Relaxivity-iron calibration in hepatic iron overload: probing underlying biophysical mechanisms using a Monte Carlo model

Effect of particle size on liver MRI R 2 * relaxometry: Monte Carlo simulation and phantom studies

PURPOSE

To investigate the effect of particle size on liverR 2 * $$ {\mathrm{R}}_2^{\ast } $$ by Monte Carlo simulation and phantom studies at both 1.5 T and 3.0 T.

METHODS

Two kinds of particles (i.e., iron sphere and fat droplet) with varying sizes were considered separately in simulation and phantom studies. MRI signals were synthesized and analyzed for predictingR 2 * $$ {\mathrm{R}}_2^{\ast } $$ , based on simulations by incorporating virtual liver model, particle distribution, magnetic field generation, and proton movement into phase accrual. In the phantom study, iron-water and fat-water phantoms were constructed, and each phantom contained 15 separate vials with combinations of five particle concentrations and three particle sizes.R 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements in the phantom were made at both 1.5 T and 3.0 T. Finally, differences inR 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions or measurements were evaluated across varying particle sizes.

RESULTS

In the simulation study, strong linear and positively correlated relationships were observed betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions and particle concentrations across varying particle sizes and magnetic field strengths (r ≥ 0.988 $$ r\ge 0.988 $$ ). The relationships were affected by iron sphere size (p < 0.001 $$ p<0.001 $$ ), where smaller iron sphere size yielded higher predictedR 2 * $$ {\mathrm{R}}_2^{\ast } $$ , whereas fat droplet size had no effect onR 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions (p ≥ 0.617 $$ p\ge 0.617 $$ ) for constant total fat concentration. Similarly, the phantom study showed thatR 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements were relatively sensitive to iron sphere size (p ≤ 0.004 $$ p\le 0.004 $$ ) unlike fat droplet size (p ≥ 0.223 $$ p\ge 0.223 $$ ).

CONCLUSION

LiverR 2 * $$ {\mathrm{R}}_2^{\ast } $$ is affected by iron sphere size, but is relatively unaffected by fat droplet size. These findings may lead to an improved understanding of the underlying mechanisms ofR 2 * $$ {\mathrm{R}}_2^{\ast } $$ relaxometry in vivo, and enable improved quantitative MRI phantom design.

Hepatic steatosis modeling and MRI signal simulations for comparison of single- and dual-R2* models and estimation of fat fraction at 1.5T and 3T

BACKGROUND AND OBJECTIVE

Magnetic resonance imaging (MRI) has emerged as a noninvasive clinical tool for assessment of hepatic steatosis. Multi-spectral fat-water MRI models, incorporating single or dual transverse relaxation decay rate(s) (R2*) have been proposed for accurate fat fraction (FF) estimation. However, it is still unclear whether single- or dual-R2* model accurately mimics in vivo signal decay for precise FF estimation and the impact of signal-to-noise ratio (SNR) on each model performance. Hence, this study aims to construct virtual steatosis models and synthesize MRI signals with different SNRs to systematically evaluate the accuracy of single- and dual-R2* models for FF and R2* estimations at 1.5T and 3.0T.

METHODS

Realistic hepatic steatosis models encompassing clinical FF range (0-60 %) were created using morphological features of fat droplets (FDs) extracted from human liver biopsy samples. MRI signals were synthesized using Monte Carlo simulations for noise-free (SNRideal) and varying SNR conditions (5-100). Fat-water phantoms were scanned with different SNRs to validate simulation results. Fat water toolbox was used to calculate R2* and FF for both single- and dual-R2* models. The model accuracies in R2* and FF estimates were analyzed using linear regression, bias plot and heatmap analysis.

RESULTS

The virtual steatosis model closely mimicked in vivo fat morphology and Monte Carlo simulation produced realistic MRI signals. For SNRideal and moderate-high SNRs, water R2* (R2*W) by dual-R2* and common R2* (R2*com) by single-R2* model showed an excellent agreement with slope close to unity (0.95-1.01) and R2 > 0.98 at both 1.5T and 3.0T. In simulations, the R2*com-FF and R2*W-FF relationships exhibited slopes similar to in vivo calibrations, confirming the accuracy of our virtual models. For SNRideal, fat R2* (R2*F) was similar to R2*W and dual-R2* model showed slightly higher accuracy in FF estimation. However, in the presence of noise, dual-R2* produced higher FF bias with decreasing SNR, while leading to only marginal improvement for high SNRs and in regions dominated by fat and water. In contrast, single-R2* model was robust and produced accurate FF estimations in simulations and phantom scans with clinical SNRs.

CONCLUSION

Our study demonstrates the feasibility of creating virtual steatosis models and generating MRI signals that mimic in vivo morphology and signal behavior. The single-R2* model consistently produced lower FF bias for clinical SNRs across entire FF range compared to dual-R2* model, hence signifying that single-R2* model is optimal for assessing hepatic steatosis.

Simulation of a virtual liver iron overload model and R2 * estimation using multispectral fat-water models for GRE and UTE acquisitions at 1.5 T and 3 T

R2 *-MRI has emerged as a noninvasive alternative to liver biopsy for assessment of hepatic iron content (HIC). Multispectral fat-water R2 * modeling techniques such as the nonlinear least squares (NLSQ) fitting and autoregressive moving average (ARMA) models have been proposed for the accurate assessment of iron overload by also considering fat, which can otherwise confound R2 *-based HIC measurements in conditions of coexisting iron overload and steatosis. However, the R2 * estimation by these multispectral models has not been systematically investigated for various acquisition methods in iron overload only conditions and across the full clinically relevant range of HICs (0-40 mg Fe/g dry liver weight). The purpose of this study is to evaluate the R2 * accuracy and precision of multispectral models for various multiecho gradient echo (GRE) and ultrashort echo time (UTE) imaging acquisitions by constructing virtual iron overload models based on true histology and synthesizing MRI signals via Monte Carlo simulations at 1.5 T and 3 T, and comparing their results with monoexponential model and published in vivo R2 *-HIC calibrations. The signals were synthesized with TE1  = 1.0 ms for GRE and TE1  = 0.1 ms for UTE acquisition for varying echo spacing, ΔTE (0.1, 0.5, 1, 2 ms), and maximum echo time, TEmax (2, 4, 6, 10 ms). An iron-doped phantom study is also conducted to validate the simulation results in experimental GRE (TE1  = 1.2 ms, ΔTE  = 0.72 ms, TEmax  = 6.24 ms) and UTE (TE1  = 0.1 ms, ΔTE  = 0.5 ms, TEmax  = 6.1 ms) acquisitions. For GRE acquisitions, the multispectral ARMA and NLSQ models produced higher slopes (0.032-0.035) compared with the monoexponential model and published in vivo R2 *-HIC calibrations (0.025-0.028). However, for UTE acquisition for shorter echo spacing (≤0.5 ms) and longer maximum echo time, TEmax (≥6 ms), the multispectral and monoexponential signal models produced similar R2 *-HIC slopes (1.5 T, 0.028-0.032; 3 T, 0.014-0.016) and precision values (coefficient of variation < 25%) across the full clinical spectrum of HICs at both 1.5 T and 3 T. The phantom analysis also showed that all signal models demonstrated a significant improvement in R2 * estimation for UTE acquisition compared with GRE, confirming our simulation findings. Future work should investigate the performance of multispectral fat-water models by simulating liver models in coexisting conditions of iron overload and steatosis for accurate R2 * and fat quantification.

Splenic iron decreases without change in volume or liver parameters during luspatercept therapy

Splenic iron decreased whereas liver iron was stable during luspatercept therapy in some individuals with thalassemia. This suggests a reduction of ineffective erythropoiesis changes the organ distribution of iron and demonstrates that liver iron concentration alone may not accurately reflect total body iron content. This article describes data from subjects enrolled in BELIEVE (NCT02604433) and BEYOND (NCT03342404).

Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. ^© RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Monte Carlo modeling of hepatic steatosis based on stereology and spatial distribution of fat droplets

BACKGROUND AND OBJECTIVE

To model hepatic steatosis in adult humans with non-alcoholic fatty liver disease based on stereology and spatial distribution of fat droplets from liver biopsy specimens.

METHODS

Histological analysis was performed on 30 adult human liver biopsy specimens with varying degrees of steatosis. Morphological features of fat droplets were characterized by gamma distribution function (GDF) in both two-dimensional (2D) and three-dimensional (3D) spaces from three aspects: 1) size distribution indicating non-uniformity of fat droplets in radius; 2) nearest neighbor distance distribution indicating heterogeneous accumulation (i.e., clustering) of fat droplets; 3) regional anisotropy indicating inter-regional variability in fat fraction (FF). To generalize the morphological description of hepatic steatosis to different FFs, correlation analysis was performed among the estimated GDF parameters and FFs for all specimens. Finally, Monte Carlo modeling of hepatic steatosis was developed to simulate fat droplet distribution in tissue.

