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Guo J, Zhou L, Zeng H. Research on the correction method for radiotherapy verification plans based on displaced electronic portal imaging device. J Appl Clin Med Phys 2024:e14401. [PMID: 38778555 DOI: 10.1002/acm2.14401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/21/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND It has been observed that under the single isocenter conditions, the potential shifts of the electronic portal imaging devices (EPID) may be introduced when executing portal dosimetry (PD) plans for bilateral breast cancer, pleural mesothelioma, and lymphoma. These shifts are relative to the calibration positions of EPID and result in significant discrepancies in the plan verification results. PURPOSE To explore methods including correction model and specific correction matrices to revise the data obtained from displaced EPID. METHODS Two methods, the correction model and the specific correction matrices, were applied to correct the data. Five experiments were designed and conducted to build correction model and to validate the effectiveness of these two methods. Gamma passing rates were calculated and data profiles along X-axis and Y-axis were captured. RESULTS The gamma passing rates for the EPID-displaced IMRT validation plans after applying correction model, along with the application of specific correction matrices to VMAT and IMRT validation plans, exhibit results that are comparable to the cases with non-displaced EPID. Except for the VMAT plans applied correction model which showed larger discrepancies (0.041 ± 0.028, 0.049 ± 0.030), the other three exhibit minimal differences in discrepancy values. In all profiles, the corrected data from displaced EPID exhibit a high level of agreement with data obtained from non-displaced EPID. Good consistency is observed in actual application of the correction model and the specific correction matrices between gamma passing rates of data corrected and those of non-displaced data. CONCLUSIONS The proposed methods involving correction model and specific correction matrices can correct the data collected from the displaced EPID, and the gamma passing rates of the corrected data show results that are comparable to some extent with those of non-displaced data. Particularly, the results corrected by specific correction matrices closely resemble the data from non-displaced EPID.
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Affiliation(s)
- Jian Guo
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Leyuan Zhou
- Department of Radiation Oncology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
| | - Haibin Zeng
- Department of Radiation Oncology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
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Zeng Y, Li H, Chang Y, Han Y, Liu H, Pang B, Han J, Hu B, Cheng J, Zhang S, Yang K, Quan H, Yang Z. In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study. Phys Eng Sci Med 2024:10.1007/s13246-024-01414-z. [PMID: 38647634 DOI: 10.1007/s13246-024-01414-z] [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: 05/07/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024]
Abstract
We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146 arcs from 42 head and neck patients were analyzed. Anatomical changes and setup errors were simulated in 17,820 EPID images of 99 arcs obtained from 30 patients using in-house software for model training, validation, and testing. Subsequently, 141 clinical EPID images from 47 arcs belonging to the remaining 12 patients were utilized for clinical testing. The hierarchical convolutional neural network (HCNN) model was trained to classify error types and magnitudes using EPID dose difference maps. Gamma analysis with 3%/2 mm (dose difference/distance to agreement) criteria was also performed. The F1 score, a combination of precision and recall, was utilized to evaluate the performance of the HCNN model and gamma analysis. The adaptive fractioned doses were calculated to verify the HCNN classification results. For error type identification, the overall F1 score of the HCNN model was 0.99 and 0.91 for primary type and subtype identification, respectively. For error magnitude identification, the overall F1 score in the simulation dataset was 0.96 and 0.70 for the HCNN model and gamma analysis, respectively; while the overall F1 score in the clinical dataset was 0.79 and 0.20 for the HCNN model and gamma analysis, respectively. The HCNN model-based EPID dosimetry can identify changes in patient transmission doses and distinguish the treatment error category, which could potentially provide information for head and neck cancer treatment adaption.
