Mahasweta Bhattacharya, Calin Reamy, Heng Li, Junghoon Lee, William T. Hrinivich
{"title":"一个Python包快速基于gpu的质子铅笔束剂量计算。","authors":"Mahasweta Bhattacharya, Calin Reamy, Heng Li, Junghoon Lee, William T. Hrinivich","doi":"10.1002/acm2.70093","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Purpose</h3>\n \n <p>Open-source GPU-based Monte Carlo (MC) proton dose calculation algorithms provide high speed and unparalleled accuracy but can be complex to integrate with new applications and remain slower than GPU-based pencil beam (PB) methods, which sacrifice some physical accuracy for sub-second plan calculation. We developed and validated a Python package implementing a GPU-based double Gaussian PB algorithm for intensity-modulated proton therapy (IMPT) planning research applications requiring a simple, widely compatible, and ultra-fast proton dose calculation solution.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Beam parameters were derived from pristine Bragg peaks generated with MC for 98 energies in our clinical treatment planning system (TPS). We validated the PB approach against measurements by comparing lateral spot profiles (using single-Gaussian sigma) and proton ranges (using R80) for pristine Bragg peaks, as well as spread-out Bragg peaks (SOBPs) in water. Further comparisons of PB and MC from the TPS were performed in a heterogeneous digital phantom and patient plans for four cancer sites using 3D gamma passing rates and dose metrics.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The PB algorithm enabled dose calculation following a single Python import statement. Mean ± standard deviation (SD) errors in sigma, R80, and SOBP dose were 0.05 ± 0.01, 0.0 ± 0.1 mm, and 0.4 ± 1.1%, respectively. Mean ± SD patient plan computation time was 0.28 ± 0.07 s for PB versus 4.68 ± 2.68 s for MC. Mean ± SD gamma passing rate at 2%/2 mm criteria was 96.0 ± 5.1%, and the mean ± SD percent difference in dose metrics was 0.5 ± 3.6%. PB accuracy degraded beyond bone and lung boundaries, characterized by inaccuracies in lateral proton scatter.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>We developed a GPU-based proton PB algorithm compiled as a Python package, providing simple beam modeling, interface, and fast dose calculation for IMPT plan optimization research and development. Like other PB algorithms, accuracy is limited in highly heterogeneous regions such as the thorax.</p>\n </section>\n </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 6","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70093","citationCount":"0","resultStr":"{\"title\":\"A Python package for fast GPU-based proton pencil beam dose calculation\",\"authors\":\"Mahasweta Bhattacharya, Calin Reamy, Heng Li, Junghoon Lee, William T. Hrinivich\",\"doi\":\"10.1002/acm2.70093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>Open-source GPU-based Monte Carlo (MC) proton dose calculation algorithms provide high speed and unparalleled accuracy but can be complex to integrate with new applications and remain slower than GPU-based pencil beam (PB) methods, which sacrifice some physical accuracy for sub-second plan calculation. We developed and validated a Python package implementing a GPU-based double Gaussian PB algorithm for intensity-modulated proton therapy (IMPT) planning research applications requiring a simple, widely compatible, and ultra-fast proton dose calculation solution.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Beam parameters were derived from pristine Bragg peaks generated with MC for 98 energies in our clinical treatment planning system (TPS). We validated the PB approach against measurements by comparing lateral spot profiles (using single-Gaussian sigma) and proton ranges (using R80) for pristine Bragg peaks, as well as spread-out Bragg peaks (SOBPs) in water. Further comparisons of PB and MC from the TPS were performed in a heterogeneous digital phantom and patient plans for four cancer sites using 3D gamma passing rates and dose metrics.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The PB algorithm enabled dose calculation following a single Python import statement. Mean ± standard deviation (SD) errors in sigma, R80, and SOBP dose were 0.05 ± 0.01, 0.0 ± 0.1 mm, and 0.4 ± 1.1%, respectively. Mean ± SD patient plan computation time was 0.28 ± 0.07 s for PB versus 4.68 ± 2.68 s for MC. Mean ± SD gamma passing rate at 2%/2 mm criteria was 96.0 ± 5.1%, and the mean ± SD percent difference in dose metrics was 0.5 ± 3.6%. PB accuracy degraded beyond bone and lung boundaries, characterized by inaccuracies in lateral proton scatter.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>We developed a GPU-based proton PB algorithm compiled as a Python package, providing simple beam modeling, interface, and fast dose calculation for IMPT plan optimization research and development. 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A Python package for fast GPU-based proton pencil beam dose calculation
Purpose
Open-source GPU-based Monte Carlo (MC) proton dose calculation algorithms provide high speed and unparalleled accuracy but can be complex to integrate with new applications and remain slower than GPU-based pencil beam (PB) methods, which sacrifice some physical accuracy for sub-second plan calculation. We developed and validated a Python package implementing a GPU-based double Gaussian PB algorithm for intensity-modulated proton therapy (IMPT) planning research applications requiring a simple, widely compatible, and ultra-fast proton dose calculation solution.
Methods
Beam parameters were derived from pristine Bragg peaks generated with MC for 98 energies in our clinical treatment planning system (TPS). We validated the PB approach against measurements by comparing lateral spot profiles (using single-Gaussian sigma) and proton ranges (using R80) for pristine Bragg peaks, as well as spread-out Bragg peaks (SOBPs) in water. Further comparisons of PB and MC from the TPS were performed in a heterogeneous digital phantom and patient plans for four cancer sites using 3D gamma passing rates and dose metrics.
Results
The PB algorithm enabled dose calculation following a single Python import statement. Mean ± standard deviation (SD) errors in sigma, R80, and SOBP dose were 0.05 ± 0.01, 0.0 ± 0.1 mm, and 0.4 ± 1.1%, respectively. Mean ± SD patient plan computation time was 0.28 ± 0.07 s for PB versus 4.68 ± 2.68 s for MC. Mean ± SD gamma passing rate at 2%/2 mm criteria was 96.0 ± 5.1%, and the mean ± SD percent difference in dose metrics was 0.5 ± 3.6%. PB accuracy degraded beyond bone and lung boundaries, characterized by inaccuracies in lateral proton scatter.
Conclusion
We developed a GPU-based proton PB algorithm compiled as a Python package, providing simple beam modeling, interface, and fast dose calculation for IMPT plan optimization research and development. Like other PB algorithms, accuracy is limited in highly heterogeneous regions such as the thorax.
期刊介绍：
Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission.
JACMP will publish:
-Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500.
-Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed.
-Technical Notes: These should be no longer than 3000 words, including key references.
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-Book Reviews: The editorial office solicits Book Reviews.
-Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics.
-Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
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