Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality,
causing over 17. 9 million deaths worldwide per year with associated costs of over $800 …

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 …

In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …

Hospitals, especially their emergency services, receive a high number of wrist fracture
cases. For correct diagnosis and proper treatment of these, images obtained from various …

Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …

Introduction Medical image analysis is of tremendous importance in serving clinical
diagnosis, treatment planning, as well as prognosis assessment. However, the image …

Imaging plays a fundamental role in the effective diagnosis, staging, management, and
monitoring of various cardiac pathologies. Successful radiological analysis relies on …

Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not
only does it require a large amount of work, it is also a complicated task to outline the lesion …

Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …

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 …