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Affiliation(s)
- Yiling Zeng
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, 430072, China
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Heng Li
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yang Han
- College of Electrical Engineering, Sichuan University, Chengdu, 610065, China
| | - Hongyuan Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Bo Pang
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Jun Han
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Bin Hu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Junping Cheng
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Sheng Zhang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Kunyu Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Hong Quan
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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Olaciregui-Ruiz I, Simões R, Jan-Jakob S. Deep learning-based tools to distinguish plan-specific from generic deviations in EPID-based in vivo dosimetry. Med Phys 2024; 51:854-869. [PMID: 38112213 DOI: 10.1002/mp.16895] [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: 07/17/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Dose distributions calculated with electronic portal imaging device (EPID)-based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan-specific deviations. Generic deviations are characteristic to a class of plans. Examples include limitations in EIVD dose reconstruction, inaccuracies in treatment planning system (TPS) calculations and systematic machine deviations. Plan-specific deviations have an unpredictable character. Examples include discrepancies between the patient model used for dose calculation and the patient position or anatomy during delivery, random machine deviations, and data transfer, human or software errors. During the inspection work performed with traditional γ-evaluation statistical methods: (i) generic deviations raise alerts that need to be inspected but that rarely lead to action as their root cause is usually understood and (ii) the detection of relevant plan-specific deviations may be hindered by the presence of generic deviations. PURPOSE To investigate whether deep learning-based tools can help in identifying γ-alerts raised by generic deviations and in improving the detectability of plan-specific deviations. METHODS A 3D U-Net was trained as an autoencoder to reconstruct underlying patterns of generic deviations in γ-distributions. The network was trained for four treatment disease sites differently affected by generic deviations: volumetric modulated arc therapy (VMAT) lung (no known deviations), VMAT prostate (TPS inaccuracies), VMAT head-and-neck (EIVD limitations) and intensity modulated radiation therapy (IMRT) breast (large EIVD limitations). The network was trained with virtual non-transit γ-distributions: 60 train/10 validation for the VMAT sites and 30 train/10 validation for IMRT breast. It was hypothesized that in vivo γ-distributions obtained in the presence of plan-specific deviations would differ from those seen during training. For each disease site, the sensitivity of γ-analysis and the network to detect (synthetically introduced) patient-related deviations was compared by receiver operator characteristic analysis. The investigated deviations were patient positioning errors, weight gain or loss, and tumor volume changes. The clinical relevance was illustrated qualitatively with 793 in vivo clinical cases (141 lung, 136 head-and-neck, 209 prostate and 307 breast). RESULTS Error detectability of patient-related deviations was better with the network than with γ-analysis. The average area under the curve values over all sites were 0.86 ± 0.12(1SD) and 0.69 ± 0.25(1SD), respectively. Regarding in vivo clinical results, the percentage of cases differently classified by γ-analysis and the network was 1%, 19%, 18% and 64% for lung, head-and-neck, prostate, and breast, respectively. In head-and-neck and breast cases, 45 γ-only alerts were examined, of which 43 were attributed to EPID dose reconstruction limitations. For prostate, all 15 investigated γ-only alerts were due to known TPS inaccuracies. All 59 investigated network alerts were explained by either patient-related deviations or EPID acquisition incidents. Some patient-related deviations detected by the network were not detected by γ-analysis. CONCLUSIONS Deep learning-based tools trained to reconstruct underlying patterns of generic deviations in γ-distributions can be used to (i) automatically identify false positives within the set of γ-alerts and (ii) improve the detection of plan-specific deviations, hence minimizing the likelihood of false negatives. The presented method provides clear additional value to the γ-alert management process for large scale EIVD systems.
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Affiliation(s)
- Igor Olaciregui-Ruiz
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rita Simões
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sonke Jan-Jakob
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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van den Berg K, Wolfs CJA, Verhaegen F. A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy. Phys Med Biol 2024; 69:035002. [PMID: 38091615 DOI: 10.1088/1361-6560/ad1547] [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: 05/23/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024]
Abstract
Objective. Deep learning models, such as convolutional neural networks (CNNs), can take full dose comparison images as input and have shown promising results for error identification during treatment. Clinically, complex scenarios should be considered, with the risk of multiple anatomical and/or mechanical errors occurring simultaneously during treatment. The purpose of this study was to evaluate the capability of CNN-based error identification in this more complex scenario.Approach. For 40 lung cancer patients, clinically realistic ranges of combinations of various treatment errors within treatment plans and/or computed tomography (CT) images were simulated. Modified CT images and treatment plans were used to predict 2580 3D dose distributions, which were compared to dose distributions without errors using various gamma analysis criteria and relative dose difference as dose comparison methods. A 3D CNN capable of multilabel classification was trained to identify treatment errors at two classification levels, using dose comparison volumes as input: Level 1 (main error type, e.g. anatomical change, mechanical error) and Level 2 (error subtype, e.g. tumor regression, patient rotation). For training the CNNs, a transfer learning approach was employed. An ensemble model was also evaluated, which consisted of three separate CNNs each taking a region of interest of the dose comparison volume as input. Model performance was evaluated by calculating sample F1-scores for training and validation sets.Main results. The model had high F1-scores for Level 1 classification, but performance for Level 2 was lower, and overfitting became more apparent. Using relative dose difference instead of gamma volumes as input improved performance for Level 2 classification, whereas using an ensemble model additionally reduced overfitting. The models obtained F1-scores of 0.86 and 0.62 on an independent test set for Level 1 and Level 2, respectively.Significance. This study shows that it is possible to identify multiple errors occurring simultaneously in 3D dose verification data.