RESULTS

Morphological features, including size and nearest neighbor distance in 2D and 3D spaces as well as regional anisotropy, statistically captured the distribution of fat droplets by the GDF fit (R2 > 0.54). The estimated GDF parameters (i.e., scale and shape parameters) and FFs were well correlated, with R2 > 0.55. In addition, simulated 3D liver morphological models demonstrated similar sections to real histological samples both visually and quantitatively.

CONCLUSIONS

The morphology of hepatic steatosis is well characterized by stereology and spatial distribution of fat droplets. Simulated models demonstrate similar appearances to real histological samples. Furthermore, the model may help understand MRI signal behavior in the presence of liver steatosis.

Brain iron imaging markers in the presence of white matter hyperintensities

PURPOSE

To investigate the relationship between pathological brain iron deposition and white matter hyperintensities (WMHs) in cerebral small vessel disease (CSVD), via Monte Carlo simulations of magnetic susceptibility imaging and the development of a novel imaging marker called the Expected Iron Coefficient (EIC).

METHODS

A synthetic pathological model of a different number of impenetrable spheres at random locations was employed to represent pathological iron deposition. The diffusion process was simulated with a Monte Carlo method with adjustable parameters to manipulate sphere size, distribution, and extracellular properties. Quantitative susceptibility mapping (QSM) was performed in a clinical dataset to study CSVD to derive and evaluate QSM, R2*, the iron microenvironment coefficient (IMC), and the EIC in the presence of WMHs.

RESULTS

The simulations show that QSM signals increase in the presence of increased tissue iron, confirming that the EIC increases with pathology. Clinical results demonstrate that while QSM, R2*, and the IMC do not show significant differences in brain iron, the EIC does in the context of CSVD.

CONCLUSION

The EIC is more sensitive to subtle changes in brain iron deposition caused by pathology, even when QSM, R2*, and the IMC fail.

Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR

Accumulation of excess iron in the body, or systemic iron overload, results from a variety of causes. The concentration of iron in the liver is linearly related to the total body iron stores and, for this reason, quantification of liver iron concentration (LIC) is widely regarded as the best surrogate to assess total body iron. Historically assessed using biopsy, there is a clear need for noninvasive quantitative imaging biomarkers of LIC. MRI is highly sensitive to the presence of tissue iron and has been increasingly adopted as a noninvasive alternative to biopsy for detection, severity grading, and treatment monitoring in patients with known or suspected iron overload. Multiple MRI strategies have been developed in the past 2 decades, based on both gradient-echo and spin-echo imaging, including signal intensity ratio and relaxometry strategies. However, there is a general lack of consensus regarding the appropriate use of these methods. The overall goal of this article is to summarize the current state of the art in the clinical use of MRI to quantify liver iron content and to assess the overall level of evidence of these various methods. Based on this summary, expert consensus panel recommendations on best practices for MRI-based quantification of liver iron are provided.

Quantitative Analysis of Liver Iron Deposition Based on Dual-Energy CT in Thalassemia Patients

Background

To explore the feasibility and accuracy of liver iron deposition based on dual-energy CT in thalassemia patients.

Materials and methods

105 thalassemia patients were examined with dual-energy CT and MR liver scanning. Dual-energy CT was performed to measure CT values on 80kVp, 140kVp, and virtual iron content (VIC) imaging; ΔH was figured out by the difference in CT values between 80kVp and 140kVp. Using the liver iron concentration (LIC) obtained by FerriScan as a gold standard, the correlation between CT measurements and LIC was evaluated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance for dual-energy CT in liver iron quantification and stratification.

Results

The correlation analysis between CT measurements and LIC showed that 80kVp, 140kVp, VIC, and ΔH all had a high positive correlation with LIC (P<0.001). The correlation analysis among different degree groups of VIC, ΔH, and LIC showed that the normal, moderate, and severe groups of VIC and ΔH had moderate or high positive correlations with that of LIC (P<0.01), but the mild group had no correlation (P>0.05). ROC analysis revealed that the corresponding optimal cutoff value of VIC was -2.8, 6.3,11.9 HU (corresponds to 3.2,7.0,15.0 mg/g dry weight) respectively, while the ΔH were 5.1, 8.4, 17.8HU, respectively. The area under the receiver operating characteristic curves (AUCs) for both VIC and ΔH increased with LIC thresholds.

Conclusion

Dual-energy CT can accurately quantify and stratify liver iron deposition, contributing to predicting the status of liver iron deposition in thalassemia patients.

Relaxivity-iron calibration in hepatic iron overload: Reproducibility and extension of a Monte Carlo model

The aim of this study was to reproduce relaxivity-iron calibration in hepatic iron overload using a Monte Carlo model, and further extend the model with multiple spin echo (MSE) imaging. As previously reported, relationships between relaxation rates ( R2* and single spin echo R₂ ) and liver iron concentration (LIC) can be characterized by a Monte Carlo model incorporating realistic liver structure, iron distribution, and proton mobility. In this study, relaxivity-iron calibration curves at 1.5 and 3.0 T were simulated using the Monte Carlo model. Furthermore, the model was extended with MSE imaging, and iron calibrations were evaluated using two different fitting models: mononexponential with a constant offset and nonmonoexponential. Results consistent with previous empirical calibrations and Monte Carlo predictions were accurately reproduced for relaxivity-iron calibration. The predicted R2* and single spin echo R₂ increased by a factor of 2.00 and 1.51, respectively, at 1.5 versus 3.0 T. MSE signals and their corresponding R₂ depended strongly on LIC, interecho time, and field strength. Preliminary results showed that a nonmonoexponential model accurately characterizes the simulated MSE signals, and that strong correlations were found between predicted relaxation parameters and LIC. In conclusion, relaxivity-iron calibration is reproducible using the proposed Monte Carlo model. Furthermore, this model can be readily extended to other important applications, including predicting signal behavior for MSE imaging.

Evaluation of Hepatic Iron Overload Using a Contemporary 0.55 T MRI System

BACKGROUND

MRI T2* and R2* mapping have gained clinical acceptance for noninvasive assessment of iron overload. Lower field MRI may offer increased measurement dynamic range in patients with high iron concentration and may potentially increase MRI accessibility, but it is compromised by lower signal-to-noise ratio that reduces measurement precision.

PURPOSE

To characterize a high-performance 0.55 T MRI system for evaluating patients with liver iron overload.

STUDY TYPE

Prospective.

POPULATION

Forty patients with known or suspected iron overload (sickle cell anemia [n = 5], ß-thalassemia [n = 3], and hereditary spherocytosis [n = 2]) and a liver iron phantom.

FIELD STRENGTH/SEQUENCE

A breath-held multiecho gradient echo sequence at 0.55 T and 1.5 T.

ASSESSMENT

Patients were imaged with T2*/R2* mapping 0.55 T and 1.5 T within 24 hours, and 16 patients returned for follow-up exams within 6-16 months, resulting in 56 paired studies. Liver T2* and R2* measurements and standard deviations were compared between 0.55 T and 1.5 T and used to validate a predictive model between field strengths. The model was then used to classify iron overload at 0.55 T.

STATISTICAL TESTS

Linear regression and Bland-Altman analysis were used for comparisons, and measurement precision was assessed using the coefficient of variation. A P-value < 0.05 was considered statistically significant.

RESULTS

R2* was significantly lower at 0.55 T in our cohort (488 ± 449 s^-1 at 1.5 T vs. 178 ± 155 s^-1 at 0.55 T, n = 56 studies) and in the patients with severe iron overload (937 ± 369 s^-1 at 1.5 T vs. 339 ± 127 s^-1 at 0.55 T, n = 23 studies). The coefficient of variation indicated reduced precision at 0.55 T (3.5 ± 2.2% at 1.5 T vs 6.9 ± 3.9% at 0.55 T). The predictive model accurately predicted 1.5 T R2* from 0.55 T R2* (Bland Altman bias = -6.6 ± 20.5%). Using this model, iron overload at 0.55 T was classified as: severe R2* > 185 s^-1 , moderate 81 s^-1  < R2* < 185 s^-1 , and mild 45 s^-1  < R2* < 91 s^-1 .

DATA CONCLUSION

We demonstrated that 0.55 T provides T2* and R2* maps that can be used for the assessment of liver iron overload in patients.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Effects of B1 + Heterogeneity on Spin Echo-Based Liver Iron Estimates

BACKGROUND

Liver iron concentration (LIC) measured by MRI has become the clinical reference standard for managing iron overload in chronically transfused patients. Transverse relaxivity (R₂ or R₂ ^* ) measurements are converted to LIC units using empirically derived calibration curves.

