A robust multi-scale approach to quantitative susceptibility mapping

• DOI: 10.1016/j.neuroimage.2018.07.065
• Publication Date: 2018-07-31T16:47:01Z
• Authors: Julio Acosta-Cabr...; Carlos Milovic; Hendrik Mattern; Cristian Tejos; Oliver Speck; Martina F. Callaghan
• Keywords: Variational regularisation; Iron; 610; Neuroimaging; Sensitivity and Specificity; Article; Magnetic susceptibility; 03 medical and health sciences; methods [Magnetic Resonance Imaging]; 0302 clinical medicine; methods [Image Processing, Computer-Assisted]; Image Processing, Computer-Assisted; Humans; standards [Phlebography]; ddc:610; diagnostic imaging [Brain]; methods [Phlebography]; standards [Magnetic Resonance Imaging]; Brain; Reproducibility of Results; Quantitative MRI; Phlebography; Laplacian pyramid; Models, Theoretical; Magnetic Resonance Imaging; standards [Image Processing, Computer-Assisted]; Venography; Medicina y salud; Iron mapping; methods [Neuroimaging]; standards [Neuroimaging]; Algorithms

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.