Deep learning based approaches have been used to improve image quality in cone-beam
computed tomography (CBCT), a medical imaging technique often used in applications such …

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT
(LDCT) imaging and are expected to be a new generation of CT reconstruction technology …

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating
chromophore concentrations, whereas the involved optical inverse problem, aiming to …

The availability of high-quality 3D microstructures is an essential prerequisite for simulating
and studying transport processes and physical properties of porous media. Such numerical …

Computed tomography (CT) stands as a pivotal medical imaging technique, delivering
timely and reliable clinical evaluations. Yet, its dependence on ionizing radiation raises …

Cone-beam computed tomography (CBCT) is generally reconstructed with hundreds of two-
dimensional X-Ray projections through the FDK algorithm, and its excessive ionizing …

Background Long‐lasting efforts have been made to reduce radiation dose and thus the
potential radiation risk to the patient for computed tomography (CT) acquisitions without …

Objective. Various deep learning methods have recently been used for low dose CT (LDCT)
denoising. Aggressive denoising may destroy the edge and fine anatomical structures of CT …

This study aims to determine an acquisitional and computational workflow that yields the
highest quality spatio-spectral reconstructions in four-dimensional neutron tomography …

This study proposes a deep learning framework, DLPVI, which integrates projection, view-by-
view backprojection (VVBP), and image domains to improve the quality of high-sparse-view …