Quantitative Susceptibility Mapping: Concepts and Applications

Reichenbach, Jürgen R.; Schweser, Ferdinand; Serres, Barthélemy; Deistung, Andreas

doi:10.1007/s00062-015-0432-9

Cited by 119 publications

(95 citation statements)

References 34 publications

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“…R2* increases proportionally to the concentration of both iron (Langkammer et al, 2010) and myelin (Lee et al, 2012), whilst the two substances exert opposing effects on magnetic susceptibility (Liu et al, 2011;Schweser et al, 2011) due to the strong diamagnetic component driven by phospholipids in myelin membranes (Langkammer et al, 2012;Li et al, 2012). QSM and R2* are, therefore, unique and potentially complimentary contrasts to explore whole brain age-related differences, chiefly owing to their differential sensitivities to iron and myelin content (Fukunaga et al, 2010); they may also be differentially modulated by other forms of mineralization such as calcifications (Chen et al, 2014), by brain oxygen-saturation levels (Rodrigue et al, 2011) and/or vascular pathology (Reichenbach et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

High-resolution characterisation of the aging brain using simultaneous quantitative susceptibility mapping (QSM) and R2* measurements at 7 T

et al. 2016

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Quantitative susceptibility mapping (QSM) has recently emerged as a novel magnetic resonance imaging (MRI) method to detect non-haem iron deposition, calcifications, demyelination and vascular lesions in the brain. It has been suggested that QSM is more sensitive than the more conventional quantifiable MRI measure, namely the transverse relaxation rate, R2*. Here, we conducted the first high-resolution, whole-brain, simultaneously acquired, comparative study of the two techniques using 7 Tesla MRI. We asked which of the two techniques would be more sensitive to explore global differences in tissue composition in elderly adults relative to young subjects. Both QSM and R2* revealed strong agerelated differences in subcortical regions, hippocampus and cortical grey matter, particularly in superior frontal regions, motor/premotor cortices, insula and cerebellar regions. Within the basal ganglia system-but also hippocampus and cerebellar dentate nucleus-, QSM was largely in agreement with R2* with the exception of the globus pallidus. QSM, however, provided superior anatomical contrast and revealed age-related differences in the thalamus and in white matter, which were otherwise largely undetected by R2* measurements. In contrast, in occipital cortex, age-related differences were much greater with R2* compared to QSM. The present study, therefore, demonstrated that in vivo QSM using ultra-high field MRI provides a novel means to characterize age-related differences in the human brain, but also combining QSM and R2* using multigradient recalled echo imaging can potentially provide a more complete picture of mineralization, demyelination and/or vascular alterations in aging and disease.

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Section: Introductionmentioning

confidence: 99%

High-resolution characterisation of the aging brain using simultaneous quantitative susceptibility mapping (QSM) and R2* measurements at 7 T

et al. 2016

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“…Quantification of liver iron concentration using the apparent susceptibility of hepatic vessels Quantitative susceptibility mapping (QSM) is a method for extracting tissue susceptibility from the magnetic field variation using data collected with a gradient-echo sequence (10)(11)(12)(13). It has great potential in improving both the accuracy and precision in the quantification of in vivo iron content, as demonstrated in various studies focused on the brain (14)(15)(16)(17)(18).…”

Section: Original Articlementioning

confidence: 99%

Quantification of liver iron concentration using the apparent susceptibility of hepatic vessels

Liu

Wang

Zhang

et al. 2018

Quant. Imaging Med. Surg.

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Background: The quantification of liver iron concentration (LIC) is important for the monitoring of the body iron level in patients with iron overload. Conventionally, LIC is quantified through R2 or R2* mapping using MRI. In this paper, we demonstrate an alternative approach for LIC quantification through measuring the apparent susceptibility of hepatic vessels using quantitative susceptibility mapping (QSM).Methods: QSM was performed in the liver region with the iterative susceptibility weighted imaging and mapping (iSWIM) algorithm, using the geometry of the vessels extracted from magnitude images as constraints. The susceptibilities of liver tissue were estimated from the apparent susceptibility of the hepatic veins and then converted to LIC. The accuracy of the proposed method was first validated using simulations, and then confirmed using in vivo data collected on 8 healthy controls and 11 patients at 3T. The effects of data acquisition parameters were studied using simulations, and the LICs estimated using QSM were compared with those estimated using R2* mapping. Results:Simulation results showed that the use of a 3D data acquisition protocol with higher image resolution led to improved accuracy in LIC quantification using QSM. Both simulations and in vivo data results demonstrated that the LICs estimated using the proposed QSM method agreed well with those estimated using R2* mapping. With the shortest echo time being 2.5ms in the multi-echo gradient echo sequence, simulations results showed that LIC up to 12.45 mg iron/g dry tissue can be quantified using the proposed QSM method. For the in vivo data, the highest LIC measured was 11.32 mg iron/g dry tissue.Conclusions: The proposed method offers a reliable and flexible way to quantify LIC and has the potential to extend the range of LIC that can be accurately measured using R2* and QSM.

