Diffuse optical tomography (DOT) has shown promise in biomedical research, such as
breast cancer diagnostics and brain imaging, by reconstructing hidden objects within …

A machine learning (ML) model with physical constraints is introduced to perform diffuse
optical tomography (DOT) reconstruction. Here, for the first time, we combine ultrasound …

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 …

Reconstructions in 3D widefield Diffuse Optical Tomography (DOT) suffer from poor spatial
resolution. Therefore, widefield DOT techniques benefit from incorporating structural priors …

Diffuse optical tomography (DOT) is a relatively new imaging modality that has
demonstrated its clinical potential of probing tumors in a non-invasive and affordable way …

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 …

Diffuse optical tomography (DOT) leverages near-infrared light propagation through in vivo
tissue to assess its optical properties and identify abnormalities such as cancerous lesions …

Three critical problems, including high-cost instrumentations, time-consuming image
recovery, and low image quality, limit clinical applications of diffuse optical tomography …

Light scattering imposes a major obstacle for imaging objects seated deeply in turbid media,
such as biological tissues and foggy air. Diffuse optical tomography (DOT) tackles scattering …

Widefield illumination and detection strategies leveraging structured light have enabled fast
and robust probing of tissue properties over large surface areas and volumes. However …