Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …

This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …

In this study, a fast data-driven optimization approach, named bias-accelerated subset
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …

Diffusion-based generative models have been used as powerful priors for magnetic
resonance imaging (MRI) reconstruction. We present a learning method to optimize sub …

Compressed sensing in magnetic resonance imaging essentially involves the optimization
of (1) the sampling pattern in k-space under MR hardware constraints and (2) image …

The Spreading Projection Algorithm for Rapid K-space sampLING, or SPARKLING, is an
optimization-driven method that has been recently introduced for accelerated 2D MRI using …

Neural networks allow solving many ill-posed inverse problems with unprecedented
performance. Physics informed approaches already progressively replace carefully hand …

Accelerated MRI protocols routinely involve a predefined sampling pattern that
undersamples the k-space. Finding an optimal pattern can enhance the reconstruction …

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …

Achieving high imaging resolution in conventional monostatic radar imaging with
mechanical scanning requires excessive acquisition time. Although real aperture radar …