HYPOTHESIS

That flip angle (FA) error due to B₁ ⁺ spatial heterogeneity causes significant LIC quantitation error. B₁ ⁺ scale (b₁ , [FA_actual /FA_specified ]) variation is a major problem at 3 T which could reduce the accuracy of transverse relaxivity measurements.

STUDY TYPE

Prospective.

POPULATION

Forty-seven subjects with chronic transfusional iron overload undergoing clinically indicated LIC assessment.

FIELD STRENGTH/SEQUENCE

5 T/3 T dual-repetition time B₁ ⁺ mapping sequence ASSESSMENT: We quantified the average/standard deviation b₁ in the right and left lobes of the liver from B₁ ⁺ maps acquired at 1.5 T and 3 T. The impact of b₁ variation on spin echo LIC estimates was determined using a Monte Carlo model.

STATISTICAL TESTS

Mean, median, and standard deviation in whole liver and right and left lobes; two-sided t-test between whole-liver b₁ means.

RESULTS

Average b₁ within the liver was 99.3% ± 12.3% at 1.5 T versus 69.6% ± 14.6% at 3 T and was independent of iron burden (P < 0.05). Monte Carlo simulations demonstrated that b₁ systematically increased R₂ estimates at lower LIC (<~25 mg/g at 1.5 T, <~15 mg/g at 3 T) but flattened or even inverted the R₂ -LIC relationship at higher LIC (≥~25 mg/g to 1.5 T, ≥~15 mg/g to 3 T); changes in the R₂ -LIC relationship were symmetric with respect to over and under excitation and were similar at 1.5 T and 3 T (for the same R₂ value). The R₂ ^* -LIC relationship was independent of b₁ .

CONCLUSION

Spin echo R₂ measurement of LIC at 3 T is error-prone without correction for b₁ errors. The impact of b₁ error on current 1.5 T spin echo-based techniques for LIC quantification is large enough to introduce measurable intersubject variability but the in vivo effect size needs a dedicated validation study.

LEVEL OF EVIDENCE

1.

TECHNICAL EFFICACY STAGE

2.

Improving CPMG liver iron estimates with a T 1 -corrected proton density estimator

PURPOSE

CPMG spin echo acquisitions are attractive for diagnosing and monitoring liver iron concentration in iron overload disorders due to their time efficiency and potential to reveal unique information about tissue iron distribution. Clinical adoption remains low due to the insensitivity of CPMG-based R 2 estimates to liver iron concentration (LIC) when common fitting techniques are applied. In this work, we demonstrate that the inclusion of a proton density estimator (PDE) derived from the CPMG acquisition increase the sensitivity of CPMG R 2 estimates to LIC in both simulated and in-vivo human data.

THEORY AND METHODS

CPMG R 2 acquisitions from 50 clinically indicated MRI studies in patients with iron overload were analyzed with and without PDE constraints. Liver regions of interest were fit to monoexpontial and nonexponential signal decay equations. LIC by R 2 ∗ served as the reference standard. The observed calibration between CPMG R 2 values and LIC were compared to results predicted from a previously validated Monte Carlo model.

RESULTS

The sensitivity of CPMG-derived R 2 triples when a proton density constraint is applied. When compared with R 2 ∗ -LIC estimates, both monoexponential and nonexponential models were unbiased but demonstrated broad 95% confidence intervals particularly for LIC values below 12 mg/g. Absolute error did not increase with LIC.

CONCLUSION

A proton density constraint can increase the sensitivity of CPMG-based models to iron. CPMG acquisitions are time-efficient and could potentially improve the dynamic range of single spin echo techniques as well as providing insight into tissue iron distribution.

MRI-Based Quantitative R2* Mapping at 3 Tesla Reflects Hepatic Iron Overload and Pathogenesis in Nonalcoholic Fatty Liver Disease Patients

BACKGROUND

The role of hepatic iron overload (HIO) in nonalcoholic fatty liver disease (NAFLD) pathogenesis has not been fully elucidated.

PURPOSE

This study aimed to investigate the effect of HIO and examine the diagnostic usefulness of magnetic resonance imaging (MRI)-based R2^* quantification in evaluating hepatic iron content (HIC) and pathological findings in NAFLD.

STUDY TYPE

Prospective and retrospective.

POPULATION

A prospective study of 168 patients (age, 57.2 ± 15.0; male/female, 80/88) and a retrospective validation study of 202 patients (age, 57.0 ± 14.4; male/female, 113/89) with liver-biopsy-confirmed NAFLD were performed.

FIELD STRENGTH/SEQUENCE

3 T; chemical-shift encoded multi-echo gradient echo.

ASSESSMENT

Using liver tissues obtained by liver biopsy, HIC was prospectively evaluated in 168 patients by atomic absorption spectrometry. Diagnostic accuracies of HIC and R2^* for grading hepatic inflammation plus ballooning (HIB) as an indicator of NAFLD activity were assessed.

STATISTICAL TESTS

Student's t-test and analysis of variance (ANOVA) with Scheffe's multiple testing correction for univariate comparisons; multivariate logistic analysis. P-value less than 0.05 is statistically significant.

RESULTS

HIC was significantly correlated with HIB grades (r = 0.407). R2^* was significantly correlated with HIC (r = 0.557) and HIB grades (r = 0.569). R2^* mapped an area under the receiver operating characteristic (AUROC; 0.774) for HIC ≥808 ng/mL (median value) with cutoff value of 62.5 s^-1 . In addition, R2^* mapped AUROC of HIB for grades ≥3 was 0.799 with cutoff value of 58.5 s^-1 . When R2^* was <62.5 s^-1 , R2^* correlated weakly with HIC (r = 0.372) as it was affected by fat deposition and did not correlate with HIB grades (P = 0.052). Conversely, when R2^* was ≥62.5 s^-1 , a significant correlation of R2^* with HIC (r = 0.556) and with HIB grades was observed (P < 0.0001) with being less affected by fat deposition.

DATA CONCLUSION

R2^*  ≥ 62.5 s^-1 is a promising modality for non-invasive diagnosis of clinically important high grades (≥3) of HIB associated with increased HIC.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 2.

Morphological characterization of hepatic steatosis and Monte Carlo modeling of MRI signal for accurate quantification of fat fraction and relaxivity

Chemical-shift-based fat-water MRI signal models with single- or dual-R₂ * correction have been proposed for quantification of fat fraction (FF) and assessment of hepatic steatosis. However, there is a void in our understanding of which model truly mimics the underlying biophysical mechanism of steatosis on MRI signal relaxation. The purpose of this study is to morphologically characterize and build realistic steatosis models from histology and synthesize MRI signal using Monte Carlo simulations to investigate the accuracy of single- and dual-R₂ * models in quantifying FF and R₂ *. Fat morphology was characterized by performing automatic segmentation on 16 mouse liver histology images and extracting the radius, nearest neighbor (NN) distance, and regional anisotropy of fat droplets. A gamma distribution function (GDF) was used to generalize extracted features, and regression analysis was performed to derive relationships between FF and GDF parameters. Virtual steatosis models were created based on derived morphological and statistical descriptors, and the MRI signal was synthesized at 1.5 T and 3 T. R₂ * and FF values were calculated using single- and dual-R₂ * models and compared with in vivo R₂ *-FF calibrations and simulated FFs. The steatosis models generated with regional anisotropy and NN distribution closely mimicked the true in vivo fat morphology. For both R₂ * models, predicted R₂ * values showed positive correlation with FFs, with slopes similar to those of the in vivo calibrations (P > 0.05), and predicted FFs showed excellent agreement with true FFs (R²  > 0.99), with slopes close to unity. Our study, hence, demonstrates the proof of concept for generating steatosis models from histologic data and synthesizing MRI signal to show the expected signal relaxation under conditions of steatosis. Our results suggest that a single R₂ * is sufficient to accurately estimate R₂ * and FF values for lower FFs, which agrees with in vivo studies. Future work involves characterizing and building steatosis models at higher FFs and testing single- and dual-R₂ * models for accurate assessment of steatosis.

