Deep learning-based image reconstruction in optical coherence tomography using
undersampled spectral data Page 1 Deep learning-based image reconstruction in optical …

Diffuse Optical Tomography (DOT) has been growing significantly in the past two decades
as a promising tool for in-vivo and non-invasive imaging of tissues using near-infrared light …

We use diffuse optical tomography to quantitatively reconstruct images of complex phantoms
with millimeter sized features located centimeters deep within a highly-scattering medium. A …

The Role of Late Photons in Time-of-Flight Diffuse Optical Tomography Page 1 IF2E.5.pdf OSA
Imaging and Applied Optics Congress 2020 (3D, AOMS, COSI, DH, ISA) © OSA 2020 The Role …

Diffuse optical tomography (DOT) is a promising non-invasive optical imaging technique that
can provide functional information of biological tissues. Since diffuse light undergoes …

We present a deep background-mismodeling-learned reconstruction framework for high-
accuracy fluorescence diffuse optical tomography (FDOT). A learnable regularizer …

We proposed and implemented a deep learning scheme using convolution neural networks
(CNNs) with batch normalization (BNCNN) to construct a sensor-image DOI computation …

We present a new method allowing the reconstruction of 3D time-domain diffuse optical
tomography images, based on the time-dependent diffusion equation and an iterative …

Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical
parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model …

Imaging through highly diffusive media is challenging because of the extensive spreading of
light propagation in both time and space. The most advanced technique utilizes an …