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Affiliation(s)
- Kars van den Berg
- Medical Image Analysis group, Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands
| | - Cecile J A Wolfs
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, The Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, The Netherlands
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Latorre-Musoll A, Jornet N, Sempau J. On the beam hardening correction of the Transit-Guided Radiation Therapy attenuation model. Phys Med 2023; 112:102660. [PMID: 37562234 DOI: 10.1016/j.ejmp.2023.102660] [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: 06/02/2023] [Revised: 07/06/2023] [Accepted: 08/05/2023] [Indexed: 08/12/2023] Open
Abstract
PURPOSE The Transit-Guided Radiation Therapy (TGRT) technique is a novel technique aimed to quantify the position error of a patient by using the transit portal images (TPI) of the treatment fields. Despite of the promising preliminary results, about 4% of the cases would have led to position overcorrections. In this work, the TGRT formalism is refined to improve its accuracy and, especially, to decrease the risk of overcorrections. METHODS A second free parameter accounting for beam hardening has been added to the attenuation model of the TGRT formalism. Five treatment plans combining different delivery techniques and tumour sites have been delivered to an anthropomorphic phantom. TPIs have been obtained under a set of random couch shifts for each field. For each TPI, both the original and the refined TGRT formalism have been used to estimate the underlying true shift. RESULTS With respect the original formalism, the refined formalism: (i) decreased both the number (from 5% to 1%) and the magnitude of the overcorrections; (ii) lowered the detection threshold (from approximately 1 mm to <0.3 mm); (iii) largely improved the accuracy in tumour sites with large mass thickness variations; and (iv) largely improved the accuracy for true shifts below 5 mm. For true shifts above 5 mm, the accuracy was slightly impaired. CONCLUSIONS The refined TGRT formalism performed globally better than the original TGRT formalism and it largely reduced the risk of overcorrections. Further refinements of the TGRT formalism should focus on true shifts above 5 mm.
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Affiliation(s)
- Artur Latorre-Musoll
- Servei d'Oncologia Radioteràpica (ICMHO), Hospital Clínic de Barcelona, Barcelona, Spain.
| | - Núria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
| | - Josep Sempau
- Universitat Politècnica de Catalunya, Barcelona, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
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Huang J, Hu J, Lu H, Liu S, Gong F, Wu X, Liu Y, Shi J. Error detection using EPID-based 3D in vivo dose verification for lung stereotactic body radiotherapy. Appl Radiat Isot 2023; 192:110567. [PMID: 36459899 DOI: 10.1016/j.apradiso.2022.110567] [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: 06/27/2022] [Revised: 10/21/2022] [Accepted: 11/15/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE To investigate the error detectability limitations of an EPID-based 3D in vivo dosimetry verification system for lung stereotactic body radiation therapy (SBRT). METHODS Thirty errors were intentionally introduced, consisting of dynamic and constant machine errors, to simulate the possible errors that may occur during delivery. The dynamic errors included errors in the output, gantry angle and MLC positions related to gantry inertial and gravitational effects, while the constant errors included errors in the collimator angle, jaw positions, central leaf positions, setup shift and thickness to simulate patient weight loss. These error plans were delivered to a CIRS phantom using the SBRT technique for lung cancer. Following irradiation of these error plans, the dose distribution was reconstructed using iViewDose™ and compared with the no error plan. RESULTS All errors caused by the central leaf positions, dynamic MLC errors, Jaw inwards movements, setup shifts and patient anatomical changes were successfully detected. However, dynamic gantry angle and collimator angle errors were not detected in the lung case due to the rotation-symmetric target shape. The results showed that the γmean and γpassrate indicators can detect 13 (81.3%) and 14 (87.5%) of the 16 errors respectively without including the gantry angle error, collimator angle error and output error. CONCLUSIONS In summary, iViewDose™ is an appropriate approach for detecting most types of clinical errors for lung SBRT. However, the phantom results also showed some detectability limitations of the system in terms of dynamic gantry angle and constant collimator angle errors.