Integrated quantitative susceptibility and R2 * mapping for evaluation of liver fibrosis: An ex vivo feasibility study

To develop a method for noninvasive evaluation of liver fibrosis, we investigated the differential sensitivities of quantitative susceptibility mapping (QSM) and R₂ * mapping using corrections for the effects of liver iron. Liver fibrosis is characterized by excessive accumulation of collagen and other extracellular matrix proteins. While collagen increases R₂ * relaxation, measures of R₂ * for fibrosis are confounded by liver iron, which may be present in the liver over a wide range of concentrations. The diamagnetic collagen contribution to susceptibility values measured by QSM is much less than the contribution of highly paramagnetic iron. In 19 ex vivo liver explants with and without fibrosis, QSM (χ), R₂ * and proton density fat fraction (PDFF) maps were constructed from multiecho gradient-recalled echo (mGRE) sequence acquisition at 3 T. Median parameter values were recorded and differences between the MRI parameters in nonfibrotic vs. advanced fibrotic/cirrhotic samples were evaluated using Mann-Whitney U tests and receiver operating characteristic analyses. Logistic regression with stepwise feature selection was employed to evaluate the utility of combined MRI measurements for detection of fibrosis. Median R₂ * increased in fibrotic vs. nonfibrotic liver samples (P = .041), while differences in χ and PDFF were nonsignificant (P = .545 and P = .395, respectively). Logistic regression identified the combination of χ and R₂ * significant for fibrosis detection (logit [prediction] = -8.45 + 0.23 R₂ * - 28.8 χ). For this classifier, a highly significant difference between nonfibrotic vs. advanced fibrotic/cirrhotic samples was observed (P = .002). The model exhibited an AUC of 0.909 (P = .003) for detection of advanced fibrosis/cirrhosis, which was substantially higher compared with AUCs of the individual parameters (AUC 0.591-0.784). An integrated QSM and R₂ * analysis of mGRE 3 T imaging data is promising for noninvasive diagnostic assessment of liver fibrosis.

Quantitative T1 Mapping for Detecting Microvascular Obstruction in Reperfused Acute Myocardial Infarction: Comparison with Late Gadolinium Enhancement Imaging

Objective

To compare native and post-contrast T1 mapping with late gadolinium enhancement (LGE) imaging for detecting and measuring the microvascular obstruction (MVO) area in reperfused acute myocardial infarction (MI).

Materials and Methods

This study included 20 patients with acute MI who had undergone 1.5T cardiovascular magnetic resonance imaging (CMR) after reperfusion therapy. CMR included cine imaging, LGE, and T1 mapping (modified look-locker inversion recovery). MI size was calculated from LGE by full-width at half-maximum technique. MVO was defined as an area with low signal intensity (LGE) or as a region of visually distinguishable T1 values (T1 maps) within infarcted myocardium. Regional T1 values were measured in MVO, infarcted, and remote myocardium on T1 maps. MVO area was measured on and compared among LGE, native, and post-contrast T1 maps.

Results

The mean MI size was 27.1 ± 9.7% of the left ventricular mass. Of the 20 identified MVOs, 18 (90%) were detected on native T1 maps, while 10 (50%) were recognized on post-contrast T1 maps. The mean native T1 values of MVO, infarcted, and remote myocardium were 1013.5 ± 58.5, 1240.9 ± 55.8 (p < 0.001), and 1062.2 ± 55.8 ms (p = 0.169), respectively, while the mean post-contrast T1 values were 466.7 ± 26.8, 399.1 ± 21.3, and 585.2 ± 21.3 ms, respectively (p < 0.001). The mean MVO areas on LGE, native, and post-contrast T1 maps were 134.1 ± 81.2, 133.7 ± 80.4, and 117.1 ± 53.3 mm², respectively. The median (interquartile range) MVO areas on LGE, native, and post-contrast T1 maps were 128.0 (58.1–215.4), 110.5 (67.7–227.9), and 143.0 (76.7–155.3) mm², respectively (p = 0.002). Concordance correlation coefficients for the MVO area between LGE and native T1 maps, LGE and post-contrast T1 maps, and native and post-contrast T1 maps were 0.770, 0.375, and 0.565, respectively.

Conclusion

MVO areas were accurately delineated on native T1 maps and showed high concordance with the areas measured on LGE. However, post-contrast T1 maps had low detection rates and underestimated MVO areas. Collectively, native T1 mapping is a useful tool for detecting MVO within the infarcted myocardium.

Decreasing iron susceptibility with temperature in quantitative susceptibility mapping: A phantom study

To clarify the temperature dependence of susceptibility estimated by quantitative susceptibility mapping (QSM) analysis, we investigated the relationship between temperature and susceptibility using a cylinder phantom with varying temperatures. Six solutions with various concentrations of superparamagnetic iron oxide (SPIO) nanoparticles were employed. These tubes were placed in a cylinder phantom and surrounded with water. The temperature of the circulated water was adjusted to change the temperature in the cylinder phantom from 25.8 °C to 42.5 °C. The cylinder phantom was scanned via a three-dimensional multiple spoiled gradient-echo sequence for R2* and QSM analyses with varying temperatures. The relationships between temperature, susceptibility, and R2* values were determined. Moreover, the temperature coefficients of susceptibility (χ-Tc) and (R2*-Tc) were calculated at each concentration and the linearities in these indices against each SPIO concentration were validated. Significant inverse correlations were found between temperature, susceptibility, and R2* values at each SPIO concentration due to the decrease in paramagnetic iron susceptibility that occurred with increasing temperature based on Curie's law. Moreover, although there were significant correlations between the susceptibility and R2* values at any temperature, the slopes of the regression lines grew in height with greater temperatures. The percentage of difference per Celsius degree in susceptibility in any SPIO concentration was lower than the corresponding finding among the R2* results. There were strong linearities between the SPIO concentration, χ-Tc (r = -0.994; p < 0.001), and R2*-Tc (r = -0.998; p < 0.001). The χ-Tc and R2*-Tc outcomes in a particular voxel varied considerably with the iron contents. Although there was an inverse correlation noted between temperature and susceptibility, the susceptibility analysis showed smaller temperature dependence relative to the R2* analysis. QSM analysis might be a more suitable option for magnetic resonance-based iron quantification in comparison with R2* relaxometry.

Design and evaluation of quantitative MRI phantoms to mimic the simultaneous presence of fat, iron, and fibrosis in the liver

PURPOSE

To design, construct, and evaluate quantitative MR phantoms that mimic MRI signals from the liver with simultaneous control of three parameters: proton-density fat fraction (PDFF), R 2 ∗ , and T₁ . These parameters are established biomarkers of hepatic steatosis, iron overload, and fibrosis/inflammation, respectively, which can occur simultaneously in the liver.

METHODS

Phantoms including multiple vials were constructed. Peanut oil was used to modulate PDFF, MnCl₂ and iron microspheres were used to modulate R 2 ∗ , and NiCl₂ was used to modulate the T₁ of water (T_1,water ). Phantoms were evaluated at both 1.5 T and 3 T using stimulated-echo acquisition-mode MRS and chemical shift-encoded MRI. Stimulated-echo acquisition-mode MRS data were processed to estimate T_1,water , T_1,fat , R 2 , water ∗ , and R 2 , fat ∗ for each vial. Chemical shift-encoded MRI data were processed to generate PDFF and R 2 ∗ maps, and measurements were obtained in each vial. Measurements were evaluated using linear regression and Bland-Altman analysis.

RESULTS

High-quality PDFF and R 2 ∗ maps were obtained with homogeneous values throughout each vial. High correlation was observed between imaging PDFF with target PDFF (slope = 0.94-0.97, R² = 0.994-0.997) and imaging R 2 ∗ with target R 2 ∗ (slope = 0.84-0.88, R² = 0.935-0.943) at both 1.5 T and 3 T. The values of R 2 , fat ∗ and R 2 , water ∗ were highly correlated with slope close to 1.0 at both 1.5 T (slope = 0.90, R² = 0.988) and 3 T (slope = 0.99, R² = 0.959), similar to the behavior observed in vivo. The value of T_1,water (500-1200 ms) was controlled with varying NiCl₂ concentration, while T_1,fat (300 ms) was independent of NiCl₂ concentration.

CONCLUSION

Novel quantitative MRI phantoms that mimic the simultaneous presence of fat, iron, and fibrosis in the liver were successfully developed and validated.

Liver transplantation and cardiac illness: Current evidences and future directions

Contraindications to liver transplantation are gradually narrowing. Cardiac illness and chronic liver disease may manifest independently or may be superimposed on each other due to shared pathophysiology. Cardiac surgery involving the cardiopulmonary bypass in patients with Child-Pugh Class C liver disease is associated with a high risk of perioperative morbidity and mortality. Liver transplantation involves hemodynamic perturbations, volume shifts, coagulation abnormalities, electrolyte disturbances, and hypothermia, which may prove fatal in patients with cardiac illness depending upon the severity. Additionally, cardiovascular complications are the major cause of adverse postoperative outcomes after liver transplantation even in the absence of cardiac pathologies. Clinical decision-making has remained an unsettled issue in these clinical scenarios. The absence of randomized clinical studies has further crippled our endeavours for a consensus on the management of patients with end-stage liver disease with cardiac illness. This review seeks to address this complex clinical setting by gathering information from published literature. The management algorithm in this review may facilitate clinical decision making and augur future research.

Sensitivity of quantitative relaxometry and susceptibility mapping to microscopic iron distribution

PURPOSE

Determine the impact of the microscopic spatial distribution of iron on relaxometry and susceptibility-based estimates of iron concentration.

