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Chang Y, Liang Y, Wu H, Li L, Yang B, Jiang L, Ren Q, Pei X. Adaptive assessment based on fractional CBCT images for cervical cancer. J Appl Clin Med Phys 2024:e14462. [PMID: 39072895 DOI: 10.1002/acm2.14462] [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: 01/30/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024] Open
Abstract
PURPOSE Anatomical and other changes during radiotherapy will cause inaccuracy of dose distributions, therefore the expectation for online adaptive radiation therapy (ART) is high in effectively reducing uncertainties due to intra-variation. However, ART requires extensive time and effort. This study investigated an adaptive assessment workflow based on fractional cone-beam computed tomography (CBCT) images. METHODS Image registration, synthetic CT (sCT) generation, auto-segmentation, and dose calculation were implemented and integrated into ArcherQA Adaptive Check. The rigid registration was based on ITK open source. The deformable image registration (DIR) method was based on a 3D multistage registration network, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN). The auto-segmentation of organs at risk (OARs) on sCT images was finished by a deep learning-based auto-segmentation software, DeepViewer. The contours of targets were obtained by the structure-guided registration. Finally, the dose calculation was based on a GPU-based Monte Carlo (MC) dose code, ArcherQA. RESULTS The dice similarity coefficient (DSCs) were over 0.86 for target volumes and over 0.79 for OARs. The gamma pass rate of ArcherQA versus Eclipse treatment planning system was more than 99% at the 2%/2 mm criterion with a low-dose threshold of 10%. The time for the whole process was less than 3 min. The dosimetric results of ArcherQA Adaptive Check were consistent with the Ethos scheduled plan, which can effectively identify the fractions that need the implementation of the Ethos adaptive plan. CONCLUSION This study integrated AI-based technologies and GPU-based MC technology to evaluate the dose distributions using fractional CBCT images, demonstrating remarkably high efficiency and precision to support future ART processes.
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Affiliation(s)
- Yankui Chang
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
| | - Yongguang Liang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Haotian Wu
- Anhui Wisdom Technology Company Limited, Hefei, China
| | - Lingyan Li
- Anhui Wisdom Technology Company Limited, Hefei, China
| | - Bo Yang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Lipeng Jiang
- Department of Radiation Oncology, First Affiliated Hospital of Jinzhou Medical University, Shenyang, China
| | - Qiang Ren
- Anhui Wisdom Technology Company Limited, Hefei, China
| | - Xi Pei
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
- Anhui Wisdom Technology Company Limited, Hefei, China
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Zhou P, Chang Y, Li S, Luo J, Lei L, Shang Y, Pei X, Ren Q, Chen C. Clinical application of a GPU-accelerated monte carlo dose verification for cyberknife M6 with Iris collimator. Radiat Oncol 2024; 19:86. [PMID: 38956685 PMCID: PMC11221037 DOI: 10.1186/s13014-024-02446-1] [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: 10/08/2023] [Accepted: 04/29/2024] [Indexed: 07/04/2024] Open
Abstract
PURPOSE To apply an independent GPU-accelerated Monte Carlo (MC) dose verification for CyberKnife M6 with Iris collimator and evaluate the dose calculation accuracy of RayTracing (TPS-RT) algorithm and Monte Carlo (TPS-MC) algorithm in the Precision treatment planning system (TPS). METHODS GPU-accelerated MC algorithm (ArcherQA-CK) was integrated into a commercial dose verification system, ArcherQA, to implement the patient-specific quality assurance in the CyberKnife M6 system. 30 clinical cases (10 cases in head, and 10 cases in chest, and 10 cases in abdomen) were collected in this study. For each case, three different dose calculation methods (TPS-MC, TPS-RT and ArcherQA-CK) were implemented based on the same treatment plan and compared with each other. For evaluation, the 3D global gamma analysis and dose parameters of the target volume and organs at risk (OARs) were analyzed comparatively. RESULTS For gamma pass rates at the criterion of 2%/2 mm, the results were over 98.0% for TPS-MC vs.TPS-RT, TPS-MC vs. ArcherQA-CK and TPS-RT vs. ArcherQA-CK in head cases, 84.9% for TPS-MC vs.TPS-RT, 98.0% for TPS-MC vs. ArcherQA-CK and 83.3% for TPS-RT vs. ArcherQA-CK in chest cases, 98.2% for TPS-MC vs.TPS-RT, 99.4% for TPS-MC vs. ArcherQA-CK and 94.5% for TPS-RT vs. ArcherQA-CK in abdomen cases. For dose parameters of planning target volume (PTV) in chest cases, the deviations of TPS-RT vs. TPS-MC and ArcherQA-CK vs. TPS-MC had significant difference (P < 0.01), and the deviations of TPS-RT vs. TPS-MC and TPS-RT vs. ArcherQA-CK were similar (P > 0.05). ArcherQA-CK had less calculation time compared with TPS-MC (1.66 min vs. 65.11 min). CONCLUSIONS Our proposed MC dose engine (ArcherQA-CK) has a high degree of consistency with the Precision TPS-MC algorithm, which can quickly identify the calculation errors of TPS-RT algorithm for some chest cases. ArcherQA-CK can provide accurate patient-specific quality assurance in clinical practice.
