@inproceedings{96474215ebb94e6badaf657737249cf9,
title = "Low-Rank + Sparse (L+S) reconstruction for accelerated dynamic MRI with separation of background and dynamic components",
abstract = "L+S matrix decomposition finds the low-rank (L) and sparse (S) components of a matrix M by solving the following convex optimization problem: min||L||* + γ||S||1 subject to M = L + S, where ||L||* is the nuclear-norm or sum of singular values of L and ||S||1 is the l1-norm or sum of absolute values of S. This work presents the application of the L+S decomposition to reconstruct incoherently undersampled dynamic MRI data as a superposition of a slowly or coherently changing background and sparse innovations. Feasibility of the method was tested in several accelerated dynamic MRI experiments including cardiac perfusion, time-resolved peripheral angiography and liver perfusion using Cartesian and radial sampling. The high acceleration and background separation enabled by L+S reconstruction promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling.",
keywords = "Compressed sensing, Dynamic MRI, Low-rank matrix completion, Sparsity",
author = "Ricardo Otazo and Sodickson, {Daniel K.} and Cand{\`e}s, {Emmanuel J.}",
year = "2013",
doi = "10.1117/12.2023359",
language = "English (US)",
isbn = "9780819497086",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Wavelets and Sparsity XV",
note = "Wavelets and Sparsity XV ; Conference date: 26-08-2013 Through 29-08-2013",
}