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Affiliation(s)
- Jianghua Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Jinyan Hu
- Department of Oncology, Longhua District People's Hospital, Shenzhen, Guangdong Province, 518109, China
| | - Huanping Lu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Shijie Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Fengying Gong
- Department of Traditonal Chinese Medicine, Nanfang Hospital of Southern Medical University, Guangzhou, 510515, China
| | - Xiuxiu Wu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Yimin Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
| | - Juntian Shi
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
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U’wais FA, Radzi Y, Noor Rizan N, Zin HM. Validation of a digital method for patient-specific verification of VMAT treatment using a 2D ionisation detector array. Radiat Phys Chem Oxf Engl 1993 2023. [DOI: 10.1016/j.radphyschem.2022.110536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Yedekci Y, Elmalı A, Demirkiran G, Ozyigit G, Yazici G. Transit dosimetry of stereotactic body radiotherapy treatments with electronic portal dosimetry device in patient with spinal implant. Phys Eng Sci Med 2022; 45:1103-1109. [PMID: 36074299 DOI: 10.1007/s13246-022-01177-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/30/2022] [Indexed: 12/15/2022]
Abstract
In recent years, the use of the Electronic Portal Imaging Device (EPID) as an in vivo dosimeter has become widespread. However, reports of EPID for stereotactic body radiotherapy (SBRT) applications is scarce. There is no data on this topic especially when there are high-density materials in the radiation field. In this study, we aimed to investigate the dose distributions of SBRT treatment plans in patients with spinal implants by transit EPID dosimetry. Implants were inserted in phantoms that mimic the vertebrae, and VMAT plans were created on the phantoms to deliver 16 Gy radiation doses to the target in 1 fraction. Transit EPID measurements were performed for each irradiation. The results were compared with the treatment planning system using the gamma analysis method. According to the gamma analysis results, while the non-implant model met the acceptance criteria with a rate of 95.4%, the implanted models did not pass the test with results between the rates of 70% to 73%. In addition, while the dose difference in the isocenter was 1.3% for the non-implanted model, this difference was observed to be between 7 and 8% in the implanted models. Our study revealed that EPID can be used as transit dosimetry for the VMAT-SBRT applications. However, unacceptable dose differences were obtained by transit EPID dosimetry in the VMAT-SBRT applications of patients with an implant. In the treatment of such patients, alternative treatment methods should be preferred in which the interaction of the implants with radiation can be prevented.
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Affiliation(s)
- Yagiz Yedekci
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, 06100, Sihhiye, Ankara, Turkey.
| | - Aysenur Elmalı
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, 06100, Sihhiye, Ankara, Turkey
| | - Gökhan Demirkiran
- Department of Orthopaedics and Traumatology, Faculty of Medicine, Hacettepe University, Sihhiye, Ankara, Turkey
| | - Gokhan Ozyigit
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, 06100, Sihhiye, Ankara, Turkey
| | - Gözde Yazici
- Department of Radiation Oncology, Faculty of Medicine, Hacettepe University, 06100, Sihhiye, Ankara, Turkey
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Chen L, Zhang Z, Yu L, Peng J, Feng B, Zhao J, Liu Y, Xia F, Zhang Z, Hu W, Wang J. A clinically relevant online patient QA solution with daily CT scans and EPID-based in vivo dosimetry: a feasibility study on rectal cancer. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac9950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Abstract
Objective. Adaptive radiation therapy (ART) could protect organs at risk (OARs) while maintain high dose coverage to targets. However, there is still a lack of efficient online patient quality assurance (QA) methods, which is an obstacle to large-scale adoption of ART. We aim to develop a clinically relevant online patient QA solution for ART using daily CT scans and EPID-based in vivo dosimetry. Approach. Ten patients with rectal cancer at our center were included. Patients’ daily CT scans and portal images were collected to generate reconstructed 3D dose distributions. Contours of targets and OARs were recontoured on these daily CT scans by a clinician or an auto-segmentation algorithm, then dose-volume indices were calculated, and the percent deviation of these indices to their original plans were determined. This deviation was regarded as the metric for clinically relevant patient QA. The tolerance level was obtained using a 95% confidence interval of the QA metric distribution. These deviations could be further divided into anatomically relevant or delivery relevant indicators for error source analysis. Finally, our QA solution was validated on an additional six clinical patients. Main results. In rectal cancer, the 95% confidence intervals of the QA metric for PTV ΔD
95 (%) were [−3.11%, 2.35%], and for PTV ΔD
2 (%) were [−0.78%, 3.23%]. In validation, 68% for PTV ΔD
95 (%), and 79% for PTV ΔD
2 (%) of the 28 fractions are within tolerances of the QA metrics. one patient’s dosimetric impact of anatomical variations during treatment were observed through the source of error analysis. Significance. The online patient QA solution using daily CT scans and EPID-based in vivo dosimetry is clinically feasible. Source of error analysis has the potential for distinguishing sources of error and guiding ART for future treatments.
