Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …

Many clinical applications based on deep learning and pertaining to radiology have been
proposed and studied in radiology for classification, risk assessment, segmentation tasks …

Radiomics and deep learning have recently gained attention in the imaging assessment of
various liver diseases. Recent research has demonstrated the potential utility of radiomics …

Abstract Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate
neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion …

Purpose: Deep learning has achieved major breakthroughs during the past decade in
almost every field. There are plenty of publicly available algorithms, each designed to …

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since
the MR signal is acquired in frequency space, any motion of the imaged object leads to …

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, with
increasing global incidence. Morbidity and mortality associated with HCC remains high, and …

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac
diseases thanks to its ability to present cardiovascular features in excellent contrast. As …

Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study
proposes a new method to detect phase-encoding (PE) lines corrupted by motion and …