Quantitative Susceptibility Mapping (QSM): Decoding MRI Data for a Tissue Magnetic Biomarker

By Yi Wang, Tian Liu. Magnetic Resonance in Medicine, 2014

864 citations recorded by the source corpus.

In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating a chemical shift for observer protons within the molecule and a magnetic susceptibility inhomogeneity field for observer protons outside the molecule. The number of water protons surrounding a molecule for detecting its magnetic susceptibility is vastly greater than the number of protons within the molecule for detecting its chemical shift. However, the study of tissue magnetic susceptibility has been hindered by poor molecular specificities of hitherto used methods based on MRI signal phase and T2* contrast, which depend convolutedly on surrounding susceptibility sources. Deconvolution of the MRI signal phase can determine tissue susceptibility but is challenged by the lack of MRI signal in the background and by the zeroes in the dipole kernel. Recently, physically meaningful regularizations, including the Bayesian approach, have been developed to enable accurate quantitative susceptibility mapping (QSM) for studying iron distribution, metabolic oxygen consumption, blood degradation, calcification, demyelination, and other pathophysiological susceptibility changes, as well as contrast agent biodistribution in MRI. This paper attempts to summarize the basic physical concepts and essential algorithmic steps in QSM, to describe clinical and technical issues under active development, and to provide references, codes, and testing data for readers interested in QSM. Magn Reson Med 73:82–101, 2015. © 2014 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance. This is an open access article under the terms of the Creative commons Attribution License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.

Subfields: Radiology, Nuclear Medicine and Imaging, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/455e181fe505a36406af032700fea2ca87a2e9b9

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Citations in the corpus

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging (2018, neutral): “Similarly specialized methods like quantitative susceptibility mapping (Wang and Liu, 2015) or the use of ultra-highfield MRI (Forstmann et al., 2017; 2014) are other potential ways of increasing anatomical precision.”
Quantitative susceptibility mapping using deep neural network: QSMnet (2018, methodology): “…is cooperative, one can obtain a more accurate QSM map by acquiring GRE data from multiple head orientations with respect to B0 and reconstructing the data using the Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) algorithm (Liu et al., 2009; Wharton and Bowtell, 2010).”
Toward defining deep brain stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural connectivity (2016, methodology): “This may lead to a smaller representation of the STN on standard MRI and greatly justiﬁes the use of histological atlases or imaging techniques that are more sensitive to iron, such as quantitative susceptibility mapping (Wang & Liu 2015).”
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, positive): “The neuromelanin levels were quantitatively assessed using neuromelanin-sensitive MRI and quantitative susceptibility mapping (QSM) [127] , an MRI modality for measuring the absolute concentrations of iron, calcium, and other substances in tissues based on changes of local susceptibility [128] .”