Artificial intelligence (AI) models for medical imaging tasks, such as classification or
segmentation, require large and diverse datasets of images. However, due to privacy and …

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases
of patient management, starting from diagnosis, through therapy planning, to treatment …

Background Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue
contrast during MRI scans and play a crucial role in the management of patients with cancer …

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance
(CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but …

Background Iodinated contrast agents (ICAs), which are widely used in CT angiography
(CTA), may cause adverse effects in humans, and their use is time-consuming and costly …

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each
contrast provides complementary information. However, the availability of each imaging …

Background Missing MRI sequences represent an obstacle in the development and use of
deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing …

Background There is increasing interest in noncontrast breast MRI alternatives for tumor
visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility …

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …

Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …