Journal Article

DZNE-2020-06603

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A robust multi-scale approach to quantitative susceptibility mapping.

Acosta-Cabronero, J. (First author)DZNE* ; Milovic, C. ; Mattern, H. ; Tejos, C. ; Speck, O.DZNE* ; Callaghan, M. F.

2018
Academic Press Orlando, Fla.

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Please use a persistent id in citations: doi:10.1016/j.neuroimage.2018.07.065

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.

Keyword(s): Algorithms (MeSH) ; Brain: diagnostic imaging (MeSH) ; Humans (MeSH) ; Image Processing, Computer-Assisted: methods (MeSH) ; Image Processing, Computer-Assisted: standards (MeSH) ; Iron (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Magnetic Resonance Imaging: standards (MeSH) ; Models, Theoretical (MeSH) ; Neuroimaging: methods (MeSH) ; Neuroimaging: standards (MeSH) ; Phlebography: methods (MeSH) ; Phlebography: standards (MeSH) ; Reproducibility of Results (MeSH) ; Sensitivity and Specificity (MeSH) ; Iron

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Contributing Institute(s):

1. Cognitive Neurology and Neurodegeneration (AG Nestor)
2. Clinical Neurophysiology and Memory (AG Düzel)

Research Program(s):

1. 344 - Clinical and Health Care Research (POF3-344) (POF3-344)

Appears in the scientific report 2018

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Database coverage:
; ; ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Ebsco Academic Search ; IF >= 5 ; JCR ; Nationallizenz ; SCOPUS ; Web of Science Core Collection

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