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Affiliation(s)
- Peng Zhou
- Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing, China
| | - Yankui Chang
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
| | - Shijun Li
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
| | - Jia Luo
- Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing, China
| | - Lin Lei
- Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing, China
| | - Yufen Shang
- Department of Radiation Oncology, Dezhou Second People's Hospital, Dezhou, China
| | - Xi Pei
- Anhui Wisdom Technology Company Limited, Hefei, China
| | - Qiang Ren
- Anhui Wisdom Technology Company Limited, Hefei, China.
| | - Chuan Chen
- Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing, China.
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Tozuka R, Kadoya N, Arai K, Sato K, Jingu K. Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator. Radiol Phys Technol 2024; 17:451-457. [PMID: 38687457 DOI: 10.1007/s12194-024-00800-2] [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: 12/11/2023] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 05/02/2024]
Abstract
Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a deep learning (DL) system for oMRgRT to predict the gamma passing rate (GPR). This study collected 125 verification plans [reference plan (RP), 100; adapted plan (AP), 25] from patients with prostate cancer treated using Elekta Unity. Based on our previous study, we employed a convolutional neural network that predicted the GPRs of nine pairs of gamma criteria from 1%/1 mm to 3%/3 mm. First, we trained and tested the DL model using RPs (n = 75 and n = 25 for training and testing, respectively) for its optimization. Second, we tested the GPR prediction accuracy using APs to determine whether the DL model could be applied to APs. The mean absolute error (MAE) and correlation coefficient (r) of the RPs were 1.22 ± 0.27% and 0.29 ± 0.10 in 3%/2 mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2 mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1 mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2 mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2 mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1 mm, respectively. The time cost was within 3 s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.
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Affiliation(s)
- Ryota Tozuka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Kazuhiro Arai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kiyokazu Sato
- Department of Radiation Technology, Tohoku University Hospital, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
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Xu Y, Xia W, Ren W, Ma M, Men K, Dai J. Is it necessary to perform measurement-based patient-specific quality assurance for online adaptive radiotherapy with Elekta Unity MR-Linac? J Appl Clin Med Phys 2024; 25:e14175. [PMID: 37817407 PMCID: PMC10860411 DOI: 10.1002/acm2.14175] [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: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/18/2023] [Indexed: 10/12/2023] Open
Abstract
This study aimed to investigate the necessity of measurement-based patient-specific quality assurance (PSQA) for online adaptive radiotherapy by analyzing measurement-based PSQA results and calculation-based 3D independent dose verification results with Elekta Unity MR-Linac. There are two workflows for Elekta Unity enabled in the treatment planning system: adapt to position (ATP) and adapt to shape (ATS). ATP plans are those which have relatively slighter shifts from reference plans by adjusting beam shapes or weights, whereas ATS plans are the new plans optimized from the beginning with probable re-contouring targets and organs-at-risk. PSQA gamma passing rates were measured using an MR-compatible ArcCHECK diode array for 78 reference plans and corresponding 208 adaptive plans (129 ATP plans and 79 ATS plans) of Elekta Unity. Subsequently, the relationships between ATP, or ATS plans and reference plans were evaluated separately. The Pearson's r correlation coefficients between ATP or ATS adaptive plans and corresponding reference plans were also characterized using regression analysis. Moreover, the Bland-Altman plot method was used to describe the agreement of PSQA results between ATP or ATS adaptive plans and reference plans. Additionally, Monte Carlo-based independent dose verification software ArcherQA was used to perform secondary dose check for adaptive plans. For ArcCHECK measurements, the average gamma passing rates (ArcCHECK vs. TPS) of PSQA (3%/2 mm criterion) were 99.51% ± 0.88% and 99.43% ± 0.54% for ATP and ATS plans, respectively, which were higher than the corresponding reference plans 99.34% ± 1.04% (p < 0.05) and 99.20% ± 0.71% (p < 0.05), respectively. The Pearson's r correlation coefficients were 0.720 between ATP and reference plans and 0.300 between ATS and reference plans with ArcCHECK, respectively. Furthermore, >95% of data points of differences between both ATP and ATS plans and reference plans were within ±2σ (standard deviation) of the mean difference between adaptive and reference plans with ArcCHECK measurements. With ArcherQA calculation, the average gamma passing rates (ArcherQA vs. TPS) were 98.23% ± 1.64% and 98.15% ± 1.07% for ATP and ATS adaptive plans, separately. It might be unnecessary to perform measurement-based PSQA for both ATP and ATS adaptive plans for Unity if the gamma passing rates of both measurements of corresponding reference plans and independent dose verification of adaptive plans have high gamma passing rates. Periodic machine QA and verification of adaptive plans were recommended to ensure treatment safety.
