The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

Magnetic resonance imaging (MRI) is a medical imaging technique used to show
anatomical structures and physiological processes of the human body. Due to limitations like …

Given the constraints posed by hardware capacity, scan duration, and patient cooperation,
the reconstruction of magnetic resonance imaging (MRI) images emerges as a pivotal …

Accurate segmentation of cerebrovascular structures from Computed Tomography
Angiography (CTA), Magnetic Resonance Angiography (MRA), and Digital Subtraction …

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns
associated with real medical data. An approach that stands out to circumvent this hurdle is …

Simple Summary Interest in applying artificial intelligence to medical imaging to enhance
image quality has grown in both clinical practice and research. Nonetheless, these artificial …

Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative
analysis and treatment. The use of convolutional neural networks (CNNs) in image …

Background Deep-learning-based reconstruction (DLR) improves the quality of magnetic
resonance (MR) images which allows faster acquisitions. The aim of this study was to …

Generative Adversarial Networks (GANs) have shown great performance on super-
resolution problems since they can generate more visually realistic images and video …

Generative artificial intelligence (AI) has enabled tasks in radiology, including tools for
improving image quality. Recently, new hotspots have emerged, such as intra-or inter-modal …