant volume changes probability.
Material and methods: According to our institutional protocol, we retrospectively evaluated the group of 20 H&N cancer patients treated with HT who received Adaptive Radiotherapy (ART) due to soft tissue alterations spotted on daily MVCT. We compared volumes on initial computed tomography (iCT) and replanning computed tomography (rCT) for clinical target volumes (CTV) – CTV1 (the primary tumor) and CTV2 (metastatic lymph nodes), parotid glands (PG) and body contour (B-body). To estimate the planned and delivered dose discrepancy, the dose from the original plan was registered and deformed to create a simulation of dose distribution on rCT (DIR-rCT).
Results: The decision to replan was made at the 4th week of RT (N = 6; 30%). The average volume reduction in parotid right PG[R] and left PG[L] was 4.37 cc (18.9%) (p < 0.001) and 3.77 cc (16.8%) (p = 0.004), respectively. In N = 13/20 cases, the delivered dose was greater than the planned dose for PG[R] of mean 3 Gy (p < 0.001), and in N = 6/20 patients for PG[L] the mean of 3.6 Gy (p = 0.031). Multivariate regression analysis showed a very strong predictor explaining 88% (R2 = 0.88) and 83% (R2 = 0.83) of the variance based on the mean dose of iPG[R] and iPG[L] (p < 0.001), respectively. No statistically significant correlation between volume changes and risk factors was found.
Conclusions: Dosimetric changes to the target demonstrated the validity of replanning. A DIR tool can be successfully used for dose deformation and ART qualification, significantly reducing the workload of radiotherapy centers. In addition, the mean dose for PG was a significant predictor that may indicate the need for a replan.
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Fransson S, Tilly D, Strand R. Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy. Phys Imaging Radiat Oncol 2022; 23:38-42. [PMID: 35769110 PMCID: PMC9234226 DOI: 10.1016/j.phro.2022.06.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/06/2022] [Accepted: 06/01/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Samuel Fransson
- Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Corresponding author at: Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden.
| | - David Tilly
- Department of Medical Physics, Uppsala University Hospital, Uppsala, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Robin Strand
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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Dose Accumulation for Multicourse Gynaecological Reirradiation: A Methodological Narrative and Clinical Examples. Int J Radiat Oncol Biol Phys 2022; 113:1085-1090. [DOI: 10.1016/j.ijrobp.2022.04.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/15/2022] [Accepted: 04/30/2022] [Indexed: 11/23/2022]
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Cheung ALY, Zhang L, Liu C, Li T, Cheung AHY, Leung C, Leung AKC, Lam SK, Lee VHF, Cai J. Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma. Front Oncol 2022; 12:816678. [PMID: 35280780 PMCID: PMC8913492 DOI: 10.3389/fonc.2022.816678] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/27/2022] [Indexed: 12/22/2022] Open
Abstract
Purpose Tumor delineation plays a critical role in radiotherapy for hepatocellular carcinoma (HCC) patients. The incorporation of MRI might improve the ability to correctly identify tumor boundaries and delineation consistency. In this study, we evaluated a novel Multisource Adaptive MRI Fusion (MAMF) method in HCC patients for tumor delineation. Methods Ten patients with HCC were included in this study retrospectively. Contrast-enhanced T1-weighted MRI at portal-venous phase (T1WPP), contrast-enhanced T1-weighted MRI at 19-min delayed phase (T1WDP), T2-weighted (T2W), and diffusion-weighted MRI (DWI) were acquired on a 3T MRI scanner and imported to in-house-developed MAMF software to generate synthetic MR fusion images. The original multi-contrast MR image sets were registered to planning CT by deformable image registration (DIR) using MIM. Four observers independently delineated gross tumor volumes (GTVs) on the planning CT, four original MR image sets, and the fused MRI for all patients. Tumor contrast-to-noise ratio (CNR) and Dice similarity coefficient (DSC) of the GTVs between each observer and a reference observer were measured on the six image sets. Inter-observer and inter-patient mean, SD, and coefficient of variation (CV) of the DSC were evaluated. Results Fused MRI showed the highest tumor CNR compared to planning CT and original MR sets in the ten patients. The mean ± SD tumor CNR was 0.72 ± 0.73, 3.66 ± 2.96, 4.13 ± 3.98, 4.10 ± 3.17, 5.25 ± 2.44, and 9.82 ± 4.19 for CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Fused MRI has the minimum inter-observer and inter-patient variations as compared to original MR sets and planning CT sets. GTV delineation inter-observer mean DSC across the ten patients was 0.81 ± 0.09, 0.85 ± 0.08, 0.88 ± 0.04, 0.89 ± 0.08, 0.90 ± 0.04, and 0.95 ± 0.02 for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. The patient mean inter-observer CV of DSC was 3.3%, 3.2%, 1.7%, 2.6%, 1.5%, and 0.9% for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Conclusion The results demonstrated that the fused MRI generated using the MAMF method can enhance tumor CNR and improve inter-observer consistency of GTV delineation in HCC as compared to planning CT and four commonly used MR image sets (T1WPP, T1WDP, T2W, and DWI). The MAMF method holds great promise in MRI applications in HCC radiotherapy treatment planning.
