LAUSR

Abstract. Significance Current optical health-sensing devices rely on simplified homogeneous tissue models or semi-empirical ratiometric methods, which inadequately address anatomical complexity and inter-individual optical variability. This introduces systematic errors in light propagation modeling, compromising measurement accuracy and clinical robustness, necessitating organ-specific optical models for reliable physiological sensing. Aim To develop a standard optical digital wrist (DW) model by integrating magnetic resonance imaging (MRI) and diffuse optical imaging (DOI), enabling anatomically accurate and optically realistic modeling of wrist tissues for improved precision in wearable optical health monitoring applications. Approach The multimodal MRI-DOI framework was implemented, comprising three key components: (1) statistical integration of high-resolution MRI datasets generated a population-averaged anatomical DW template; (2) region-based time-domain diffuse optical tomography (TD-DOT) with MRI-derived anatomical priors, extracted depth-resolved optical properties of subsurface tissues; (3) spatial frequency domain imaging (SFDI) supplemented high-resolution optical properties of superficial skin layers. Results Simulation experiments demonstrated the high accuracy of region-based TD-DOT reconstruction, with mean errors below 8.57% (μa) and 9.63% (μs′), quantitatively supporting the precision of the proposed approach. Phantom experiments with wrist-mimicking phantoms yielded mean reconstruction errors of 10.52% (μa) and 13.23% (μs′) for TD-DOT, and the SFDI top-layer quantification yielded lower errors of 4.48% (μa) and 8.69% (μs′), validating the performance of the TD-DOT system and the SFDI system. Furthermore, in vivo optical property measurements showed strong agreement with literature values, further validating the reliability and practicality of the methodology. Conclusions We establish a standard DW template and develop an in vivo optical structure acquisition methodology, transitioning biosensing models from homogeneous approximations to anatomically layered models. The approach can enhance the customization, dynamic adaptability, and clinical validity of biosensing technologies.