Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …

Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …

Although convolutional neural networks (CNNs) demonstrate the superior performance in
denoising positron emission tomography (PET) images, a supervised training of the CNN …

Plug-and-play priors are popular for solving ill-posed imaging inverse problems. Recent
efforts indicate that the convergence guarantee of the imaging algorithms using plug-and …

The medical practitioners primarily used medical images to reveal abnormalities in the
internal critical organs and structures of body covered by the bones and the skin. Main …

Objectives Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of
positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from …

Objective This study aims to assess the statistical significance of training parameters in 240
dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and …

This study aims to maximize the utilization of vector information from small-scale magnetic
targets and proposes a high-precision magnetic field reconstruction model based on the …

Image reconstruction based on sparse constraints is an important research topic in
compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit …

In this paper, the problem of constructing the measurement matrix in compressed sensing is
addressed. In compressed sensing, constructing a measurement matrix of good …