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A robust multi-scale approach to quantitative susceptibility mapping
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 p...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academic Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215336/ https://www.ncbi.nlm.nih.gov/pubmed/30075277 http://dx.doi.org/10.1016/j.neuroimage.2018.07.065 |
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author | Acosta-Cabronero, Julio Milovic, Carlos Mattern, Hendrik Tejos, Cristian Speck, Oliver Callaghan, Martina F. |
author_facet | Acosta-Cabronero, Julio Milovic, Carlos Mattern, Hendrik Tejos, Cristian Speck, Oliver Callaghan, Martina F. |
author_sort | Acosta-Cabronero, Julio |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6215336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62153362018-12-01 A robust multi-scale approach to quantitative susceptibility mapping Acosta-Cabronero, Julio Milovic, Carlos Mattern, Hendrik Tejos, Cristian Speck, Oliver Callaghan, Martina F. Neuroimage Article 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. Academic Press 2018-12 /pmc/articles/PMC6215336/ /pubmed/30075277 http://dx.doi.org/10.1016/j.neuroimage.2018.07.065 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Acosta-Cabronero, Julio Milovic, Carlos Mattern, Hendrik Tejos, Cristian Speck, Oliver Callaghan, Martina F. A robust multi-scale approach to quantitative susceptibility mapping |
title | A robust multi-scale approach to quantitative susceptibility mapping |
title_full | A robust multi-scale approach to quantitative susceptibility mapping |
title_fullStr | A robust multi-scale approach to quantitative susceptibility mapping |
title_full_unstemmed | A robust multi-scale approach to quantitative susceptibility mapping |
title_short | A robust multi-scale approach to quantitative susceptibility mapping |
title_sort | robust multi-scale approach to quantitative susceptibility mapping |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215336/ https://www.ncbi.nlm.nih.gov/pubmed/30075277 http://dx.doi.org/10.1016/j.neuroimage.2018.07.065 |
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