A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
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DOI：
10.1137/130947246
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发表时间：
2014-01-01
影响因子：
2.1
通讯作者：
Wiaux, Yves
中科院分区：
文献类型：
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作者：
Davies, Mike;Puy, Gilles;Wiaux, Yves
受最近提出的磁共振指纹（MRF）技术的启发，我们开发了一个原则性的压缩传感定量MRI框架。三个关键组成部分是一个随机脉冲激励序列的MRF技术，随机EPI子采样策略，和迭代投影算法，施加与布洛赫方程的一致性。我们表明，从理论上讲，只要激发序列具有适当形式的持续激发，我们能够准确地恢复质子密度，T1，T2，和非共振地图同时从有限数量的样品。这些结果进一步支持通过广泛的模拟使用的大脑幻影。
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.