A modulated closed form solution for quantitative susceptibility mapping - a thorough evaluation and comparison to iterative methods based on edge prior knowledge

Diana Khabipova, Yves Wiaux, Rolf Gruetter, Jose P. Marques

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33 Citations (Scopus)
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Abstract

The aim of this study is to perform a thorough comparison of quantitative susceptibility mapping (QSM) techniques and their dependence on the assumptions made. The compared methodologies were: two iterative single orientation methodologies minimizing the l2, l1TV norm of the prior knowledge of the edges of the object, one over-determined multiple orientation method (COSMOS) and anewly proposed modulated closed-form solution (MCF). The performance of these methods was compared using a numerical phantom and in-vivo high resolution (0.65mm isotropic) brain data acquired at 7T using a new coil combination method. For all QSM methods, the relevant regularization and prior-knowledge parameters were systematically changed in order to evaluate the optimal reconstruction in the presence and absence of a ground truth. Additionally, the QSM contrast was compared to conventional gradient recalled echo (GRE) magnitude and R2* maps obtained from the same dataset. The QSM reconstruction results of the single orientation methods show comparable performance. The MCF method has the highest correlation (corrMCF=0.95, r2MCF =0.97) with the state of the art method (COSMOS) with additional advantage of extreme fast computation time. The l- curve method gave the visually most satisfactory balance between reduction of streaking artifacts and over-regularization with the latter being overemphasized when the using the COSMOS susceptibility maps as ground-truth. R2* and susceptibility maps, when calculated from the same datasets, although based on distinct features of the data, have a comparable ability to distinguish deep gray matter structures.
Original languageEnglish
Pages (from-to)163-174
JournalNeuroImage
Volume107
Early online date22 Nov 2014
DOIs
Publication statusPublished - 2015

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