METHODS

Monte Carlo simulations and in vitro experiments of erythrocytes were used to create different microscopic distributions of iron. Measuring iron with intact erythrocyte cells created a heterogeneous distribution of iron, whereas lysing erythrocytes was used to create a homogeneous distribution of iron. Multi-echo spin echo and spoiled gradient echo acquisitions were then used to estimate relaxation parameters ( R 2 and R 2 * ) and susceptibility.

RESULTS

Simulations demonstrate that R 2 and R 2 * measurements depend on the spatial distribution of iron even for the same iron concentration and volume susceptibility. Similarly, in vitro experiments demonstrate that R 2 and R 2 * measurements depend on the microscopic spatial distribution of iron whereas the quantitative susceptibility mapping (QSM) susceptibility estimates reflect iron concentration without sensitivity to spatial distribution.

CONCLUSIONS

R 2 and R 2 * for iron quantification depend on the spatial distribution or iron. QSM-based estimation of iron concentration is insensitive to the microscopic spatial distribution of iron, potentially providing a distribution independent measure of iron concentration.

Quantitative ferumoxytol-enhanced MRI in pregnancy: A feasibility study in the nonhuman primate

OBJECTIVES

To assess the feasibility of ferumoxytol-enhanced MRI in pregnancy with a nonhuman primate model.

MATERIALS AND METHODS

In this prospective study, eleven pregnant rhesus macaques at day 98 ± 5 of gestation were divided into three groups, untreated control (UC) (n = 3), saline control (SC) (n = 4) and interleukin 1 beta (IL-1β) treated (IT) (n = 4), which were administered with either saline or IL-1β into the amniotic fluid. All animals were imaged at multiple time points before and after ferumoxytol administration (4 mg/kg). Longitudinal R2* and susceptibility of tissues were obtained using region-of-interest analysis and the longitudinal changes were assessed using linear mixed models and Student's t-test.

RESULTS

In fetuses, a slope of 0.3 s-1/day (P = 0.008), 0.00 ppm/day (P = 0.699) and - 0.2 s-1/day (P = 0.023) was observed in liver R2*, liver susceptibility, and lung R2*, respectively. In placentas, R2* and susceptibility increased immediately after ferumoxytol administration (P < 0.001) and decreased to baseline within two days. The mean change from baseline showed no significant difference between the SC group and the IT group at all scan time points. In maternal livers, R2* increased immediately after ferumoxytol administration, further increased at one-day, and then decreased but remained elevated (P < 0.001). The mean change from baseline showed no significant difference between the SC group and the IT group at all scan time points.

CONCLUSIONS

This work demonstrates the feasibility of quantitative ferumoxytol-enhanced MRI to measure dynamics of ferumoxytol delivery and washout in the placenta. Stable MRI measurements indicated no evidence of iron deposition in fetal tissues of nonhuman primates after maternal ferumoxytol exposure.

Topics on quantitative liver magnetic resonance imaging

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

The influence of liver fat deposition on the quantification of the liver-iron fraction using fast-kilovolt-peak switching dual-energy CT imaging and material decomposition technique: an in vitro experimental study

Background

To assess the feasibility of dual-energy spectral computed tomography (DECT) for quantifying the liver iron content (LIC) with material decomposition (MD) technique in vitro.

Methods

Liver-iron mixture samples (model A) and liver-iron-fat mixture samples (model B) were prepare and scanned by a single source DECT using GSI mode with successive tube currents of 200, 320, and 485 mA. A standard algorithm of 1.25 mm was used to reconstruct iron (fat) MD images and iron (water) MD images. The iron concentrations of all samples were measured and analyzed by Spearman's rank correlation and linear regression analysis.

Results

Significant positive linear correlations were found between virtual iron content (VIC) and LIC in the absence of fat (model A) and in the presence of fat (model B) in the range of LIC 0 to 25 mg/mL. The lines of best fit to model A had slopes around 1.1 and an intercept around (-1.5) mg/mL for iron (water) MD images, and had slopes around 1.1 and an intercept around (-10) mg/mL for iron (fat) MD images. The lines of best fit to the model B had slopes around 1.5 and an intercept around (-15) mg/mL. At the same value of LIC (LIC >0), the VIC values of model A were always higher than those of model B. At the high value of LIC (12.5 mg/mL), the VIC values of model B were similar, but they differed greatly from those of model A.

Conclusions

The fast-kilovolt-peak switching dual-energy CT imaging and MD techniques allow for quantification of iron content. Fat and the post-reconstruction algorithm of iron (fat) MD images, were confounding factors, and led to the underestimation and overestimation of LIC, respectively.

Liver R2* is affected by both iron and fat: A dual biopsy-validated study of chronic liver disease

BACKGROUND

Liver iron content (LIC) in chronic liver disease (CLD) is currently determined by performing an invasive liver biopsy. MRI using R2* relaxometry is a noninvasive alternative for estimating LIC. Fat accumulation in the liver, or proton density fat fraction (PDFF), may be a possible confounder of R2* measurements. Previous studies of the effect of PDFF on R2* have not used quantitative LIC measurement.

PURPOSE

To assess the associations between R2*, LIC, PDFF, and liver histology in patients with suspected CLD.

STUDY TYPE

Prospective.

POPULATION

Eighty-one patients with suspected CLD.

FIELD STRENGTH/SEQUENCE

1.5 T. Multiecho turbo field echo to quantify R2*. PRESS MRS to quantify PDFF.

ASSESSMENT

Each patient underwent an MR examination, followed by two needle biopsies immediately following the MR examination. The first biopsy was used for conventional histological assessment of LIC, whereas the second biopsy was used to quantitatively measure LIC using mass spectrometry. R2* was correlated with both LIC and PDFF. A correction for the influence of fat on R2* was calculated.

STATISTICAL TESTS

Pearson correlation, linear regression, and area under the receiver operating curve.

RESULTS

There was a positive linear correlation between R2* and PDFF (R = 0.69), after removing data from patients with elevated iron levels, as defined by LIC. R2*, corrected for PDFF, was the best method for identifying patients with elevated iron levels, with a correlation of R = 0.87 and a sensitivity and specificity of 87.5% and 98.6%, respectively.

DATA CONCLUSION

PDFF increases R2*.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:325-333.

Validation of a standardized MRI method for liver fat and T2* quantification

Purpose

Several studies have demonstrated the accuracy, precision, and reproducibility of proton density fat fraction (PDFF) quantification using vendor-specific image acquisition protocols and PDFF estimation methods. The purpose of this work is to validate a confounder-corrected, cross-vendor, cross field-strength, in-house variant LMS IDEAL of the IDEAL method licensed from the University of Wisconsin, which has been developed for routine clinical use.

Methods

LMS IDEAL is implemented using a combination of patented and/or published acquisition and some novel model fitting methods required to correct confounds which result from the imaging and estimation processes, including: water-fat ambiguity; T2* relaxation; multi-peak fat modelling; main field inhomogeneity; T1 and noise bias; bipolar readout gradients; and eddy currents. LMS IDEAL has been designed to use image acquisition protocols that can be installed on most MRI scanners and cloud-based image processing to provide fast, standardized clinical results. Publicly available phantom data were used to validate LMS IDEAL PDFF calculations against results from originally published IDEAL methodology. LMS PDFF and T2* measurements were also compared with an independent technique in human volunteer data (n = 179) acquired as part of the UK Biobank study.

Results

We demonstrate excellent agreement of LMS IDEAL across vendors, field strengths, and over a wide range of PDFF and T2* values in the phantom study. The performance of LMS IDEAL was then assessed in vivo against widely accepted PDFF and T2* estimation methods (LMS Dixon and LMS T2*, respectively), demonstrating the robustness of LMS IDEAL to potential sources of error.

Conclusion

The development and clinical validation of the LMS IDEAL algorithm as a chemical shift-encoded MRI method for PDFF and T2* estimation contributes towards robust, unbiased applications for quantification of hepatic steatosis and iron overload, which are key features of chronic liver disease.

Validation of water-fat MRI and proton MRS in assessment of hepatic fat and the heterogeneous distribution of hepatic fat and iron in subjects with non-alcoholic fatty liver disease

BACKGROUND

Research studies demonstrated pathologic lesions were unevenly distributed in patients with non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis. As hepatic steatosis occurs prior to steatohepatitis and other late stage liver conditions, the distribution pattern of hepatic fat and iron concentration should be investigated to prevent sampling variability. The first purpose of this study was to perform comparison and validation of in-house hepatic fat measurements using water-fat MRI and MRS. The second objective was to quantify hepatic fat-fraction and T2* values in left and right liver lobes using water-fat MRI.

