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Library‐driven approach for fast implementation of the voxel spread function to correct magnetic field inhomogeneity artifacts for gradient‐echo sequences
Author(s) -
Liu Ying,
Ye Qiong,
Zeng Feiyan,
Jiang Xiaohua,
Cai Bin,
Lv Weifu,
Wen Jie
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14904
Subject(s) - voxel , computer science , field (mathematics) , function (biology) , algorithm , magnetic field , echo (communications protocol) , phase (matter) , signal (programming language) , physics , artificial intelligence , mathematics , quantum mechanics , evolutionary biology , pure mathematics , biology , computer network , programming language
Purpose Previously developed Voxel Spread Function (VSF) method (Yablonskiy, et al, MRM, 2013;70:1283) provides solution to correct artifacts induced by macroscopic magnetic field inhomogeneity in the images obtained by multi‐Gradient‐Recalled‐Echo (mGRE) techniques. The goal of this study was to develop a library‐driven approach for fast VSF implementation. Methods The VSF approach describes the contribution of the magnetic field inhomogeneity effects on the mGRE signal decay in terms of the F‐function calculated from mGRE phase and magnitude images. A pre‐calculated library accounting for a variety of background field gradients caused by magnetic field inhomogeneity was used herein to speed up the calculation of F‐function. Quantitative R2* maps from the mGRE data collected from two healthy volunteers were generated using the library as validation. Results As compared with direct calculation of the F‐function based on a voxel‐wise approach, the new library‐driven method substantially reduces computational time from several hours to few minutes, while, at the same time, providing similar accuracy of R2* mapping. Conclusion The new procedure proposed in this study provides a fast post‐processing algorithm that can be incorporated in the quantitative analysis of mGRE data to account for background field inhomogeneity artifacts, thus can facilitate the applications of mGRE‐based quantitative techniques in clinical practices.