Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping.

Priya S Balasubramanian,Weiyuan Huang,Ilhami Kovanlikaya,Pascal Spincemaille,Lingfei Guo,Yi Wang

doi:10.1016/j.isci.2020.101553

Priya S Balasubramanian, Weiyuan Huang + Show 4 more

Open Access

https://doi.org/10.1016/j.isci.2020.101553

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Journal: iScience	Publication Date: Sep 12, 2020
Citations: 3	License type: cc-by

Affiliation: Cornell University

Abstract

SummaryAdaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously proposed regularized total field inversion methods, improvements were demonstrated in phantoms and subjects without and with hemorrhages. This algorithm, named TFIR, demonstrates the lowest error in numerical and gadolinium phantom datasets. In COSMOS data, TFIR performs well in matching ground truth in high-susceptibility regions. For patient data, TFIR comes close to meeting the quality of the reference local field method and outperforms other total field techniques in both clinical scores and shadow reduction.

Highlights

Quantitative susceptibility mapping (QSM) aims to solve the inverse problem of mapping the magnetic susceptibility from the measured magnetic field
This paper introduces a regularized total field inversion (TFIR) method that operates on the principles of spatially adaptive regularization, building off of the recent development by Sun et al (2018)
Given the COSMOS evaluation performed for the optimal kernel size selection, all subjects are analyzed with a 1-mm kernel

Summary

Introduction

Quantitative susceptibility mapping (QSM) aims to solve the inverse problem of mapping the magnetic susceptibility from the measured magnetic field. Current QSM methods typically perform brain extraction that may be followed by additional erosion, either as part of the background field removal process (Schweser et al, 2011) or due to the inclusion of a spherical mean value (SMV) operator in the dipole inversion process (Wang and Liu, 2015), referred to in the following as Morphology Enabled Dipole Inversion with SMV or MEDI-PDF-SMV, shortened to MEDI-SMV for the remainder of this paper This type of local field method has been utilized prior to the introduction of total field methods and, with the introduction of the SMV operator, is successful at suppressing shadow artifacts, as described in the literature (Kee et al, 2017). A straightforward dipole field inversion of the resulting tissue field often leads to large residual streaking and shadow artifacts (Li et al, 2015; Shmueli et al, 2009)

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