College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Foshan Graduate School of Innovation, Northeastern University, Foshan, China.
J Biophotonics. 2024 Aug;17(8):e202300564. doi: 10.1002/jbio.202300564. Epub 2024 Jun 17.
Spatially offset Raman spectroscopy (SORS) is valuable for noninvasive bone assessment but requires a clearer understanding of how offset distances influence detection depth. To address this, our study devised a forward-adjoint Monte Carlo multi-layer (MCML) model to simulate photon paths in SORS, aiming to determine optimal offsets for various tissue types. We examined photon migration at offsets between 0 and 15 mm against layered phantoms of differing thicknesses and compositions to optimize the signal-to-noise ratio for bone layers. The findings highlight that optimal offsets are contingent on tissue characteristics: a metacarpal beneath 2.5 mm of tissue had an ideal offset of 6.7 mm, while a tibia with 5 mm of soft tissue required 10-11 mm. This precise calibration of SORS via MCML modeling promises substantial improvements in bone health diagnostics and potential for expansive medical applications.
空间位移拉曼光谱(SORS)在非侵入性骨评估方面具有重要价值,但需要更清楚地了解位移距离如何影响检测深度。为此,我们设计了一种正向伴随蒙特卡罗多层(MCML)模型来模拟 SORS 中的光子路径,旨在确定各种组织类型的最佳偏移量。我们在 0 到 15mm 的偏移量下检查了不同厚度和组成的分层幻像中的光子迁移,以优化骨层的信噪比。研究结果表明,最佳偏移量取决于组织特征:在 2.5mm 厚的组织下的掌骨的理想偏移量为 6.7mm,而在 5mm 厚的软组织下的胫骨需要 10-11mm。通过 MCML 建模对 SORS 进行精确校准有望显著提高骨健康诊断的准确性,并为广泛的医学应用提供潜力。