Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that
can accurately reconstruct images from undersampled k-space data with a much lower …

We propose a forward-backward splitting algorithm to integrate deep learning into maximum-
a-posteriori (MAP) positron emission tomography (PET) image reconstruction. The MAP …

Purpose To introduce a dual‐domain reconstruction network with V‐Net and K‐Net for
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …

Imaging is omnipresent in modern society with imaging devices based on a zoo of physical
principles, probing a specimen across different wavelengths, energies and time. Recent …

The combination of positron emission tomography (PET) with magnetic resonance (MR)
imaging opens the way to more accurate diagnosis and improved patient management. At …

The recently introduced kernelized expectation maximization (KEM) method has shown
promise across varied applications. These studies have demonstrated the benefits and …

Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is one of the
powerful ways to reduce the scan time of MR imaging with performance guarantee …

In positron emission tomography (PET) and X-ray computed tomography (CT), reducing
radiation dose can cause significant degradation in image quality. For image quality …

Purpose Numerous image reconstruction methodologies for positron emission tomography
(PET) have been developed that incorporate magnetic resonance (MR) imaging structural …

Anatomically driven image reconstruction algorithms have become very popular in positron
emission tomography (PET) where they have demonstrated improved image resolution and …