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172 results on '"Guan, Fada"'

1. Optimization of FLASH Proton Beams Using a Track-Repeating Algorithm

Author

Wang, Qianxia, Titt, Uwe, Mohan, Radhe, Guan, Fada, Zhao, Yao, Yang, Ming, and Yepes, Pablo

Subjects
Physics - Medical Physics
Abstract

Methods: A phase space file in a plane at 202 mm downstream of the beam exit window is generated through tuning parameters to match FDC results with measured or MCNPX Monte Carlo-simulated integrated depth-dose distribution (IDD) and lateral dose profiles. To spread out the Bragg peak, widen the beam and reduce the penumbra, a ridge filter (RF), a high-Z material scatterer and a collimator with compensator are inserted in the beam path and their shapes and sizes have been optimized. The FDC calculations are validated by comparing Geant4 Monte Carlo simulations. In addition, a set of algorithms to automatically choose the optimum dimensions of the beam shaping elements is developed and tested using the same beams. At the last part, dose rates for optimized beams were estimated by scaling their dose distributions to that of their original beams. Results: The optimized 86.4 MeV beam had an 8.5 mm wide spread-out Bragg peak (SOBP) (proximal 90% to distal 90% of the maximum dose), 14.5 mm, 12.0 mm and 11.0 lateral widths with dose above 50%, 80% and 90% respectively and a 2.5 mm penumbra from 80% to 20% in the lateral profile for the energy. The 159.5 MeV beam had a SOBP of 39.0 mm and the lateral widths with dose above 50%, 80% and 90% of 20.5 mm, 15.0 and 12.5 mm when the source to surface distance (SSD) was 550 mm. Wider lateral widths was obtained with increased SSD. The FDC calculations had passing rates higher than 96% using 3mm/3% as the gamma-index criterion comparing with Geant4 simulations for both energies. The set of automatic algorithms can choose the proper dimensions for the high-density scatterer, RF, collimator and compensator efficiently. And the optimized 159.5 MeV beam with different SDDs had entrance dose rate higher than 40 Gy/s if the entrance dose rate of the original beam was 150 Gy/s., Comment: 15 pages, 7 figures, two tables

Published
2021
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3. Looking on the horizon; potential and unique approaches to developing radiation countermeasures for deep space travel

Author

Bokhari, Rihana S., Beheshti, Afshin, Blutt, Sarah E., Bowles, Dawn E., Brenner, David, Britton, Robert, Bronk, Lawrence, Cao, Xu, Chatterjee, Anushree, Clay, Delisa E., Courtney, Colleen, Fox, Donald T., Gaber, M.Waleed, Gerecht, Sharon, Grabham, Peter, Grosshans, David, Guan, Fada, Jezuit, Erin A., Kirsch, David G., Liu, Zhandong, Maletic-Savatic, Mirjana, Miller, Kyle M., Montague, Ruth A., Nagpal, Prashant, Osenberg, Sivan, Parkitny, Luke, Pierce, Niles A., Porada, Christopher, Rosenberg, Susan M., Sargunas, Paul, Sharma, Sadhana, Spangler, Jamie, Tavakol, Daniel Naveed, Thomas, Dilip, Vunjak-Novakovic, Gordana, Wang, Chunbo, Whitcomb, Luke, Young, Damian W., and Donoviel, Dorit

Published
2022
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6. Predicting the PSQA results of volumetric modulated arc therapy based on dosiomics features: a multi-center study.

Author

Ni, Qianxi, Chen, Luqiao, Tan, Jianfeng, Pang, Jinmeng, Luo, Longjun, Zhu, Jun, Yang, Xiaohua, Wei, Wei, Li, Xiadong, and Guan, Fada

Subjects
VOLUMETRIC-modulated arc therapy, BOOSTING algorithms, MACHINE learning, RADIOTHERAPY treatment planning, INTENSITY modulated radiotherapy, FEATURE selection
Abstract

Backgroud and objectives: The implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model's performance was compared by evaluating the impact of two distinct feature selection methods. Methods: A retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, y analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model's classification performance was assessed through F1-score and area under the curve (AUC) values. Results: The model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible. Conclusion: Based on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Characterization of GafchromicTM EBT4 film with clinical kV/MV photons and MeV electrons.

