Using backscattered thermal neutrons to monitor boron concentration during BNCT: a Monte Carlo feasibility study
Abstract
Boron neutron capture therapy (BNCT) requires knowledge of patient-specific B concentration for accurate dose estimation, yet no established method provides real-time boron-sensitive information during irradiation. Backscattered thermal neutrons carry a B-dependent intensity modulation through the B(n,)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 B concentration. A thin LiF-converter detector placed at the beam exit captures the composite forward-plus-backscatter field; differential imaging against a B-free baseline isolates the B-dependent component, quantified by the fractional reduction in the Li capture rate, termed Relative Detector Signal Reduction (RDSR). In homogeneous phantoms, RDSR shows linear concentration dependence () 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 Li capture cross-section provides intrinsic thermal neutron energy selectivity that preferentially weights the band where B absorption is concentrated. Region-of-interest integration achieves counting-statistics sensitivity below 10 ppm; the systematic detection floor (~22-28 ppm at 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.
Keywords
Cite
@article{arxiv.2607.13589,
title = {Using backscattered thermal neutrons to monitor boron concentration during BNCT: a Monte Carlo feasibility study},
author = {Zirui Ye and Yuxin Wang and Meitong Wei and Xie George Xu},
journal= {arXiv preprint arXiv:2607.13589},
year = {2026}
}
Comments
41 pages, 13 figures