METHOD

Fifty-four non-alcoholic adults (27 NAFLD, age: 42.8 ± 11.8), 27 non-NAFLD, age: 45.5 ± 11.2) and 46 non-alcoholic teenagers (23 NAFLD (age: 15.4 ± 2.6), 23 non-NAFLD (age: 13.9 ± 2.3) were recruited. All participants underwent chemical shift water-fat MRI and 1H MRS at 3 T. Hepatic steatosis was defined by intrahepatic triglyceride more than the threshold of 5.56% using MRS (clinical reference) and non-alcoholic was defined by alcohol ingestion of no more than 30 g and 20 g per day for male and female respectively. Hepatic fat-fractions in left and right liver lobes were measured using regions-of-interest (ROIs) approach. Three ROIs were drawn on the fat-fraction images and duplicated on to the co-registered T2* images at the inferior right, superior right and superior left liver lobes. Comparison and validation of water-fat MRI and MRS were performed using intraclass correlation coefficient (ICC) and Bland-Altman plot. Hepatic fat-fraction and T2* measured from the ROIs were compared using repeated measures ANOVA. Independent t-test was used for between groups analysis.

RESULTS

Statistical analysis indicated good correlation (R = 0.987) and agreement (ICC = 0.982) between MRS and water-fat MRI in hepatic fat measurements. Results indicated that hepatic fat was significantly higher in the right lobe compared to the left in NAFLD adults (p < 0.001) and NAFLD teenagers (p < 0.001). For T2*, significant difference between left and right lobes was observed in NAFLD adults (p < 0.001) and non-NAFLD adults (p < 0.001) but not in teenagers.

CONCLUSION

Hepatic fat measurements using MRS and water-fat MRI are statistically equivalent. In subjects with NAFLD regardless of their age, hepatic fat is stored preferentially in the right live lobe probably due to the streamline of blood flow to the right liver. T2* value is significantly higher in the right liver lobe in adults but not in the teenagers regardless of their hepatic fat contents probably due to the longer time span of hepatic iron accumulation.

Hemochromatosis: pathophysiology, evaluation, and management of hepatic iron overload with a focus on MRI

Hereditary hemochromatosis (HH) is an autosomal recessive disorder that occurs in approximately 1 in 200-250 individuals. Mutations in the HFE gene lead to excess iron absorption. Excess iron in the form of non-transferrin-bound iron (NTBI) causes injury and is readily uptaken by cardiomyocytes, pancreatic islet cells, and hepatocytes. Symptoms greatly vary among patients and include fatigue, abdominal pain, arthralgias, impotence, decreased libido, diabetes, and heart failure. Untreated hemochromatosis can lead to chronic liver disease, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Many invasive and noninvasive diagnostic tests are available to aid in diagnosis and treatment. MRI has emerged as the reference standard imaging modality for the detection and quantification of hepatic iron deposition, as ultrasound (US) is unable to detect iron overload and computed tomography (CT) findings are nonspecific and influenced by multiple confounding variables. If caught and treated early, HH disease progression can significantly be altered. Area covered: The data on Hemochromatosis, iron overload, and MRI were gathered by searching PubMed. Expert commentary: MRI is a great tool for diagnosis and management of iron overload. It is safe, effective, and a standard protocol should be included in diagnostic algorithms of future treatment guidelines.

Assessment of MR-based R2* and quantitative susceptibility mapping for the quantification of liver iron concentration in a mouse model at 7T

PURPOSE

To assess the feasibility of quantifying liver iron concentration (LIC) using R2* and quantitative susceptibility mapping (QSM) at a high field strength of 7 Tesla (T).

METHODS

Five different concentrations of Fe-dextran were injected into 12 mice to produce various degrees of liver iron overload. After mice were sacrificed, blood and liver samples were harvested. Ferritin enzyme-linked immunosorbent assay (ELISA) and inductively coupled plasma mass spectrometry were performed to quantify serum ferritin concentration and LIC. Multiecho gradient echo MRI was conducted to estimate R2* and the magnetic susceptibility of each liver sample through complex nonlinear least squares fitting and a morphology enabled dipole inversion method, respectively.

RESULTS

Average estimates of serum ferritin concentration, LIC, R2*, and susceptibility all show good linear correlations with injected Fe-dextran concentration; however, the standard deviations in the estimates of R2* and susceptibility increase with injected Fe-dextran concentration. Both R2* and susceptibility measurements also show good linear correlations with LIC (R²  = 0.78 and R²  = 0.91, respectively), and a susceptibility-to-LIC conversion factor of 0.829 ppm/(mg/g wet) is derived.

CONCLUSION

The feasibility of quantifying LIC using MR-based  R2* and QSM at a high field strength of 7T is demonstrated. Susceptibility quantification, which is an intrinsic property of tissues and benefits from being field-strength independent, is more robust than R2* quantification in this ex vivo study. A susceptibility-to-LIC conversion factor is presented that agrees relatively well with previously published QSM derived results obtained at 1.5T and 3T.

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

BACKGROUND

A challenge for R2 and R2* methods in measuring liver iron concentration (LIC) is that fibrosis, fat, and other hepatic cellular pathology contribute to R2 and R2* and interfere with LIC estimation.

PURPOSE

To examine the interfering effects of fibrosis, fat, and other lesions on R2* LIC estimation and to use quantitative susceptibility mapping (QSM) to reduce these distortions.

STUDY TYPE

Prospective.

PHANTOMS, SUBJECTS

Water phantoms with various concentrations of gadolinium (Gd), collagen (Cl, modeling fibrosis), and fat; nine healthy controls with no known hepatic disease, nine patients with known or suspected hepatic iron overload, and nine patients with focal liver lesions.

FIELD STRENGTH/SEQUENCE

The phantoms and human subjects were imaged using a 3D multiecho gradient-echo on clinical 1.5T and 3T MRI systems.

ASSESSMENT

QSM and R2* images were postprocessed from the same gradient-echo data. Fat contributions to susceptibility and R2* were corrected in signal models for LIC estimation.

STATISTICAL TESTS

Polynomial regression analyses were performed to examine relations among susceptibility, R2* and true [Gd] and [Cl] in phantoms, and among susceptibility and R2* in patient livers.

RESULTS

In phantoms, R2* had a strong nonlinear dependency on [Cl], [fat], and [Gd], while susceptibility was linearly dependent (R²  > 0.98). In patients, R2* was highly sensitive to liver pathological changes, including fat, fibrosis, and tumors, while QSM was relatively insensitive to these abnormalities (P = 0.015). With moderate iron overload, liver susceptibility and R2* were not linearly correlated over a common R2* range [0, 100] sec^-1 (P = 0.35).

DATA CONCLUSION

R2* estimation of LIC is prone to substantial nonlinear interference from fat, fibrosis, and other lesions. QSM processing of the same gradient echo MRI data can effectively minimize the effects of cellular pathology.

LEVEL OF EVIDENCE

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

Mapping the microscale origins of magnetic resonance image contrast with subcellular diamond magnetometry

Magnetic resonance imaging (MRI) is a widely used biomedical imaging modality that derives much of its contrast from microscale magnetic field patterns in tissues. However, the connection between these patterns and the appearance of macroscale MR images has not been the subject of direct experimental study due to a lack of methods to map microscopic fields in biological samples. Here, we optically probe magnetic fields in mammalian cells and tissues with submicron resolution and nanotesla sensitivity using nitrogen-vacancy diamond magnetometry, and combine these measurements with simulations of nuclear spin precession to predict the corresponding MRI contrast. We demonstrate the utility of this technology in an in vitro model of macrophage iron uptake and histological samples from a mouse model of hepatic iron overload. In addition, we follow magnetic particle endocytosis in live cells. This approach bridges a fundamental gap between an MRI voxel and its microscopic constituents.

Magnetic resonance imaging derives its contrast from local magnetic fields, however the connection between these fields and macroscale contrast has not been established through direct experiments. Here, Davis et al. use diamond magnetometry to map local magnetic fields within mammalian cells with sub-micron resolution and predict macroscale contrast.

Applications of cardiac magnetic resonance imaging in sickle cell disease

Cardiac magnetic resonance imaging (CMR) has evolved from an effective research tool to a non-invasive clinical modality with versatile applications. The accuracy of volume measurements and functional assessment and the ability to identify unique myocardial tissue characteristics non-invasively are the primary advantages of CMR. The use of CMR in sickle cell disease (SCD) has been limited clinically to myocardial iron assessment. The use of other CMR applications to characterize the cardiac pathology in SCD is slowly emerging but remains limited to research level. In this review, we discuss some of the applications of CMR in studying cardiovascular diseases and its potential uses in SCD for research and clinical purposes.

Comparison of clinical MRI liver iron content measurements using signal intensity ratios, R 2 and R 2

Purpose

To compare three types of MRI liver iron content (LIC) measurement performed in daily clinical routine in a single center over a 6-year period.