Author

Guan, Fada, Chen, Huixiao, Draeger, Emily, Li, Yuting, Aydin, Resat, Tien, Christopher J., and Chen, Zhe

Subjects
RADIATION therapy equipment, EXPERIMENTAL design, X-rays, ELECTRONS, NOISE, RADIATION, UNCERTAINTY, QUANTITATIVE research, GOODNESS-of-fit tests, DIAGNOSTIC imaging, ELECTROMAGNETIC fields, RADIATION doses, RADIOTHERAPY, RADIATION dosimetry
Abstract

Objective: A newly developed GafchromicTM EBT4 film model was released recently. According to the vendor‐stated specifications, this new model maintains major features of its more recent predecessor, EBT3, but has a great improvement in the signal‐to‐noise ratio (SNR). The purpose of this study was to fully characterize the EBT4 model by testing its dose response dependence on beam energy using clinical photon and electron beams and validating its improvement in SNR. Methods: In the beam energy dependence experiments, 6 MV and 15 MV photon beams, 6 MeV, 12 MeV and 20 MeV electron beams, 120 kVp and two 250 kVp x‐ray beams (with different half‐layer values) were selected, representing different clinical beam energies and radiation types. In the noise and SNR analyses, only the 6 MV photon beam was selected as the representative to compare between EBT4 and EBT3 models. Results: The energy dependence test results showed that there was a slight difference (<2% at most dose levels) in the dose response among the clinical MV photons and MeV electrons. However, a large deviation (>10%) in the dose response was found for the 120 kVp x‐ray beam. For the two 250 kVp x‐ray beams, the difference in the dose response was moderate (<5%), but not negligible. The noise and SNR results showed a reduced noise and strengthened SNR in EBT4. Conclusion: We conclude that the EBT4 film has a minimal energy dependence for MV photon and MeV electron beams but has a notable energy dependence for kilovoltage x‐ray beams, and the vendor‐stated features of the reduced noise and improved SNR in EBT4 are validated. [ABSTRACT FROM AUTHOR]

Published
2023
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26. PyMedPhys: A community effort to develop an open, Python-based standard library for medical physics applications

Author

Biggs, Simon, primary, Jennings, Matthew, additional, Swerdloff, Stuart, additional, Chlap, Phillip, additional, Lane, Derek, additional, Rembish, Jacob, additional, McAloney, Jacob, additional, King, Paul, additional, Ayala, Rafael, additional, Guan, Fada, additional, Lambri, Nicola, additional, Crewson, Cody, additional, and Sobolewski, Matthew, additional

Published
2022
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28. Adaptation and dosimetric commissioning of a synchrotron-based proton beamline for FLASH experiments

Author

Yang, Ming, primary, Wang, Xiaochun, additional, Guan, Fada, additional, Titt, Uwe, additional, Iga, Kiminori, additional, Jiang, Dadi, additional, Takaoka, Takeshi, additional, Tootake, Satoshi, additional, Katayose, Tadashi, additional, Umezawa, Masumi, additional, Schüler, Emil, additional, Frank, Steven, additional, Lin, Steven H, additional, Sahoo, Narayan, additional, Koong, Albert C, additional, Mohan, Radhe, additional, and Zhu, X Ronald, additional

Published
2022
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29. Roadmap: helium ion therapy

Author

Mairani, Andrea, primary, Mein, Stewart, additional, Blakely, Eleanor, additional, Debus, Jürgen, additional, Durante, Marco, additional, Ferrari, Alfredo, additional, Fuchs, Hermann, additional, Georg, Dietmar, additional, Grosshans, David R, additional, Guan, Fada, additional, Haberer, Thomas, additional, Harrabi, Semi, additional, Horst, Felix, additional, Inaniwa, Taku, additional, Karger, Christian P, additional, Mohan, Radhe, additional, Paganetti, Harald, additional, Parodi, Katia, additional, Sala, Paola, additional, Schuy, Christoph, additional, Tessonnier, Thomas, additional, Titt, Uwe, additional, and Weber, Ulrich, additional

Published
2022
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33. Impact of dose calculation accuracy on inverse linear energy transfer optimization for intensity‐modulated proton therapy.