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Affiliation(s)
- Yuan Xu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wenlong Xia
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wenting Ren
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Min Ma
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Kuo Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Lin J, Chen M, Lai Y, Trivedi Z, Wu J, Foo T, Gonzalez Y, Reynolds R, Park C, Yan Y, Godley A, Jiang S, Jia X, Lin MH, Pompos A, Lu W. ART2Dose: A comprehensive dose verification platform for online adaptive radiotherapy. Med Phys 2024; 51:18-30. [PMID: 37856190 DOI: 10.1002/mp.16806] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/09/2023] [Accepted: 10/07/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures. PURPOSE The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic. METHODS To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre-clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use. RESULTS With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre-treatment checks by 3-4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively. CONCLUSION Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor-intensive work.
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Affiliation(s)
- Jingying Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Mingli Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Youfang Lai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Zipalkumar Trivedi
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Junjie Wu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Tim Foo
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yesenia Gonzalez
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Robert Reynolds
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Chunjoo Park
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yulong Yan
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Andrew Godley
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Xun Jia
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Mu-Han Lin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Arnold Pompos
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Weiguo Lu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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Riis HL, Chick J, Dunlop A, Tilly D. The Quality Assurance of a 1.5 T MR-Linac. Semin Radiat Oncol 2024; 34:120-128. [PMID: 38105086 DOI: 10.1016/j.semradonc.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The recent introduction of a commercial 1.5 T MR-linac system has considerably improved the image quality of the patient acquired in the treatment unit as well as enabling online adaptive radiation therapy (oART) treatment strategies. Quality Assurance (QA) of this new technology requires new methodology that allows for the high field MR in a linac environment. The presence of the magnetic field requires special attention to the phantoms, detectors, and tools to perform QA. Due to the design of the system, the integrated megavoltage imager (MVI) is essential for radiation beam calibrations and QA. Additionally, the alignment between the MR image system and the radiation isocenter must be checked. The MR-linac system has vendor-supplied phantoms for calibration and QA tests. However, users have developed their own routine QA systems to independently check that the machine is performing as required, as to ensure we are able to deliver the intended dose with sufficient certainty. The aim of this work is therefore to review the MR-linac specific QA procedures reported in the literature.
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Affiliation(s)
- Hans Lynggaard Riis
- Department of Oncology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
| | - Joan Chick
- The Joint Department of Physics, The Royal Marsden Hospital and the Institute of Cancer Research, London, UK
| | - Alex Dunlop
- The Joint Department of Physics, The Royal Marsden Hospital and the Institute of Cancer Research, London, UK
| | - David Tilly
- Department of Immunology, Genetics and Pathology, Medical Radiation Physics, Uppsala University, Uppsala, Sweden; Medical Physics, Uppsala University Hospital, Uppsala, Sweden
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