Methods

Patients undergoing LIC MRI-scans (1.5T) at our center between January 1, 2008 and December 31, 2013 were retrospectively included. LIC was measured routinely with signal intensity ratio (SIR) and MR-relaxometry (R₂ and R₂*) methods. Three observers placed regions-of-interest. The success rate was the number of correctly acquired scans over the total number of scans. Interobserver agreement was assessed with intraclass correlation coefficients (ICC) and Bland–Altman analysis, correlations between LIC_SIR, R₂, R₂*, and serum values with Spearman’s rank correlation coefficient. Diagnostic accuracies of LIC_SIR, R₂ and serum transferrin, transferrin-saturation, and ferritin compared to increased R₂* (≥44 Hz) as indicator of iron overload were assessed using ROC-analysis.

Results

LIC MRI-scans were performed in 114 subjects. SIR, R₂, and R₂* data were successfully acquired in 102/114 (89%), 71/114 (62%), and 112/114 (98%) measurements, with the lowest success rate for R₂. The ICCs of SIR, R₂, and R₂* did not differ at 0.998, 0.997, and 0.999. R₂ and serum ferritin had the highest diagnostic accuracies to detect elevated R₂* as mark of iron overload.

Conclusions

SIR and R₂* are preferable over R₂ in terms of success rates. R₂*’s shorter acquisition time and wide range of measurable LIC values favor R₂* over SIR for MRI-based LIC measurement.

Electronic supplementary material

The online version of this article (doi:10.1007/s00261-016-0831-7) contains supplementary material, which is available to authorized users.

MRI-based quantitative susceptibility mapping (QSM) and R2* mapping of liver iron overload: Comparison with SQUID-based biomagnetic liver susceptometry

PURPOSE

We aimed to determine the agreement between quantitative susceptibility mapping (QSM)-based biomagnetic liver susceptometry (BLS) and confounder-corrected R2* mapping with superconducting quantum interference device (SQUID)-based biomagnetic liver susceptometry in patients with liver iron overload.

METHODS

Data were acquired from two healthy controls and 22 patients undergoing MRI and SQUID-BLS as part of routine monitoring for iron overload. Magnetic resonance imaging was performed on a 3T system using a three-dimensional multi-echo gradient-echo acquisition. Both magnetic susceptibility and R2* of the liver were estimated from this acquisition. Linear regression was used to compare estimates of QSM-BLS and R2* to SQUID-BLS.

RESULTS

Both QSM-BLS and confounder-corrected R2* were sensitive to the presence of iron in the liver. Linear regression between QSM-BLS and SQUID-BLS demonstrated the following relationship: QSM-BLS = (-0.22 ± 0.11) + (0.49 ± 0.05) · SQUID-BLS with r²  = 0.88. The coefficient of determination between liver R2* and SQUID-BLS was also r²  = 0.88.

CONCLUSION

We determined a strong correlation between both QSM-BLS and confounder-corrected R2* to SQUID-BLS. This study demonstrates the feasibility of QSM-BLS and confounder-corrected R2* for assessing liver iron overload, particularly when SQUID systems are not accessible. Magn Reson Med 78:264-270, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

A model for hepatic fibrosis: the competing effects of cell loss and iron on shortened modified Look-Locker inversion recovery T1 (shMOLLI-T1 ) in the liver

PURPOSE

To propose a simple multicompartment model of the liver and use Bloch-McConnell simulations to demonstrate the effects of iron and fibrosis on shortened-MOLLI (shMOLLI) T₁ measurements. Liver T₁ values have shown sensitivity to inflammation and fibrosis, but are also affected by hepatic iron content. Modified Look-Locker inversion recovery (MOLLI) T₁ measurements are biased by the lower T₂ associated with high iron.

MATERIALS AND METHODS

A tissue model was generated consisting of liver cells and extracellular fluid (ECF), with iron-dependent relaxation rates. Fibrosis was imitated by increasing the ECF proportion. Simulations of the shMOLLI sequence produced a look-up table (LUT) of shMOLLI-T₁ for a given ECF fraction and iron content. The LUT was used to calculate ECF(shMOLLI-T₁ ), assuming normal hepatic iron content (HIC), and ECF(shMOLLI- T1,T2*), accounting for HIC determined by T2*, for 77 patients and compared to fibrosis assessed by liver biopsy.

RESULTS

Simulations showed that increasing HIC decreases shMOLLI-T₁ , with an increase in HIC from 1.0 to 2.5 mg/g at normal ECF fraction decreasing shMOLLI-T₁ by 160 msec, while increasing ECF increased ShMOLLI-T₁ , with an increase of 20% ECF at normal iron increasing shMOLLI-T₁ by 200 msec. Calculated patient ECF(shMOLLI-T₁ ) showed a strong dependence on Ishak score (3.3 ± 0.8 %ECF/Ishak stage) and 1/T2* (-0.23 ± 0.04 %ECF/Hz). However, when iron was accounted for to produce ECF(shMOLLI- T1,T2*), it was independent of HIC but retained sensitivity to Ishak score.

CONCLUSION

Use of this multicompartment model of the liver with Bloch-McConnell simulation should enable compensation of iron effects when using shMOLLI-T₁ to assess fibrosis.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:450-462.

A USPIO doped gel phantom for R2* relaxometry

OBJECTIVE

This work describes a phantom containing regions of controlled R2* (1/T2*) values to provide a stable reference object for testing implementations of R2* relaxometry commonly used for liver and heart iron assessments.

MATERIALS AND METHODS

A carrageenan-strengthened gadolinium DTPA doped agarose gel was used to enclose nine gels additionally doped with ultra-small superparamagnetic iron oxide. R2* values were determined at 1.5 T using multi-echo GRE sequences and exponential regression of pixel values from a region of interest against echo time using non-linear regression algorithms. We measured R2*, R2 and R1 values and the inter-scan and inter-operator reproducibility.

RESULTS

The phantom reliably demonstrated R2* values in seven steps between 22.4 s^-1 (SE 1.98) and 441.9 s^-1 (SE 6.76), with an R2* relaxivity (r2*) of 792 (SE 5.6) mM^-1 s^-1. The doped gels displayed a concentration-dependent R2' component of R2* phantom, indicating superparamagnetic enhancement effects. We observed no significant change in relaxivity (r2*) over 12 months, and estimate a useful life of 3 years. Detailed descriptions of the production process and calculators are been provided as Online Resources.

CONCLUSION

The phantom provides a durable test object with controlled R2* relaxation behaviour, useful for a range of R2* relaxometry reference work.

Quantification of liver fat in the presence of iron overload

PURPOSE

To evaluate the accuracy of R2* models (1/T₂ * = R2*) for chemical shift-encoded magnetic resonance imaging (CSE-MRI)-based proton density fat-fraction (PDFF) quantification in patients with fatty liver and iron overload, using MR spectroscopy (MRS) as the reference standard.

MATERIALS AND METHODS

Two Monte Carlo simulations were implemented to compare the root-mean-squared-error (RMSE) performance of single-R2* and dual-R2* correction in a theoretical liver environment with high iron. Fatty liver was defined as hepatic PDFF >5.6% based on MRS; only subjects with fatty liver were considered for analyses involving fat. From a group of 40 patients with known/suspected iron overload, nine patients were identified at 1.5T, and 13 at 3.0T with fatty liver. MRS linewidth measurements were used to estimate R2* values for water and fat peaks. PDFF was measured from CSE-MRI data using single-R2* and dual-R2* correction with magnitude and complex fitting.

RESULTS

Spectroscopy-based R2* analysis demonstrated that the R2* of water and fat remain close in value, both increasing as iron overload increases: linear regression between R2*_W and R2*_F resulted in slope = 0.95 [0.79-1.12] (95% limits of agreement) at 1.5T and slope = 0.76 [0.49-1.03] at 3.0T. MRI-PDFF using dual-R2* correction had severe artifacts. MRI-PDFF using single-R2* correction had good agreement with MRS-PDFF: Bland-Altman analysis resulted in -0.7% (bias) ± 2.9% (95% limits of agreement) for magnitude-fit and -1.3% ± 4.3% for complex-fit at 1.5T, and -1.5% ± 8.4% for magnitude-fit and -2.2% ± 9.6% for complex-fit at 3.0T.

CONCLUSION

Single-R2* modeling enables accurate PDFF quantification, even in patients with iron overload.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:428-439.

Pierre T, Heeney MM, Schultz WH, Davis BR, Ware RE. Liver iron concentration measurements by MRI in chronically transfused children with sickle cell anemia: baseline results from the TWiTCH trial

Noninvasive, quantitative, and accurate assessment of liver iron concentration (LIC) by MRI is useful for patients receiving transfusions, but R2 and R2* MRI techniques have not been systematically compared in sickle cell anemia (SCA). We report baseline LIC results from the TWiTCH trial, which compares hydroxyurea with blood transfusion treatment for primary stroke prophylaxis assessed by transcranial Doppler sonography in pediatric SCA patients. Liver R2 was collected and processed using a FDA-approved commercial process (FerriScan®), while liver R2* quality control and processing were performed by a Core Laboratory blinded to clinical site and patient data. Baseline LIC studies using both MRI techniques were available for 120 participants. LICR2* and LICR2 results were highly correlated (r(2)  = 0.93). A proportional bias of LIC(R2*)/LIC(R2), decreasing with average LIC, was observed. Systematic differences between LICR2* and LICR2 were also observed by MRI manufacturer. Importantly, LICR2* and LICR2 estimates had broad 95% limits of agreement with respect to each other. We recommend LICR2 and LICR2* not be used interchangeably in SCA patients to follow individual patient trends in iron burden.

