arXiv subjects

Xie George Xu

Publications and source records attributed to Xie George Xu.

3 recordsLinked to original sources

arXiv2026

Using backscattered thermal neutrons to monitor boron concentration during BNCT: a Monte Carlo feasibility study

Boron neutron capture therapy (BNCT) requires knowledge of patient-specific $^{10}$B concentration for accurate dose estimation, yet no established method provides real-time boron-sensitive information during irradiation. Backscattered thermal neutrons carry a $^{10}$B-dependent intensity modulation through the $^{10}$B(n,$α$)$^{7}$Li reaction, documented in BNCT treatment rooms for three decades but not yet developed as a measurement signal. This paper uses Monte Carlo simulation to assess the feasibility of backscattered thermal neutrons as a measurement channel for $^{10}$B concentration. A thin $^{nat}$LiF-converter detector placed at the beam exit captures the composite forward-plus-backscatter field; differential imaging against a $^{10}$B-free baseline isolates the $^{10}$B-dependent component, quantified by the fractional reduction in the $^{6}$Li capture rate, termed Relative Detector Signal Reduction (RDSR). In homogeneous phantoms, RDSR shows linear concentration dependence ($R^2 = 0.997$) with a practical depth limit of approximately 6 cm. Edge-response analysis yields a diffusion-limited FWHM of 32-176 mm over 1-5 cm depth, with weak concentration dependence. In a voxelized patient phantom across 12 boron configurations, the $^{6}$Li capture cross-section provides intrinsic thermal neutron energy selectivity that preferentially weights the band where $^{10}$B absorption is concentrated. Region-of-interest integration achieves counting-statistics sensitivity below 10 ppm; the systematic detection floor (~22-28 ppm at $\pm$1% baseline uncertainty) identifies baseline-reference precision as the dominant constraint. The modeled detector produces limited dose perturbation (+11.6% treatment-time increase). These results establish the physical basis for a boron-sensitive backscattered neutron measurement concept in BNCT.

physics.med-ph↗

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↗