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“…1) (Deistung et al, 2008;Duyn et al, 2007;Reichenbach, 2012;Reichenbach et al, 2015Reichenbach et al, , 1997Schenck and Zimmerman, 2004).…”

Section: Properties Of the Gre-mri Signalunclassified

“…white matter, gray matter and CSF) at a high spatial resolution (Duyn, 2017;Liu et al, 2015Liu et al, , 2015Reichenbach et al, 2015;Schweser et al, 2016). SWI enhances susceptibility-induced contrast by merging the T2 * weighted magnitude image with a filtered phase map in a multiplicative fashion (de Crispgny et al, 1993;Liu et al, 2015).…”

Section: Ch 1 Temporal Susceptibility Mapping and Investigation Of Nmentioning

confidence: 99%

“…However, SWI images can only provide qualitative information about an anatomical region because the individual voxels do not reflect a physical quantity. Quantitative susceptibility mapping (QSM) on the other hand is an emerging post-processing tool that isolates the local tissue magnetic susceptibility (source) from the measured magnetic field distribution (effect) Liu et al, 2009;Marques and Bowtell, 2005;Reichenbach et al, 2015;Sun and Wilman, 2014). SWI and QSM share similar parameters with regards to data acquisition, as well as certain data processing procedures, such as phase unwrapping, background field removal, and mask generation.…”

Section: Ch 1 Temporal Susceptibility Mapping and Investigation Of Nmentioning

confidence: 99%

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Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

Kadamangudi¹

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Magnetic resonance imaging (MRI) has played a key role in our understanding of the brain's anatomy and physiology. In particular, gradient recalled echo magnetic resonance imaging (GRE-MRI) at ultra-high field holds great promise for new contrast mechanisms to examine brain structure non-invasively. Multi-echo GRE-MRI is affected by signal compartments which may inform structural characterization. A number of studies have adopted the three water-pool compartment model to study white matter brain regions by associating individual compartments with myelin, axonal and extracellular water. Many key questions, however, remain unanswered in the context of GRE-MRI signal compartmentalization.First, the number and identifiability of GRE-MRI signal compartments has not been fully explored. We examined these issues in the human brain using a data driven approach, as detailed in Ch. 1. Multiple echo time GRE-MRI data were acquired in five healthy participants, each brain was segmented into anatomical regions (substantia nigra, caudate, insula, putamen, thalamus, fornix, internal capsule, corpus callosum and cerebrospinal fluid) and the temporal signal fitted with models with one to six signal compartments. With the use of information criteria and cluster analysis methods we ascertained the number of distinct signal compartments within each region and established differences in their respective frequency shifts between the brain regions studied. We identified five dominant signal compartments; these contributed to the local signal frequency of each brain region differently. Maps of compartment volume fractions, resolved by fixing the respective compartment frequency shifts in each voxel, corresponded with commonly observed tissue properties.Second, the influence of the scanner field strength on the temporal evolution of the multiecho GRE-MRI signal is not known. Subsequently, the influence of scanner field strength on GRE-MRI signal compartments also remains uncharacterized. In Ch. 3, we evaluated variations in the signal frequency shifts due to changes in field strength within the putamen, CSF, and corpus callosum (representatives of gray matter, CSF, and white mater regions respectively). Multiple echo time GRE-MRI data at 3T and 7T were acquired in six healthy participants, and temporal frequency shift profiles were generated via the quantitative susceptibility pipeline. From a qualitative lens, we observed unique echo-time dependent frequency shift profiles for each brain region to broadly correspond between 3T and 7T measurements. Furthermore, inter-participant variability in frequency shifts were higher in MPhil Thesis -Shrinath Kadamangudi 3 3T measurements than at 7T. In general, signal compartment frequency shifts correspond well between 3T and 7T data, and standard error estimates indicate an improved quality-of-fit within the putamen and CSF, when compared to the corpus callosum. Compartment frequency shifts mapped using multi-echo GRE-MRI signal compartment models may thus provide new insights into tissu...

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Quantitative Susceptibility Mapping: Concepts and Applications

Cited by 119 publications

References 34 publications

High-resolution characterisation of the aging brain using simultaneous quantitative susceptibility mapping (QSM) and R2* measurements at 7 T

High-resolution characterisation of the aging brain using simultaneous quantitative susceptibility mapping (QSM) and R2* measurements at 7 T

Quantification of liver iron concentration using the apparent susceptibility of hepatic vessels

Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

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