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Affiliation(s)
- Andy Lai-Yin Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Lei Zhang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.,Medical Physics Graduate Program, Duke University, Durham, NC, United States.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Anson Ho-Yin Cheung
- Radiotherapy and Oncology Centre, Hong Kong Baptist Hospital, Hong Kong, Hong Kong SAR, China
| | - Chun Leung
- Radiotherapy and Oncology Centre, Hong Kong Baptist Hospital, Hong Kong, Hong Kong SAR, China
| | | | - Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
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Amjad A, Xu J, Thill D, Lawton C, Hall W, Awan MJ, Shukla M, Erickson BA, Li XA. General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis. Med Phys 2022; 49:1686-1700. [PMID: 35094390 PMCID: PMC8917093 DOI: 10.1002/mp.15507] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-established deep convolutional neural network (DCNN). METHODS Five CT-based autosegmentation models for 42 organs at risk (OARs) in head and neck (HN), abdomen (ABD), and male pelvis (MP) were developed using a full three-dimensional (3D) DCNN architecture. Two types of deep learning (DL) models were separately trained using either general diversified multi-institutional datasets or custom well-controlled single-institution datasets. To improve segmentation accuracy, an adaptive spatial resolution approach for small and/or narrow OARs and a pseudo scan extension approach, when CT scan length is too short to cover entire organs, were implemented. The performance of the obtained models was evaluated based on accuracy and clinical applicability of the autosegmented contours using qualitative visual inspection and quantitative calculation of dice similarity coefficient (DSC), mean distance to agreement (MDA), and time efficiency. RESULTS The five DL autosegmentation models developed for the three anatomical sites were found to have high accuracy (DSC ranging from 0.8 to 0.98) for 74% OARs and marginally acceptable for 26% OARs. The custom models performed slightly better than the general models, even with smaller custom datasets used for the custom model training. The organ-based approaches improved autosegmentation accuracy for small or complex organs (e.g., eye lens, optic nerves, inner ears, and bowels). Compared with traditional manual contouring times, the autosegmentation times, including subsequent manual editing, if necessary, were substantially reduced by 88% for MP, 80% for HN, and 65% for ABD models. CONCLUSIONS The obtained autosegmentation models, incorporating organ-based approaches, were found to be effective and accurate for most OARs in the male pelvis, head and neck, and abdomen. We have demonstrated that our multianatomical DL autosegmentation models are clinically useful for radiation treatment planning.
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Affiliation(s)
- Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | | | | | - Colleen Lawton
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - William Hall
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - Musaddiq J. Awan
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - Monica Shukla
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - Beth A. Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, WI, USA
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Jiang C, Huang Y, Ding S, Gong X, Yuan X, Wang S, Li J, Zhang Y. Comparison of an in-house hybrid DIR method to NiftyReg on CBCT and CT images for head and neck cancer. J Appl Clin Med Phys 2022; 23:e13540. [PMID: 35084081 PMCID: PMC8906219 DOI: 10.1002/acm2.13540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 11/10/2022] Open
Abstract
An in-house hybrid deformable image registration (DIR) method, which combines free-form deformation (FFD) and the viscous fluid registration method, is proposed. Its results on the planning computed tomography (CT) and the day 1 treatment cone-beam CT (CBCT) image from 68 head and neck cancer patients are compared with the results of NiftyReg, which uses B-spline FFD alone. Several similarity metrics, the target registration error (TRE) of annotated points, as well as the Dice similarity coefficient (DSC) and Hausdorff distance (HD) of the propagated organs at risk are employed to analyze their registration accuracy. According to quantitative analysis on mutual information, normalized cross-correlation, and the absolute pixel value differences, the results of the proposed DIR are more similar to the CBCT images than the NiftyReg results. Smaller TRE of the annotated points is observed in the proposed method, and the overall mean TRE for the proposed method and NiftyReg was 2.34 and 2.98 mm, respectively (p < 0.001). The mean DSC in the larynx, spinal cord, oral cavity, mandible, and parotid given by the proposed method ranged from 0.78 to 0.91, significantly higher than the NiftyReg results (ranging from 0.77 to 0.90), and the HD was significantly lower compared to NiftyReg. Furthermore, the proposed method did not suffer from unrealistic deformations as the NiftyReg did in the visual evaluation. Meanwhile, the execution time of the proposed method was much higher than NiftyReg (96.98 ± 11.88 s vs. 4.60 ± 0.49 s). In conclusion, the in-house hybrid method gave better accuracy and more stable performance than NiftyReg.
