@inproceedings{9ce2682e5b0242f8bdbb09fe11a145ab,
title = "Fast magnetic resonance parametric imaging via structured low-rank matrix reconstruction",
abstract = "Magnetic Resonance Parametric Imaging is a recently-proposed method that permits quantitative determination of MR parameters such as the T1 and T2 relaxation times. In contrast to conventional MRI, one or more encoding parameters in the RF excitation are randomly varied over the scan and tissue parameters are inferred from the temporal response to the excitation. This work presents a novel low-rank model-based parametric matrix estimation method for joint reconstruction and parameter estimation suitable for highly accelerated (i.e. highly undersampled) scans. The method is demonstrated on T2 cardiac breath-hold imaging with varying spin echo times.",
keywords = "Compressed sensing, Image reconstruction, Low-rank decomposition, MRI, T mapping, T mapping",
author = "Eliasi, {Parisa Amiri} and Li Feng and Ricardo Otazo and Sundeep Rangan",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 02-11-2014 Through 05-11-2014",
year = "2015",
month = apr,
day = "24",
doi = "10.1109/ACSSC.2014.7094477",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "423--428",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers",
}