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Latorre-Musoll A, Delgado-Tapia P, Gisbert ML, Sala NJ, Sempau J. Transit-guided radiation therapy: proof of concept of an on-line technique for correcting position errors using transit portal images. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7d32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/29/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Transit in vivo dosimetry methods monitor that the dose distribution is delivered as planned. However, they have a limited ability to identify and to quantify the cause of a given disagreement, especially those caused by position errors. This paper describes a proof of concept of a simple in vivo technique to infer a position error from a transit portal image (TPI). Approach. For a given treatment field, the impact of a position error is modeled as a perturbation of the corresponding reference (unperturbed) TPI. The perturbation model determines the patient translation, described by a shift vector, by comparing a given in vivo TPI to the corresponding reference TPI. Patient rotations can also be determined by applying this formalism to independent regions of interest over the patient. Eight treatment plans have been delivered to an anthropomorphic phantom under a large set of couch shifts (<15 mm) and rotations (<10°) to experimentally validate this technique, which we have named Transit-Guided Radiation Therapy (TGRT). Main results. The root mean squared error (RMSE) between the determined and the true shift magnitudes was 1.0/2.4/4.9 mm for true shifts ranging between 0–5/5–10/10–15 mm, respectively. The angular accuracy of the determined shift directions was 12° ± 14°. The RMSE between the determined and the true rotations was 0.5°. The TGRT technique decoupled translations and rotations satisfactorily. In 96% of the cases, the TGRT technique decreased the existing position error. The detection threshold of the TGRT technique was around 1 mm and it was nearly independent of the tumor site, delivery technique, beam energy or patient thickness. Significance. TGRT is a promising technique that not only provides reliable determinations of the position errors without increasing the required equipment, acquisition time or patient dose, but it also adds on-line correction capabilities to existing methods currently using TPIs.
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Bedford JL, Hanson IM. A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry. Phys Imaging Radiat Oncol 2022; 22:36-43. [PMID: 35493850 PMCID: PMC9048084 DOI: 10.1016/j.phro.2022.03.004] [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: 12/21/2021] [Revised: 03/04/2022] [Accepted: 03/28/2022] [Indexed: 11/18/2022] Open
Abstract
Background and purpose Real-time portal dosimetry compares measured images with predicted images to detect delivery errors as the radiotherapy treatment proceeds. This work aimed to investigate the performance of a recurrent neural network for processing image metrics so as to detect delivery errors as early as possible in the treatment. Materials and methods Volumetric modulated arc therapy (VMAT) plans of six prostate patients were used to generate sequences of predicted portal images. Errors were introduced into the treatment plans and the modified plans were delivered to a water-equivalent phantom. Four different metrics were used to detect errors. These metrics were applied to a threshold-based method to detect the errors as soon as possible during the delivery, and also to a recurrent neural network consisting of four layers. A leave-two-out approach was used to set thresholds and train the neural network then test the resulting systems. Results When using a combination of metrics in conjunction with optimal thresholds, the median segment index at which the errors were detected was 107 out of 180. When using the neural network, the median segment index for error detection was 66 out of 180, with no false positives. The neural network reduced the rate of false negative results from 0.36 to 0.24. Conclusions The recurrent neural network allowed the detection of errors around 30% earlier than when using conventional threshold techniques. By appropriate training of the network, false positive alerts could be prevented, thereby avoiding unnecessary disruption to the patient workflow.