Author

Chen, Mei, Cao, Wenhua, Yepes, Pablo, Guan, Fada, Poenisch, Falk, Xu, Cheng, Chen, Jiayi, Li, Yupeng, Vazquez, Ivan, Yang, Ming, Zhu, X. Ronald, and Zhang, Xiaodong

Subjects
STATISTICS, RETROSPECTIVE studies, COMPARATIVE studies, PROTON therapy, RADIATION doses, ENERGY transfer, RESEARCH funding, DESCRIPTIVE statistics, DATA analysis, DATA analysis software, PROSTATE tumors, ALGORITHMS
Abstract

Objective: To determine the effect of dose calculation accuracy on inverse linear energy transfer (LET) optimization for intensity‐modulated proton therapy, and to determine whether adding more beams would improve the plan robustness to different dose calculation engines. Methods: Two sets of intensity‐modulated proton therapy plans using two, four, six, and nine beams were created for 10 prostate cancer patients: one set was optimized with dose constraints (DoseOpt) using the pencil beam (PB) algorithm, and the other set was optimized with additional LET constraints (LETOpt) using the Monte Carlo (MC) algorithm. Dose distributions of DoseOpt plans were then recalculated using the MC algorithm, and the LETOpt plans were recalculated using the PB algorithm. Dosimetric indices of targets and critical organs were compared between the PB and MC algorithms for both sets of plans. Results: For DoseOpt plans, dose differences between the PB and MC algorithms were minimal. However, the maximum dose differences in LETOpt plans were 11.11% and 15.85% in the dose covering 98% and 2% (D2) of the clinical target volume, respectively. Furthermore, the dose to 1 cc of the bladder differed by 11.42 Gy (relative biological effectiveness). Adding more beams reduced the discrepancy in target coverage, but the errors in D2 of the structure were increased with the number of beams. Conclusion: High modulation of LET requires high dose calculation accuracy during the optimization and final dose calculation in the inverse treatment planning for intensity‐modulated proton therapy, and adding more beams did not improve the plan robustness to different dose calculation algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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41. Mapping the Relative Biological Effectiveness of Proton, Helium and Carbon Ions with High-Throughput Techniques

Author

Bronk, Lawrence, primary, Guan, Fada, additional, Patel, Darshana, additional, Ma, Duo, additional, Kroger, Benjamin, additional, Wang, Xiaochun, additional, Tran, Kevin, additional, Yiu, Joycelyn, additional, Stephan, Clifford, additional, Debus, Jürgen, additional, Abdollahi, Amir, additional, Jäkel, Oliver, additional, Mohan, Radhe, additional, Titt, Uwe, additional, and Grosshans, David R., additional

Published
2020
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42. Investigation of the dose perturbation effect for therapeutic beams with the presence of a 1.5 T transverse magnetic field in magnetic resonance imaging-guided radiotherapy

Author

Shao, Wencheng, Tang, Xiaobin, Bai, Yanling, Shu, Diyun, Geng, Changran, Gong, Chunhui, and Guan, Fada

Subjects
Protons -- Health aspects, Cancer -- Research, Electrons -- Research, Radiotherapy -- Usage, Magnetic resonance imaging -- Usage, Health
Abstract

Byline: Wencheng. Shao, Xiaobin. Tang, Yanling. Bai, Diyun. Shu, Changran. Geng, Chunhui. Gong, Fada. Guan Background: Magnetic resonance imaging (MRI)-guided radiotherapy is a promising image-guided cancer radiotherapy method. For MRI-guided [...]

Published
2018

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