.png){kind=icon}
返回
Fast Quantitative Susceptibility Mapping with L1-Regularization and Automatic Parameter Selection
.png){kind=icon}
OA
AI
DOI：10.1002/mrm.25029.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
PurposeTo enable fast reconstruction of quantitative susceptibility maps with total variation penalty and automatic regularization parameter selection. Methods(1)-Regularized susceptibility mapping is accelerated by variable splitting, which allows closed-form evaluation of each iteration of the algorithm by soft thresholding and fast Fourier transforms. This fast algorithm also renders automatic regularization parameter estimation practical. A weighting mask derived from the magnitude signal can be incorporated to allow edge-aware regularization. ResultsCompared with the nonlinear conjugate gradient (CG) solver, the proposed method is 20 times faster. A complete pipeline including Laplacian phase unwrapping, background phase removal with SHARP filtering, and (1)-regularized dipole inversion at 0.6 mm isotropic resolution is completed in 1.2 min using MATLAB on a standard workstation compared with 22 min using the CG solver. This fast reconstruction allows estimation of regularization parameters with the L-curve method in 13 min, which would have taken 4 h with the CG algorithm. The proposed method also permits magnitude-weighted regularization, which prevents smoothing across edges identified on the magnitude signal. This more complicated optimization problem is solved 5 times faster than the nonlinear CG approach. Utility of the proposed method is also demonstrated in functional blood oxygen level-dependent susceptibility mapping, where processing of the massive time series dataset would otherwise be prohibitive with the CG solver. ConclusionOnline reconstruction of regularized susceptibility maps may become feasible with the proposed dipole inversion. Magn Reson Med 72:1444-1459, 2014. (c) 2013 Wiley Periodicals, Inc.
Keyword:
Quantitative susceptibility mapping
Regularization
Total variation
L-curve
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
1.2W
被引数:
3.1W
机构
引用论文
.png){kind=icon}
Accuracy of the Morphology Enabled Dipole Inversion (MEDI) Algorithm for Quantitative Susceptibility Mapping in MRIMRI中定量磁化率映射的形态学启用偶极子反演 (MEDI) 算法的准确性
OAAI
OAAI分享收藏分享收藏
查看更多内容