Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.

Abstract

The conventional optical properties (OPs) reconstruction in spatial frequency domain (SFD) imaging, like the lookup table (LUT) method, causes OPs aliasing and yields only average OPs without depth resolution. Integrating SFD imaging with time-resolved (TR) measurements enhances space-TR information, enabling improved reconstruction of absorption () and reduced scattering () coefficients at various depths. To achieve the stratified reconstruction of OPs and the separation between and , using deep learning workflow based on the temporal and spatial information provided by time-domain SFD imaging technique, while enhancing the reconstruction accuracy. Two data processing methods are employed for the OPs reconstruction with TR-SFD imaging, one is full TR data, and the other is the featured data extracted from the full TR data (, continuous-wave component, , mean time of flight). We compared their performance using a series of simulation and phantom validations. Compared to the LUT approach, utilizing full TR, and datasets yield high-resolution OPs reconstruction results. Among the three datasets employed, full TR demonstrates the optimal accuracy. Utilizing the data obtained from SFD and TR measurement techniques allows for achieving high-resolution separation reconstruction of and at different depths within 5mm.