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Affiliation(s)
- James L. Bedford
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London SM2 5PT, UK
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Feng B, Yu L, Mo E, Chen L, Zhao J, Wang J, Hu W. Evaluation of Daily CT for EPID-Based Transit In Vivo Dosimetry. Front Oncol 2021; 11:782263. [PMID: 34796120 PMCID: PMC8592931 DOI: 10.3389/fonc.2021.782263] [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: 09/24/2021] [Accepted: 10/14/2021] [Indexed: 11/20/2022] Open
Abstract
Purpose The difference in anatomical structure and positioning between planning and treatment may lead to bias in electronic portal image device (EPID)-based in vivo dosimetry calculations. The purpose of this study was to use daily CT instead of planning CT as a reference for EPID-based in vivo dosimetry calculations and to analyze the necessity of using daily CT for EPID-based in vivo dosimetry calculations in terms of patient quality assurance. Materials and Methods Twenty patients were enrolled in this study. The study design included eight different sites (the cervical, nasopharyngeal, and oral cavities, rectum, prostate, bladder, lung, and esophagus). All treatments were delivered with a CT-linac 506c (UIH, Shanghai) using 6 MV photon beams. This machine is equipped with diagnosis-level fan-beam CT and an amorphous silicon EPID XRD1642 (Varex Imaging Corporation, UT, USA). A Monte Carlo algorithm was developed to calculate the transmit EPID image. A pretreatment measurement was performed to assess system accuracy by delivering based on a homogeneous phantom (RW3 slab, PTW, Freiburg). During treatment, each patient underwent CT scanning before delivery either once or twice for a total of 268 fractions obtained daily CT images. Patients may have had a position correction that followed our image-guided radiation therapy (IGRT) procedure. Meanwhile, transmit EPID images were acquired for each field during delivery. After treatment, all patient CTs were reviewed to ensure that there was no large anatomical change between planning and treatment. The reference of transmit EPID images was calculated based on both planning and daily CTs, and the IGRT correction was corrected for the EPID calculation. The gamma passing rate (3 mm 3%, 2 mm 3%, and 2 mm 2%) was calculated and compared between the planning CT and daily CT. Mechanical errors [ ± 1 mm, ± 2 mm, ± 5 mm multileaf collimator (MLC) systematic shift and 3%, 5% monitor unit (MU) scaling] were also introduced in this study for comparing detectability between both types of CT. Result The average (standard deviation) gamma passing rate (3 mm 3%, 2 mm 3%, and 2 mm 2%) in the RW3 slab phantom was 99.6% ± 1.0%, 98.9% ± 2.1%, and 97.2% ± 3.9%. For patient measurement, the average (standard deviation) gamma passing rates were 87.8% ± 14.0%, 82.2% ± 16.9%, and 74.2% ± 18.9% for using planning CTs as reference and 93.6% ± 8.2%, 89.7% ± 11.0%, and 82.8% ± 14.7% for using daily CTs as reference. There were significant differences between the planning CT and daily CT results. All p-values (Mann–Whitney test) were less than 0.001. In terms of error simulation, nonparametric test shows that there were significant differences between practical daily results and error simulation results (p < 0.001). The receiver operating characteristic (ROC) analysis indicated that the detectability of mechanical delivery error using daily CT was better than that of planning CT. AUCDaily CT = 0.63–0.96 and AUCPlanning CT = 0.49–0.93 in MLC systematic shift and AUCDaily CT = 0.56–0.82 and AUCPlanning CT = 0.45–0.73 in MU scaling. Conclusion This study shows the feasibility and effectiveness of using two-dimensional (2D) EPID portal image and daily CT-based in vivo dosimetry for intensity-modulated radiation therapy (IMRT) verification during treatment. The daily CT-based in vivo dosimetry has better sensitivity and specificity to identify the variation of IMRT in MLC-related and dose-related errors than planning CT-based.
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Affiliation(s)
- Bin Feng
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Lei Yu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Enwei Mo
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Liyuan Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Jun Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
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13
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Implementation of in-vivo diode dosimetry for intensity modulated radiotherapy as routine patients' quality assurance. Radiat Phys Chem Oxf Engl 1993 2021. [DOI: 10.1016/j.radphyschem.2021.109564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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14
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Olaciregui-Ruiz I, Vivas-Maiques B, van der Velden S, Nowee ME, Mijnheer B, Mans A. Automatic dosimetric verification of online adapted plans on the Unity MR-Linac using 3D EPID dosimetry. Radiother Oncol 2021; 157:241-246. [PMID: 33582193 DOI: 10.1016/j.radonc.2021.01.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/18/2021] [Accepted: 01/27/2021] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND PURPOSE The Unity MR-Linac is equipped with an EPID, the images from which contain information about the dose delivered to the patient. The purpose of this study was to introduce a framework for the automatic dosimetric verification of online adapted plans using 3D EPID dosimetry and to present the obtained dosimetric results. MATERIALS AND METHODS The framework was active during the delivery of 1207 online adapted plans corresponding to 127 clinical IMRT treatments (74 prostate, 19 rectum, 19 liver and 15 lymph node oligometastases). EPID reconstructed dose distributions in the patient geometry were calculated automatically and then compared to the dose distributions calculated online by the treatment planning system (TPS). The comparison was performed by γ-analysis (3% global/2mm/10% threshold) and by the difference in median dose to the high-dose volume (ΔHDVD50). 85% for γ-pass rate and 5% for ΔHDVD50 were used as tolerance limit values. RESULTS 93% of the online plans were verified automatically by the framework. Missing EPID data was the reason for automation failure. 91% of the verified plans were within tolerance. CONCLUSION Automatic dosimetric verification of online adapted plans on the Unity MR-Linac is feasible using in vivo 3D EPID dosimetry. Almost all online adapted plans were approved automatically by the framework. This newly developed framework is a major step forward towards the clinical implementation of a permanent safety net for the entire online adaptive workflow.
