Medical image reconstruction aims to acquire high-quality medical images for clinical usage
at minimal cost and risk to the patients. Deep learning and its applications in medical …

The ubiquity of systems using artificial intelligence or" AI" has brought increasing attention to
how those systems should be regulated. The choice of how to regulate AI systems will …

Alternating direction methods are a common tool for general mathematical programming
and optimization. These methods have become particularly important in the field of …

In the ever-evolving landscape of tomographic imaging algorithms, this literature review
explores a diverse array of themes shaping the field's progress. It encompasses …

Fourier transformation is an extensively studied problem in many research fields. It has
many applications in machine learning, signal processing, compressed sensing, and so on …

In this work, a computational model of magnetic resonance (MR) flow imaging is proposed.
The first model component provides fluid dynamics maps by applying the lattice Boltzmann …

In this paper, we consider the extensively studied problem of computing a k-sparse
approximation to the d-dimensional Fourier transform of a length n signal. Our algorithm …

OBJECTIVE. This article outlines artifactual findings commonly encountered in
neuroradiologic MRI studies and offers clues to differentiate them from true pathology on the …

In this paper we present MESA: a platform for design and evaluation of medical image
segmentation methods. The platform offers a complete approach for the method creation …

Presented work is a feature-extraction and classification study for Alzheimer's disease (AD),
Mild Cognitive Impaired (MCI) and Normal subjects. The proposed technique consists of …