Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration

Li, Jianqi; Lin, Huimin; Liu, Tian; Zhang, Zhuwei; Prince, Martin R.; Gillen, Kelly M.; Xu, Yan; Song, Qi; Ting, Hua; Zhao, Xia; Zhang, Miao; Zhao, Yu; Li, Gaiying; Tang, Guangyu; Yang, Guang; Brittenham, Gary M.; Wang, Yi

doi:10.1002/jmri.26019

Cited by 50 publications

(95 citation statements)

References 41 publications

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“…Additionally, the fact that liver iron grade has a relatively small effect on liver R2* suggests that R2* may not be a suitable method for assessing liver iron in this population, even if corrections for PDFF are implemented. Liver fibrosis has also been shown to confound R2*, although to a much lesser extent in our study …”

Section: Discussionmentioning

confidence: 40%

Hepatic R2* is more strongly associated with proton density fat fraction than histologic liver iron scores in patients with nonalcoholic fatty liver disease

Bashir

Wolfson

Gamst

et al. 2018

Magnetic Resonance Imaging

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Background The liver R2* value is widely used as a measure of liver iron but may be confounded by the presence of hepatic steatosis and other covariates. Purpose To identify the most influential covariates for liver R2* values in patients with nonalcoholic fatty liver disease (NAFLD). Study Type Retrospective analysis of prospectively acquired data. Population Baseline data from 204 subjects enrolled in NAFLD/NASH (nonalcoholic steatohepatitis) treatment trials. Field Strength 1.5T and 3T; chemical‐shift encoded multiecho gradient echo. Assessment Correlation between liver proton density fat fraction and R2*; assessment for demographic, metabolic, laboratory, MRI‐derived, and histological covariates of liver R2*. Statistical Tests Pearson's and Spearman's correlations; univariate analysis; gradient boosting machines (GBM) multivariable machine‐learning method. Results Hepatic proton density fat fraction (PDFF) was the most strongly correlated covariate for R2* at both 1.5T (r = 0.652, P < 0.0001) and at 3T (r = 0.586, P < 0.0001). In the GBM analysis, hepatic PDFF was the most influential covariate for hepatic R2*, with relative influences (RIs) of 61.3% at 1.5T and 47.5% at 3T; less influential covariates had RIs of up to 11.5% at 1.5T and 16.7% at 3T. Nonhepatocellular iron was weakly associated with R2* at 3T only (RI 6.7%), and hepatocellular iron was not associated with R2* at either field strength. Data Conclusion Hepatic PDFF is the most influential covariate for R2* at both 1.5T and 3T; nonhepatocellular iron deposition is weakly associated with liver R2* at 3T only. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1456–1466.

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Section: Discussionmentioning

confidence: 40%

Hepatic R2* is more strongly associated with proton density fat fraction than histologic liver iron scores in patients with nonalcoholic fatty liver disease

Bashir

Wolfson

Gamst

et al. 2018

Magnetic Resonance Imaging

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“…As expected, QSM has been shown to be superior to R2* in regions near air-tissue interface, such as the midbrain (22,23). For measurement of the liver iron concentration (LIC), a fundamental limitation of the R2* method is that that intravoxel contents other than iron, including fibrosis, steatosis and necroinflammation, also alter relaxation (3). Hepatic fibrosis, highly prevalent in thalassemia major patients (25,26), increases R2* and interferes with accurate measurement of LIC which is challenging for commonly-encountered moderate overload patients (24).…”

Section: Discussionmentioning

confidence: 99%

“…For zero-reference in this study, we used the prior information that the blood in the aorta is uniform and does not accumulate iron. With similar assumptions, variations within the fat in the abdominal region can be minimized and susceptibility value of 0.7 ppm can be used as a reference (3,26). This requires a fat mask which may be automatically generated from PDFF.…”

Section: Discussionmentioning

confidence: 99%

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Rapid automated liver quantitative susceptibility mapping

Jafari

Sheth

Spincemaille

et al. 2019

Magnetic Resonance Imaging

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Background: Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC. Purpose: To develop a rapid, robust and automated liver QSM for clinical practice. Study Type: Prospective Population: 13 healthy subjects and 22 patients. Field strength/Sequences 1.5T and 3T / 3D multi-echo gradient-recalled echo (GRE) sequence. Assessment: Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2*-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (SPURS) in healthy subjects (n=5). Reproducibility was assessed over 4 scanners at 2 field strengths from 2 manufacturers using healthy subjects (n=8). Clinical feasibility was evaluated in patients (n=22). Statistical Tests: IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and ROI measurements. Reproducibility of QSM, R2*, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC). Results: Liver QSM using the IP method was found to be approximately 5.5 times faster than SPURS (P< 0.05) in initializing T2*-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between −0.06 to 0.07 ppm, ICC 0.97). Conclusion: Use of IP echo-based initialization, enables robust water/fat separation and field estimation for automated, rapid and reproducible liver QSM for clinical applications.

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“…Recently, QSM has been investigated as an alternative method for quantifying BMD, with promising initial results . Dimov et al showed that susceptibility values were closely correlated with CT measurements of BMD in a porcine hoof, and were able to generate susceptibility maps in which cortical bone was homogenous and diamagnetic, as expected from theory .…”

Section: Introductionmentioning

confidence: 95%

Association of bone mineral density and fat fraction with magnetic susceptibility in inflamed trabecular bone

Bray

Karsa

Bainbridge

et al. 2019

Magnetic Resonance in Med

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Purpose To evaluate the relationship between bone mineral density (BMD) and magnetic susceptibility, and between proton density fat fraction and susceptibility, in inflamed trabecular bone. Methods Two different phantoms modeling the fat fraction (FF) and BMD values of healthy bone marrow and disease states were scanned using a multiecho gradient echo acquisition at 3T. After correction for fat‐water chemical shift, susceptibility mapping was performed, and susceptibility measurements were compared with BMD and FF values using linear regression. Patients with spondyloarthritis were scanned using the same protocol, and susceptibility values were calculated in areas of inflamed bone (edema) and fat metaplasia, both before and after accounting for the contribution of fat to the total susceptibility. Results Susceptibility values in the phantoms were accurately described by a 2D linear function, with a negative correlation between BMD and susceptibility and a positive correlation between FF and susceptibility (adjusted R 2 = 0.77; P = 3·10 −5 ). In patients, significant differences in susceptibility were observed between fat metaplasia and normal marrow, but these differences were eliminated by removing the fat contribution to the total susceptibility. Conclusions BMD and proton density fat fraction both influence the total susceptibility of bone marrow and failure to account for the fat contribution could lead to errors in BMD quantification. We propose a method for removing the fat contribution from the total susceptibility, based on the observed linear relationship between susceptibility and FF. In inflamed bone, the overall increase in susceptibility in areas of fat metaplasia is at least partly due to increased fat content.

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Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration

Abstract: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1069-1079.

Cited by 50 publications

References 41 publications

Hepatic R2* is more strongly associated with proton density fat fraction than histologic liver iron scores in patients with nonalcoholic fatty liver disease

Hepatic R2* is more strongly associated with proton density fat fraction than histologic liver iron scores in patients with nonalcoholic fatty liver disease

Rapid automated liver quantitative susceptibility mapping

Association of bone mineral density and fat fraction with magnetic susceptibility in inflamed trabecular bone

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