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Affiliation(s)
- Igor Olaciregui-Ruiz
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
| | - Begoña Vivas-Maiques
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Sandra van der Velden
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Marlies E Nowee
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Ben Mijnheer
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Anton Mans
- Department of Radiation Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
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15
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Ślosarek K, Plaza D, Nas A, Reudelsdorf M, Wendykier J, Bekman B, Grządziel A. Portal dosimetry in radiotherapy repeatability evaluation. J Appl Clin Med Phys 2020; 22:156-164. [PMID: 33314643 PMCID: PMC7856497 DOI: 10.1002/acm2.13123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/17/2020] [Accepted: 11/20/2020] [Indexed: 01/10/2023] Open
Abstract
The accuracy of radiotherapy is the subject of continuous discussion, and dosimetry methods, particularly in dynamic techniques, are being developed. At the same time, many oncology centers develop quality procedures, including pretreatment and online dose verification and proper patient tracking methods. This work aims to present the possibility of using portal dosimetry in the assessment of radiotherapy repeatability. The analysis was conducted on 74 cases treated with dynamic techniques. Transit dosimetry was made for each collision‐free radiation beam. It allowed the comparison of summary fluence maps, obtained for fractions with the corresponding summary maps from all other treatment fractions. For evaluation of the compatibility in the fluence map pairs (6798), the gamma coefficient was calculated. The results were considered in four groups, depending on the used radiotherapy technique: stereotactic fractionated radiotherapy, breath‐hold, free‐breathing, and conventionally fractionated other cases. The chi2 or Fisher's exact test was made depending on the size of the analyzed set and also Mann–Whitney U‐test was used to compare treatment repeatability of different techniques. The aim was to test whether the null hypothesis of error‐free therapy was met. The patient is treated repeatedly if the P‐value in all the fluence maps sets is higher than the level of 0.01. The best compatibility between treatment fractions was obtained for the stereotactic technique. The technique with breath‐holding gave the lowest percentage of compliance of the analyzed fluence pairs. The results indicate that the repeatability of the treatment is associated with the radiotherapy technique. Treated volume location is also an essential factor found in the evaluation of treatment accuracy. The EPID device is a useful tool in assessing the repeatability of radiotherapy. The proposed method of fluence maps comparison also allows us to assess in which therapeutic session the patient was treated differently from the other fractions.
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Affiliation(s)
- Krzysztof Ślosarek
- Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland
| | - Dominika Plaza
- Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland
| | - Aleksandra Nas
- Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland
| | - Marta Reudelsdorf
- Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland
| | - Jacek Wendykier
- Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland
| | - Barbara Bekman
- Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland
| | - Aleksandra Grządziel
- Radiotherapy Planning Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland
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16
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Wolfs CJA, Canters RAM, Verhaegen F. Identification of treatment error types for lung cancer patients using convolutional neural networks and EPID dosimetry. Radiother Oncol 2020; 153:243-249. [PMID: 33011206 DOI: 10.1016/j.radonc.2020.09.048] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 09/16/2020] [Accepted: 09/26/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND/PURPOSE Electronic portal imaging device (EPID) dosimetry aims to detect treatment errors, potentially leading to treatment adaptation. Clinically used threshold classification methods for detecting errors lead to loss of information (from multi-dimensional EPID data to a few numbers) and cannot be used for identifying causes of errors. Advanced classification methods, such as deep learning, can use all available information. In this study, convolutional neural networks (CNNs) were trained to detect and identify error type and magnitude of simulated treatment errors in lung cancer patients. The purpose of this simulation study is to provide a proof-of-concept of CNNs for error identification using EPID dosimetry in an in vivo scenario. MATERIALS AND METHODS Clinically realistic ranges of anatomical changes, positioning errors and mechanical errors were simulated for lung cancer patients. Predicted portal dose images (PDIs) containing errors were compared to error-free PDIs using the widely used gamma analysis. CNNs were trained to classify errors using 2D gamma maps. Three classification levels were assessed: Level 1 (main error type, e.g., anatomical change), Level 2 (error subtype, e.g., tumor regression) and Level 3 (error magnitude, e.g., >50% tumor regression). RESULTS CNNs showed good performance for all classification levels (training/test accuracy 99.5%/96.1%, 92.5%/86.8%, 82.0%/72.9%). For Level 3, overfitting became more apparent. CONCLUSION This simulation study indicates that deep learning is a promising powerful tool for identifying types and magnitude of treatment errors with EPID dosimetry, providing additional information not currently available from EPID dosimetry. This is a first step towards rapid, automated models for identification of treatment errors using EPID dosimetry.