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Affiliation(s)
- Chunling Jiang
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China.,Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Nanchang, P. R. China.,Medical College of Nanchang University, Nanchang, P. R. China
| | - Yuling Huang
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Shenggou Ding
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Xiaochang Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Xingxing Yuan
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
| | - Shaobin Wang
- MedMind Technology Co. Ltd., Beijing, P. R. China
| | - Jingao Li
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China.,Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Nanchang, P. R. China.,Medical College of Nanchang University, Nanchang, P. R. China
| | - Yun Zhang
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, P. R. China
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Nix M, Gregory S, Aldred M, Aspin L, Lilley J, Al-Qaisieh B, Uzan J, Svensson S, Dickinson P, Appelt AL, Murray L. Dose summation and image registration strategies for radiobiologically and anatomically corrected dose accumulation in pelvic re-irradiation. Acta Oncol 2022; 61:64-72. [PMID: 34586938 DOI: 10.1080/0284186x.2021.1982145] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 09/14/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Re-irradiation (reRT) is a promising technique for patients with localized recurrence in a previously irradiated area but presents major challenges. These include how to deal with anatomical change between two courses of radiotherapy and integration of radiobiology when summating original and re-irradiation doses. The Support Tool for Re-Irradiation Decisions guided by Radiobiology (STRIDeR) project aims to develop a software tool for use in a commercial treatment planning system to facilitate more informed reRT by accounting for anatomical changes and incorporating radiobiology. We evaluated three approaches to dose summation, incorporating anatomical change and radiobiology to differing extents. METHODS In a cohort of 21 patients who previously received pelvic re-irradiation the following dose summation strategies were compared: (1) Rigid registration (RIR) and physical dose summation, to reflect the current clinical approach, (2) RIR and radiobiological dose summation in equivalent dose in 2 Gy fractions (EQD2), and (3) Patient-specific deformable image registration (DIR) with EQD2 dose summation. RESULTS RIR and physical dose summation (Strategy 1) resulted in high cumulative organ at risk (OAR) doses being 'missed' in 14% of cases, which were highlighted by EQD2 dose summation (Strategy 2). DIR (with EQD2 dose summation; Strategy 3) resulted in improved OAR overlap and distance to agreement metrics compared to RIR (with EQD2 dose summation; Strategy 2) and was consistently preferred in terms of clinical utility. DIR was considered to have a clinically important impact on dose summation in 38% of cases. CONCLUSION Re-irradiation cases require individualized assessment when considering dose summation with the previous treatment plan. Fractionation correction is necessary to meaningfully assess cumulative doses and reduce the risk of unintentional OAR overdose. DIR can add clinically relevant information in selected cases, especially for significant anatomical change. Robust solutions for cumulative dose assessment offer the potential for future improved understanding of cumulative OAR tolerances.
