Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning.

ABSTRACT: Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single-snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy. We introduce OxyGAN, a data-driven, content-aware method to estimate tissue oxygenation directly from single structured-light images. OxyGAN is an end-to-end approach that uses supervised generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659- and 851-nm sinusoidal illumination. We benchmark OxyGAN by compari

SUBMITTER: Chen MT 

PROVIDER: S-EPMC7701163 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature