提出新型路径长度因子模型，显著提升近红外成像定量精度。
A Novel Differential Pathlength Factor Model for Near-Infrared Diffuse Optical Imaging
- 基于蒙特卡洛模拟构建距离与组织特性相关的新模型
- 误差低于10%，传统方法误差超100%
- 适合大范围探测和深度变化场景，如脑部成像
近红外漫射光学成像可在反射和透射模式下进行，依赖物理模型与测量数据提取染色体浓度变化信息。连续波近红外成像需精确的差分路径长度因子（DPF）以实现染色体浓度的定量估计。现有DPF定义存在依赖公式的问题，在反射模式下源-探测器间距较小时误差显著增大，限制了在大面积检测及信号深度变化情况下的应用。本文通过蒙特卡洛模拟推导出两个依赖距离和组织特性的新DPF模型（理想与实验实用型），并对比标准方法进行基准测试。所提模型在广泛光学条件下误差均低于10%，而传统方法误差可超过100%。理论预测经受控仿体实验验证，证明其在连续波近红外成像中提升了定量准确性。
原文摘要 · Abstract (English)
Near infrared diffuse optical imaging can be performed in reflectance and transmission mode and relies on physical models along with measurements to extract information on changes in chromophore concentration. Continuous-wave near-infrared diffuse optical imaging relies on accurate differential pathlength factors (DPFs) for quantitative chromophore estimation. Existing DPF definitions inherit formulation-dependent limitations that can introduce large errors in modified Beer--Lambert law analyses. These errors are significantly higher at smaller source-detector separations in a reflectance mode of measurement. This minimizes their applicability in situations where large area detection is used and also when signal depth is varying. Using Monte Carlo simulations, we derive two distance- and property-dependent DPF models one ideal and one experimentally practical and benchmark them against standard formulations. The proposed models achieve errors below 10 percent across broad optical conditions, whereas conventional DPFs can exceed 100 percent error. The theoretical predictions are further validated using controlled phantom experiments, demonstrating improved quantitative accuracy in CW-NIR imaging.
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