A robust multi-scale approach to quantitative susceptibility mapping

Abstract

Quantitative Susceptibility Mapping (QSM), best known as a surrogate for tissue iron content, is becoming a highly relevant MRI contrast for monitoring cellular and vascular status in aging, addiction, traumatic brain injury and, in general, a wide range of neurological disorders. In this study we present a new Bayesian QSM algorithm, named Multi-Scale Dipole Inversion (MSDI), which builds on the nonlinear Morphology-Enabled Dipole Inversion (nMEDI) framework, incorporating three additional features: (i) improved implementation of Laplace's equation to reduce the influence of background fields through variable harmonic filtering and subsequent deconvolution, (ii) improved error control through dynamic phase-reliability compensation across spatial scales, and (iii) scalewise use of the morphological prior. More generally, this new pre-conditioned QSM formalism aims to reduce the impact of dipole-incompatible fields and measurement errors such as flow effects, poor signal-to-noise ratio or other data inconsistencies that can lead to streaking and shadowing artefacts. In terms of performance, MSDI is the first algorithm to rank in the top-10 for all metrics evaluated in the 2016 QSM Reconstruction Challenge. It also demonstrated lower variance than nMEDI and more stable behaviour in scan-rescan reproducibility experiments for different MRI acquisitions at 3 and 7 Tesla. In the present work, we also explored new forms of susceptibility MRI contrast making explicit use of the differential information across spatial scales. Specifically, we show MSDI-derived examples of: (i) enhanced anatomical detail with susceptibility inversions from short-range dipole fields (hereby referred to as High-Pass Susceptibility Mapping or HPSM), (ii) high specificity to venous-blood susceptibilities for highly regularised HPSM (making a case for MSDI-based Venography or VenoMSDI), (iii) improved tissue specificity (and possibly statistical conditioning) for Macroscopic-Vessel Suppressed Susceptibility Mapping (MVSSM), and (iv) high spatial specificity and definition for HPSM-based Susceptibility-Weighted Imaging (HPSM-SWI) and related intensity projections.

Brain iron deposition is linked with cognitive severity in Parkinson’s disease

“…QSM image reconstruction, including phase pre-processing and estimation of susceptibility maps, followed the default QSMbox (https://gitlab.com/acostaj/QSMbox) pipeline for single-echo, coil-combined data 28. Three-dimensional complex phase data (adaptive combined using scanner software) were unwrapped with a discrete Laplacian method.…”

“…Phase pre-processing was completed in two background field suppression steps: Laplacian boundary value extraction, followed by variable spherical mean-value filtering. Susceptibility maps were estimated using a recently validated QSM algorithm, Multi-Scale Dipole Inversion,28 which is more robust than the previous non-linear Morphology-Enabled Dipole Inversion approach28 (figure 1). To increase cortical sensitivity, filtering during reconstruction was performed using a kernel with 8 mm radius.…”

Investigating the accuracy and precision of TE‐dependent versus multi‐echo QSM using Laplacian‐based methods at 3 T

“…For TE-dependent QSM only, residual phase offsets were removed by applying V-SHARP with an initial kernel radius of approximately 40 mm and a 1-voxel step-size/final kernel radius. 27 In all these pipelines, ΔB Loc -to-χ inversion was performed using Tikhonov regularization 2,28 with correction for susceptibility underestimation 23 and using the L-curve method 29 to determine the optimal value for the regularization parameter. This inversion method was chosen because it is computationally efficient and has been shown to substantially reduce streaking artifacts relative to the truncated k-space division method.…”

Reducing bias in dual flip angle T1‐mapping in human brain at 7T

“…) as well as the use of ESTATICS for calculations. The multi‐scale dipole inversion (MSDI) algorithm was used for QSM on the T 1 ‐w multi‐echo dataset.…”