Cover tree compressed sensing for fast mr fingerprint recovery
Cover tree compressed sensing for fast mr fingerprint recovery
复制标题
用于快速 mr 指纹恢复的覆盖树压缩感知
DOI：
10.1109/mlsp.2017.8168167
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Davies
中科院分区：
文献类型：
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作者：
Mohammad Golbabaee;Zhouye Chen;Y. Wiaux;M. Davies
我们采用覆盖树形式的数据结构，并迭代地应用近似最近邻（ANN）搜索快速压缩感知重建离散光滑流形上的信号。利用不精确迭代投影梯度（IPG）算法最近的稳定性结果，并通过使用覆盖树的ANN搜索，我们降低了IPG算法的投影代价，使其随着低维光滑流形上的数据量的增长而几何地增长。我们将我们的研究结果应用于定量MRI压缩传感，特别是在磁共振指纹（MRF）框架内。对于类似的（有时更好）重建精度，我们报告2-3个数量级减少计算相比，标准的迭代方法，使用蛮力搜索。
We adopt a data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds. Leveraging on the recent stability results for the inexact Iterative Projected Gradient (IPG) algorithm and by using the cover tree's ANN searches, we decrease the projection cost of the IPG algorithm to be logarithmically growing with data population for low dimensional smooth manifolds. We apply our results to quantitative MRI compressed sensing and in particular within the Magnetic Resonance Fingerprinting (MRF) framework. For a similar (or sometimes better) reconstruction accuracy, we report 2–3 orders of magnitude reduction in computations compared to the standard iterative method, which uses brute-force searches.
影响因子：
2.1
作者：
Davies, Mike;Puy, Gilles;Wiaux, Yves
通讯作者：
Wiaux, Yves