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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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Rostamzadeh M, Thomas S, Camborde M, Karan T, Liu M, Ma R, Mestrovic A, Gill B, Tai I, Bergman A. Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets. J Appl Clin Med Phys 2024; 25:e14161. [PMID: 37789572 PMCID: PMC10860457 DOI: 10.1002/acm2.14161] [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: 02/06/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
PURPOSE To assess the feasibility of using the diaphragm as a surrogate for liver targets during MDTT. METHODS Diaphragm as surrogate for markers: a dome-shaped phantom with implanted markers was fabricated and underwent dual-orthogonal fluoroscopy sequences on the Vero4DRT linac. Ten patients participated in an IRB-approved, feasibility study to assess the MDTT workflow. All images were analyzed using an in-house program to back-project the diaphragm/markers position to the isocenter plane. ExacTrac imager log files were analyzed. Diaphragm as tracking structure for MDTT: The phantom "diaphragm" was contoured as a markerless tracking structure (MTS) and exported to Vero4DRT/ExacTrac. A single field plan was delivered to the phantom film plane under static and MDTT conditions. In the patient study, the diaphragm tracking structure was contoured on CT breath-hold-exhale datasets. The MDTT workflow was applied until just prior to MV beam-on. RESULTS Diaphragm as surrogate for markers: phantom data confirmed the in-house 3D back-projection program was functioning as intended. In patients, the diaphragm/marker relative positions had a mean ± RMS difference of 0.70 ± 0.89, 1.08 ± 1.26, and 0.96 ± 1.06 mm in ML, SI, and AP directions. Diaphragm as tracking structure for MDTT: Building a respiratory-correlation model using the diaphragm as surrogate for the implanted markers was successful in phantom/patients. During the tracking verification imaging step, the phantom mean ± SD difference between the image-detected and predicted "diaphragm" position was 0.52 ± 0.18 mm. The 2D film gamma (2%/2 mm) comparison (static to MDTT deliveries) was 98.2%. In patients, the mean difference between the image-detected and predicted diaphragm position was 2.02 ± 0.92 mm. The planning target margin contribution from MDTT diaphragm tracking is 2.2, 5.0, and 4.7 mm in the ML, SI, and AP directions. CONCLUSION In phantom/patients, the diaphragm motion correlated well with markers' motion and could be used as a surrogate. MDTT workflows using the diaphragm as the MTS is feasible using the Vero4DRT linac and could replace the need for implanted markers for liver radiotherapy.
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Affiliation(s)
- Maryam Rostamzadeh
- Department of Physics and AstronomyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Steven Thomas
- Medical Physics DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
| | | | - Tania Karan
- Medical Physics DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
| | - Mitchell Liu
- Radiation Oncology DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
| | - Roy Ma
- Radiation Oncology DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
| | - Ante Mestrovic
- Medical Physics DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
| | - Bradford Gill
- Medical Physics DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
| | - Isaac Tai
- Radiation Therapy DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
| | - Alanah Bergman
- Medical Physics DepartmentBC Cancer‐VancouverVancouverBritish ColumbiaCanada
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Ting LL, Guo ML, Liao AH, Cheng ST, Yu HW, Ramanathan S, Zhou H, Boominathan CM, Jeng SC, Chiou JF, Kuo CC, Chuang HC. Development and evaluation of ultrasound image tracking technology based on Mask R-CNN applied to respiratory motion compensation system. Quant Imaging Med Surg 2023; 13:6827-6839. [PMID: 37869357 PMCID: PMC10585533 DOI: 10.21037/qims-23-23] [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: 01/05/2023] [Accepted: 08/18/2023] [Indexed: 10/24/2023]
Abstract
Background For respiration induced tumor displacement during a radiation therapy, a common method to prevent the extra radiation is image-guided radiation therapy. Moreover, mask region-based convolutional neural networks (Mask R-CNN) is one of the state-of-the-art (SOTA) object detection frameworks capable of conducting object classification, localization, and pixel-level instance segmentation. Methods We developed a novel ultrasound image tracking technology based on Mask R-CNN for stable tracking of the detected diaphragm motion and applied to the respiratory motion compensation system (RMCS). For training Mask R-CNN, 1800 ultrasonic images of the human diaphragm are collected. Subsequently, an ultrasonic image tracking algorithm was developed to compute the mean pixel coordinates of the diaphragm detected by Mask R-CNN. These calculated coordinates are then utilized by the RMCS for compensation purposes. The tracking similarity verification experiment of mask ultrasonic imaging tracking algorithm (M-UITA) is performed. Results The correlation between the input signal and the signal tracked by M-UITA was evaluated during the experiment. The average discrete Fréchet distance was less than 4 mm. Subsequently, a respiratory displacement compensation experiment was conducted. The proposed method was compared to UITA, and the compensation rates of three different respiratory signals were calculated and compared. The experimental results showed that the proposed method achieved a 6.22% improvement in compensation rate compared to UITA. Conclusions This study introduces a novel method called M-UITA, which offers high tracking precision and excellent stability for monitoring diaphragm movement. Additionally, it eliminates the need for manual parameter adjustments during operation, which is an added advantage.
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Affiliation(s)
- Lai-Lei Ting
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan
| | - Ming-Lu Guo
- Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Ai-Ho Liao
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
- Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan
| | - Sen-Ting Cheng
- Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Hsiao-Wei Yu
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan
- School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Subramaninan Ramanathan
- Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Hong Zhou
- Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka, Japan
| | | | - Shiu-Chen Jeng
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jeng-Fong Chiou
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan
| | - Chia-Chun Kuo
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Ho-Chiao Chuang
- Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan
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Combining the wavelet transform with a phase-lead compensator to a respiratory motion compensation system with an ultrasound tracking technique in radiation therapy. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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