Diffuse Optical Tomography (DOT) is an non-invasive optical imaging technique that measures the optical properties of physiological tissue using near infrared spectrum light. Optical properties are extracted from the measurement using reconstruction algorithm. This project uses the steepest descent method for reconstruction of optical data.

Addresses the problem of reconstructing images acquired by diffuse optical tomography using deep learning.

Simulated frequency-domain diffuse optical tomography dataset

Repository for our deep multitask paper in DOT imaging.

Repository for our limited angle deep learning based DOT image reconstruction.

Repository for our limited angle deep learning based DOT image reconstruction

Repository for our deep multi-frequency fusion paper in DOT imaging.

Simulation of the detection of a perturbation in homogeneous media via diffuse optical tomography.

Repository for our deep multitask paper in DOT imaging.

Official Implementation for Unrolled-DOT: an unrolled network for solving (time-of-flight) diffuse optical tomography inverse problems.

Addresses the problem of reconstructing images acquired by diffuse optical tomography using deep learning.