Micro-CT guided deep neural network for 3D reconstructions in widefield diffuse optical tomography
Reconstructions in 3D widefield Diffuse Optical Tomography (DOT) suffer from poor spatial
resolution. Therefore, widefield DOT techniques benefit from incorporating structural priors …
resolution. Therefore, widefield DOT techniques benefit from incorporating structural priors …
Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions
Widefield illumination and detection strategies leveraging structured light have enabled fast
and robust probing of tissue properties over large surface areas and volumes. However …
and robust probing of tissue properties over large surface areas and volumes. However …
Multitask deep learning reconstruction and localization of lesions in limited angle diffuse optical tomography
Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to
assess its optical properties and identify abnormalities. DOT image reconstruction is an ill …
assess its optical properties and identify abnormalities. DOT image reconstruction is an ill …
A self-supervised learning approach for high-resolution diffuse optical tomography using neural fields
Diffuse optical tomography (DOT) has shown promise in biomedical research, such as
breast cancer diagnostics and brain imaging, by reconstructing hidden objects within …
breast cancer diagnostics and brain imaging, by reconstructing hidden objects within …
Machine learning model with physical constraints for diffuse optical tomography
A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse
optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under …
optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under …
FDU-net: deep learning-based three-dimensional diffuse optical image reconstruction
Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast
cancer imaging; however, the clinical translation of DOT is hampered by technical …
cancer imaging; however, the clinical translation of DOT is hampered by technical …
Unrolled-DOT: an interpretable deep network for diffuse optical tomography
Significance Imaging through scattering media is critical in many biomedical imaging
applications, such as breast tumor detection and functional neuroimaging. Time-of-flight …
applications, such as breast tumor detection and functional neuroimaging. Time-of-flight …
Deep learning based image reconstruction for sparse-view diffuse optical tomography
MH Jalalimanesh, MA Ansari - Waves in Random and Complex …, 2021 - Taylor & Francis
Three critical problems, including high-cost instrumentations, time-consuming image
recovery, and low image quality, limit clinical applications of diffuse optical tomography …
recovery, and low image quality, limit clinical applications of diffuse optical tomography …
A novelty convolutional neural network based direct reconstruction for MRI guided diffuse optical tomography
W Zhang, Z Li, Z Sun, K Jia… - … Biomedical Imaging XVII, 2022 - spiedigitallibrary.org
Diffuse Optical Tomography (DOT) is a promising non-invasive and relatively low-cost
biomedical image technology. The aim of DOT is to reconstruct optical properties of the …
biomedical image technology. The aim of DOT is to reconstruct optical properties of the …
Solving heterogenous region for diffuse optical tomography with a convolutional forward calculation model and the inverse neural network
X Fang, C Gao, Y Li, T Li - Advanced Optical Imaging …, 2020 - spiedigitallibrary.org
Diffuse optical tomography (DOT) is a noninvasive biomedical imaging method to
reconstruct optical property distribution. Since the underdetermined characteristic of …
reconstruct optical property distribution. Since the underdetermined characteristic of …