覆盖树压缩传感快速mr指纹恢复

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-06-01 DOI:10.1109/MLSP.2017.8168167

Mohammad Golbabaee, Zhouye Chen, Y. Wiaux, M. Davies

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引用次数: 10

摘要

我们采用覆盖树形式的数据结构，并迭代地应用近似近邻(ANN)搜索对离散光滑流形上的信号进行快速压缩感知重构。利用最近的不精确迭代投影梯度(IPG)算法的稳定性结果，并通过使用覆盖树的人工神经网络搜索，我们降低了IPG算法的投影成本，使其随着低维光滑流形的数据填充呈对数增长。我们将我们的结果应用于定量MRI压缩传感，特别是在磁共振指纹(MRF)框架内。对于类似的(或有时更好的)重建精度，我们报告与使用暴力搜索的标准迭代方法相比，计算减少了2-3个数量级。

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Cover tree compressed sensing for fast mr fingerprint recovery

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.

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来源期刊

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

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