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Affiliation(s)
- Mike Nix
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Stephen Gregory
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Michael Aldred
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Lynn Aspin
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - John Lilley
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Bashar Al-Qaisieh
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Julien Uzan
- RaySearch Laboratories AB, Stockholm, Sweden
| | | | - Peter Dickinson
- Department of Clinical Oncology, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
| | - Ane L Appelt
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Louise Murray
- Department of Clinical Oncology, Leeds Cancer Centre, St. James' University Hospital, Leeds, UK
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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Omidi A, Weiss E, Wilson JS, Rosu-Bubulac M. Quantitative assessment of intra- and inter-modality deformable image registration of the heart, left ventricle, and thoracic aorta on longitudinal 4D-CT and MR images. J Appl Clin Med Phys 2021; 23:e13500. [PMID: 34962065 PMCID: PMC8833287 DOI: 10.1002/acm2.13500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/17/2021] [Accepted: 11/29/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose Magnetic resonance imaging (MRI)‐based investigations into radiotherapy (RT)‐induced cardiotoxicity require reliable registrations of magnetic resonance (MR) imaging to planning computed tomography (CT) for correlation to regional dose. In this study, the accuracy of intra‐ and inter‐modality deformable image registration (DIR) of longitudinal four‐dimensional CT (4D‐CT) and MR images were evaluated for heart, left ventricle (LV), and thoracic aorta (TA). Methods and materials Non‐cardiac‐gated 4D‐CT and T1 volumetric interpolated breath‐hold examination (T1‐VIBE) MRI datasets from five lung cancer patients were obtained at two breathing phases (inspiration/expiration) and two time points (before treatment and 5 weeks after initiating RT). Heart, LV, and TA were manually contoured. Each organ underwent three intramodal DIRs ((A) CT modality over time, (B) MR modality over time, and (C) MR contrast effect at the same time) and two intermodal DIRs ((D) CT/MR multimodality at same time and (E) CT/MR multimodality over time). Hausdorff distance (HD), mean distance to agreement (MDA), and Dice were evaluated and assessed for compliance with American Association of Physicists in Medicine (AAPM) Task Group (TG)‐132 recommendations. Results Mean values of HD, MDA, and Dice under all registration scenarios for each region of interest ranged between 8.7 and 16.8 mm, 1.0 and 2.6 mm, and 0.85 and 0.95, respectively, and were within the TG‐132 recommended range (MDA < 3 mm, Dice > 0.8). Intramodal DIR showed slightly better results compared to intermodal DIR. Heart and TA demonstrated higher registration accuracy compared to LV for all scenarios except for HD and Dice values in Group A. Significant differences for each metric and tissue of interest were noted between Groups B and D and between Groups B and E. MDA and Dice significantly differed between LV and heart in all registrations except for MDA in Group E. Conclusions DIR of the heart, LV, and TA between non‐cardiac‐gated longitudinal 4D‐CT and MRI across two modalities, breathing phases, and pre/post‐contrast is acceptably accurate per AAPM TG‐132 guidelines. This study paves the way for future evaluation of RT‐induced cardiotoxicity and its related factors using multimodality DIR.
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Affiliation(s)
- Alireza Omidi
- Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Elisabeth Weiss
- Department of Radiation Oncology, Virginia Commonwealth University Health, Richmond, Virginia, USA
| | - John S Wilson
- Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA.,Pauley Heart Center, Virginia Commonwealth University Health System, Richmond, Virginia, USA
| | - Mihaela Rosu-Bubulac
- Department of Radiation Oncology, Virginia Commonwealth University Health, Richmond, Virginia, USA
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Utility of deformable image registration for adaptive prostate cancer treatment. Analysis and comparison of two commercially available algorithms. Z Med Phys 2021; 32:369-377. [PMID: 34906406 PMCID: PMC9948872 DOI: 10.1016/j.zemedi.2021.10.001] [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: 07/13/2021] [Revised: 09/24/2021] [Accepted: 10/17/2021] [Indexed: 11/23/2022]
Abstract
INTRODUCTION This study aimed to assess and compare the capabilities of two commercially available deformable image registration algorithms implemented in Raystation 9A (A1) and Velocity AI (A2) for possible usage in adaptive prostate radiotherapy based on the propagation of anatomical contours from computed tomography (CT) images to cone-beam CT (CBCT) images. MATERIAL AND METHODS Ten patients were retrospectively selected from a group treated for localized prostate cancer. The propagation of rectum contours was analyzed in a set of CT-CBCT pairs. Two independent observers carried out qualitative analysis using the two-level descriptive scale (meet/fail). Quantitative analysis was done using landmark points distances based on implanted markers as navigation points and differently obtained contours (manually and automatically using DIR algorithms). Quantitative analysis was taken on sets preselected by qualitative analysis. RESULTS Qualitative analysis shows that 83.7% of the rectum contours were scored identically (meet or fail) for both algorithms, from which 53.5% and 55.8% are failed results for A1 and A2, respectively. For the rectum size (RWD parameter), differences between referenced and deformation-based values were 5.5 and 5.8mm, and for the rectum wall, the prostate marker distance (WMD parameter) was 4.5 and 5.5mm for A1 and A2, respectively. The differences between the WMD parameters were statistically significant (p=0.007). CONCLUSIONS In both tested algorithms, neither effectiveness nor measured uncertainties in the propagation of rectum contour process in prostate patient cases were satisfactory. Careful selection of input images followed by case/set-based verification of every deformable registration is a substantial step to avoid inappropriate results.
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