A Compressed Sensing Framework for Magnetic Resonance Fingerprinting

被引:88
作者
Davies, Mike [1 ]
Puy, Gilles [2 ]
Vandergheynst, Pierre [2 ]
Wiaux, Yves [3 ]
机构
[1] Univ Edinburgh, Sch Engn, IDCOM, Edinburgh EH9 3JL, Midlothian, Scotland
[2] Ecole Polytech Fed Lausanne, ST1 1EL LTS2 ELE 237, CH-1015 Lausanne, Switzerland
[3] Heriot Watt Univ, Sch Engn & Phys Sci, Inst Sensors Signals & Syst, Edinburgh EH14 4AS, Midlothian, Scotland
基金
英国工程与自然科学研究理事会;
关键词
compressed sensing; MRI; Bloch equations; manifolds; Johnson-Linderstrauss embedding; TRANSIENT-RESPONSE; RECONSTRUCTION; SIGNALS; UNION;
D O I
10.1137/130947246
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Inspired by the recently proposed magnetic resonance fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are a random pulse excitation sequence following the MRF technique, a random EPI subsampling strategy, and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that, theoretically, as long as the excitation sequence possesses an appropriate form of persistent excitation, we are able to accurately recover the proton density, T1, T2, and off-resonance maps simultaneously from a limited number of samples. These results are further supported through extensive simulations using a brain phantom.
引用
收藏
页码:2623 / 2656
页数:34
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