The development of compressed-sensing (CS) methods for magnetic resonance (MR)
image reconstruction led to an explosion of research on models and optimization algorithms …

Estimating the frequencies of elements in a data stream is a fundamental task in data
analysis and machine learning. The problem is typically addressed using streaming …

In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …

In the area of magnetic resonance imaging (MRI), an extensive range of non-linear
reconstruction algorithms has been proposed which can be used with general Fourier …

Space partitions of $\mathbb {R}^ d $ underlie a vast and important class of fast nearest
neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general …

Abstract Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by
under-sampling in k-space (ie, the Fourier domain). In this paper, we consider the problem …

In recent years, there have been significant advances in the use of deep learning methods in
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …

We introduce a “learning-based” algorithm for the low-rank decomposition problem: given
an $ n\times d $ matrix $ A $, and a parameter $ k $, compute a rank-$ k $ matrix $ A'$ that …

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 …

Linear encoding of sparse vectors is widely popular, but is commonly data-independent–
missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we …