arXiv subjects

Yankui Chang

Publications and source records attributed to Yankui Chang.

3 recordsLinked to original sources

arXiv2025

A GPU-based Monte Carlo framework for IMRT QA using EPID transit dosimetry

Purpose: We presented a GPU-based MC framework, ARCHER-EPID, specifically designed for EPID transit dosimetry, with improving accuracy and efficiency. Methods: A comprehensive MC framework was developed to perform full radiation transport simulations through three distinct zones: a detailed linear accelerator head model, a CT-based patient/phantom geometry, and a realistic, multi-layered EPID model. To convert the simulated absorbed dose to a realistic detector signal, a dose-response correction model was implemented. The framework was validated by comparing simulations against experimental measurements for 25 IMRT fields delivered to both a solid water phantom and a anthropomorphic phantom. Agreement was quantified using Gamma analysis. Results: The GPU-accelerated ARCHER-EPID framework can complete the simulation for a complex IMRT field in about 90 seconds. A 2D correction factor lookup table is generated by parameterizing radiological thickness and effective field size to account for the EPID's energy-dependent response. The data revealed that for small fields, beam hardening is the dominant effect, while for large fields, the contribution from patient-generated scatter overwhelms this effect. The average 2D gamma passing rates (3%/3 mm criteria) between simulation and measurements are 98.43% for the solid water phantom and 97.86% for the anthropomorphic phantom, respectively. Visual comparison of the images and dose profiles between simulation and measurements show a high degree of agreement. Conclusions: We have successfully developed and validated a GPU-based MC framework that provides gold-standard accuracy for EPID transit dosimetry in radiotherapy. The results demonstrate that our proposed method has potential for routine application in PSQA.

physics.med-ph↗

arXiv2024

Patient-Specific CT Doses Using DL-based Image Segmentation and GPU-based Monte Carlo Calculations for 10,281 Subjects

Computed tomography (CT) scans are a major source of medical radiation exposure worldwide. In countries like China, the frequency of CT scans has grown rapidly, particularly in routine physical examinations where chest CT scans are increasingly common. Accurate estimation of organ doses is crucial for assessing radiation risk and optimizing imaging protocols. However, traditional methods face challenges due to the labor-intensive process of manual organ segmentation and the computational demands of Monte Carlo (MC) dose calculations. In this study, we present a novel method that combines automatic image segmentation with GPU-accelerated MC simulations to compute patient-specific organ doses for a large cohort of 10,281 individuals undergoing CT examinations for physical examinations at a Chinese hospital. This is the first big-data study of its kind involving such a large population for CT dosimetry. The results show considerable inter-individual variability in CTDIvol-normalized organ doses, even among subjects with similar BMI or WED. Patient-specific organ doses vary widely, ranging from 33% to 164% normalized by the doses from ICRP Adult Reference Phantoms. Statistical analyses indicate that the "Reference Man" based average phantoms can lead to significant dosimetric uncertainties, with relative errors exceeding 50% in some cases. These findings underscore the fact that previous assessments of radiation risk may be inaccurate. It took our computational tool, on average, 135 seconds per subject, using a single NVIDIA RTX 3080 GPU card. The big-data analysis provides interesting data for improving CT dosimetry and risk assessment by avoiding uncertainties that were neglected in the past.

physics.med-ph↗

arXiv2022

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dose-volume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH criteria. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for a conventional planning model. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. In the interest of reproducibility, our data and code is freely available at https://github.com/ababier/open-kbp-opt.

physics.med-ph↗