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Affiliation(s)
- Cecile J A Wolfs
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Richard A M Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands.
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Olaciregui-Ruiz I, Beddar S, Greer P, Jornet N, McCurdy B, Paiva-Fonseca G, Mijnheer B, Verhaegen F. In vivo dosimetry in external beam photon radiotherapy: Requirements and future directions for research, development, and clinical practice. Phys Imaging Radiat Oncol 2020; 15:108-116. [PMID: 33458335 PMCID: PMC7807612 DOI: 10.1016/j.phro.2020.08.003] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 08/17/2020] [Accepted: 08/18/2020] [Indexed: 11/18/2022] Open
Abstract
External beam radiotherapy with photon beams is a highly accurate treatment modality, but requires extensive quality assurance programs to confirm that radiation therapy will be or was administered appropriately. In vivo dosimetry (IVD) is an essential element of modern radiation therapy because it provides the ability to catch treatment delivery errors, assist in treatment adaptation, and record the actual dose delivered to the patient. However, for various reasons, its clinical implementation has been slow and limited. The purpose of this report is to stimulate the wider use of IVD for external beam radiotherapy, and in particular of systems using electronic portal imaging devices (EPIDs). After documenting the current IVD methods, this report provides detailed software, hardware and system requirements for in vivo EPID dosimetry systems in order to help in bridging the current vendor-user gap. The report also outlines directions for further development and research. In vivo EPID dosimetry vendors, in collaboration with users across multiple institutions, are requested to improve the understanding and reduce the uncertainties of the system and to help in the determination of optimal action limits for error detection. Finally, the report recommends that automation of all aspects of IVD is needed to help facilitate clinical adoption, including automation of image acquisition, analysis, result interpretation, and reporting/documentation. With the guidance of this report, it is hoped that widespread clinical use of IVD will be significantly accelerated.
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Affiliation(s)
- Igor Olaciregui-Ruiz
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sam Beddar
- Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Greer
- Calvary Mater Newcastle Hospital and University of Newcastle, Newcastle, New South Wales, Australia
| | - Nuria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Boyd McCurdy
- Medical Physics Department, CancerCare Manitoba, Winnipeg, Manitoba, Canada
| | - Gabriel Paiva-Fonseca
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Ben Mijnheer
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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A new registration algorithm of electronic portal imaging devices images based on the automatic detection of bone edges during radiotherapy. Sci Rep 2020; 10:10253. [PMID: 32581340 PMCID: PMC7314748 DOI: 10.1038/s41598-020-67331-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Accepted: 06/03/2020] [Indexed: 11/12/2022] Open
Abstract
The precision and efficiency of the registration of megavolt-level electronic portal imaging devices (EPID) images with the naked eye in the orthogonal window are reduced. This study aims to develop a new registration algorithm with enhanced accuracy and efficiency. Ten setup errors with different translation and rotation were simulated with the phantom. For each error, one set of simulated computer tomography images and EPID images were acquired and registered with the traditional and the new method. The traditional method was performed by two senior physicists with the Varian Offline Review software. The new method is basing on the comparison of the precise contours of the same bone structure in the digital reconstruction radiography images and the EPID images, and the contours were fitted with an automatic edge detection algorithm based on gradient images. The average error of the new method was decreased by 44.44%, 28.33%, 49.09% in the translation of X, Y, and Z axes (The traditional vs. the new: X axes, 0.45 mm vs. 0.25 mm; Y axes, 0.75 mm vs. 0.35 mm; Z axes, 0.55 mm vs. 0.28 mm), 42.86% and 40.48% in the rotation of X and Z axes (The traditional vs. the new: X axes, 0.49° vs. 0.28°; Z axes, 0.42° vs. 0.25°), respectively. The average elapsed time in the new method was reduced by 11.14% (The traditional vs. the new: 44 s vs. 39.1 s). The new registration method has significant advantages of accuracy and efficiency compared with the traditional method.
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