Does fat suppression via chemically selective saturation affect R2*-MRI for transfusional iron overload assessment? A clinical evaluation at 1.5T and 3T

PURPOSE

Fat suppression (FS) via chemically selective saturation (CHESS) eliminates fat-water oscillations in multiecho gradient echo (mGRE) R2*-MRI. However, for increasing R2* values as seen with increasing liver iron content (LIC), the water signal spectrally overlaps with the CHESS band, which may alter R2*. We investigated the effect of CHESS on R2* and developed a heuristic correction for the observed CHESS-induced R2* changes.

METHODS

Eighty patients [female, n = 49; male, n = 31; mean age (± standard deviation), 18.3 ± 11.7 y] with iron overload were scanned with a non-FS and a CHESS-FS mGRE sequence at 1.5T and 3T. Mean liver R2* values were evaluated using three published fitting approaches. Measured and model-corrected R2* values were compared and statistically analyzed.

RESULTS

At 1.5T, CHESS led to a systematic R2* reduction (P < 0.001 for all fitting algorithms) especially toward higher R2*. Our model described the observed changes well and reduced the CHESS-induced R2* bias after correction (linear regression slopes: 1.032/0.927/0.981). No CHESS-induced R2* reductions were found at 3T.

CONCLUSION

The CHESS-induced R2* bias at 1.5T needs to be considered when applying R2*-LIC biopsy calibrations for clinical LIC assessment, which were established without FS at 1.5T. The proposed model corrects the R2* bias and could therefore improve clinical iron overload assessment based on linear R2*-LIC calibrations. Magn Reson Med 76:591-601, 2016. © 2015 Wiley Periodicals, Inc.

Measurement of liver fat fraction and iron with MRI and MR spectroscopy techniques

Diffuse liver disease is a widespread global healthcare burden, and the abnormal accumulation of lipid and/or iron is common to important disease processes. Developing the improved methods for detecting and quantifying liver lipid and iron is an important clinical need. The inherent risk, invasiveness, and sampling error of liver biopsy have prompted the development of noninvasive imaging methods for lipid and iron assessment. Ultrasonography and computed tomography have the ability to detect diffuse liver disease, but with limited accuracy. The purpose of this review is to describe the current state-of-the-art methods for quantifying liver lipid and iron using magnetic resonance imaging and spectroscopy, including their implementation, benefits, and potential pitfalls. Imaging- and spectroscopy-based methods are naturally suited for lipid and iron quantification. Lipid can be detected and decomposed from the inherent chemical shift between lipid and water signals, whereas iron imparts significant paramagnetic susceptibility to tissue, which accelerates proton relaxation. However, measurements of these biomarkers are confounded by technical and biological effects. Current methods must address these factors to allow a precise correlation between the lipid fraction and iron concentration. Although this correlation becomes increasingly challenging in the presence of combined lipid and iron accumulation, advanced techniques show promise for delineating these quantities through multi-lipid peak analysis, T2 water mapping, and fast single-voxel water-lipid spectroscopy.

Estimating tissue iron burden: current status and future prospects

Iron overload is becoming an increasing problem as haemoglobinopathy patients gain greater access to good medical care and as therapies for myelodysplastic syndromes improve. Therapeutic options for iron chelation therapy have increased and many patients now receive combination therapies. However, optimal utilization of iron chelation therapy requires knowledge not only of the total body iron burden but the relative iron distribution among the different organs. The physiological basis for extrahepatic iron deposition is presented in order to help identify patients at highest risk for cardiac and endocrine complications. This manuscript reviews the current state of the art for monitoring global iron overload status as well as its compartmentalization. Plasma markers, computerized tomography, liver biopsy, magnetic susceptibility devices and magnetic resonance imaging (MRI) techniques are all discussed but MRI has come to dominate clinical practice. The potential impact of recent pancreatic and pituitary MRI studies on clinical practice are discussed as well as other works-in-progress. Clinical protocols are derived from experience in haemoglobinopathies but may provide useful guiding principles for other iron overload disorders, such as myelodysplastic syndromes.

Cobalt ferrite nanowhiskers as T2 MRI contrast agent

A novel, one-step synthesis of one-dimensional cobalt ferrite nanowhiskers (CfW) is reported.

Guidelines for quantifying iron overload

Both primary and secondary iron overload are increasingly prevalent in the United States because of immigration from the Far East, increasing transfusion therapy in sickle cell disease, and improved survivorship of hematologic malignancies. This chapter describes the use of historical data, serological measures, and MRI to estimate somatic iron burden. Before chelation therapy, transfusional volume is an accurate method for estimating liver iron burden, whereas transferrin saturation reflects the risk of extrahepatic iron deposition. In chronically transfused patients, trends in serum ferritin are helpful, inexpensive guides to relative changes in somatic iron stores. However, intersubject variability is quite high and ferritin values may change disparately from trends in total body iron load over periods of several years. Liver biopsy was once used to anchor trends in serum ferritin, but it is invasive and plagued by sampling variability. As a result, we recommend annual liver iron concentration measurements by MRI for all patients on chronic transfusion therapy. Furthermore, it is important to measure cardiac T2* by MRI every 6-24 months depending on the clinical risk of cardiac iron deposition. Recent validation data for pancreas and pituitary iron assessments are also presented, but further confirmatory data are suggested before these techniques can be recommended for routine clinical use.

The Role of Magnetic Resonance Imaging in the Evaluation of Thalassemic Syndromes: Current Practice and Future Perspectives

Iron can be deposited in all internal organs, leading to different types of functional abnormalities. However, myocardial iron overload that contributes to heart failure remains one of the main causes of death in thalassemia major. Using magnetic resonance imaging, tissue iron is detected indirectly by the effects on relaxation times of ferritin and hemosiderin iron interacting with hydrogen nuclei. The presence of iron in the human body results in marked alterations of tissue relaxation times. Currently, cardiovascular magnetic resonance using T2* is routinely used in many countries to identify patients with myocardial iron loading and guide chelation therapy, specifically tailored to the heart. Myocardial T2* is the only clinically validated non-invasive measure of myocardial iron loading and is superior to surrogates such as serum ferritin, liver iron, ventricular ejection fraction and tissue Doppler parameters. Finally, the substantial amelioration of patients’ survival, allows the detection of other organs’ abnormalities due to iron overload, apart from the heart, missed in the past. Recent studies revealed that iron deposition has a different pattern in various parenchymal organs, which is independent from serum ferritin and follows an individual way after chelation treatment application. This new upcoming reality orders a closer monitoring of all organs of the body in order to detect preclinical lesions and early apply adequate treatment.

Relaxivity-iron calibration in hepatic iron overload: Predictions of a Monte Carlo model

PURPOSE

R2* (1/T2*) and single echo R2 (1/T2) have been calibrated to liver iron concentration (LIC) in patients with thalassemia and transfusion-dependent sickle cell disease at 1.5T. The R2*-LIC relationship is linear, whereas that of R2 is curvilinear. However, the increasing popularity of high-field scanners requires generalizing these relationships to higher field strengths. In this study, we tested the hypothesis that numerical simulation can accurately determine the field dependence of iron-mediated transverse relaxation rates.

METHODS

We previously replicated the calibration curves between R2 and R2* and iron at 1.5T using Monte Carlo models incorporating realistic liver structure, iron deposit susceptibility, and proton mobility. In this paper, we extend our model to predict relaxivity-iron calibrations at higher field strengths. Predictions were validated by measuring R2 and R2* at 1.5T and 3T in six β-thalassemia major patients.

RESULTS

Predicted R2* increased twofold at 3T from 1.5T, whereas R2 increased by a factor of 1.47. Patient data exhibited a coefficient of variation of 3.6% and 7.2%, respectively, to the best-fit simulated data. Simulations over the range 0.25T-7T showed R2* increasing linearly with field strength, whereas R2 exhibited a concave-downward relationship.

CONCLUSION

A model-based approach predicts alterations in relaxivity-iron calibrations with field strength without repeating imaging studies. The model may generalize to alternative pulse sequences and tissue iron distribution.

Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload

PURPOSE

The purpose of this work was to develop and demonstrate feasibility and initial clinical validation of quantitative susceptibility mapping (QSM) in the abdomen as an imaging biomarker of hepatic iron overload.

THEORY AND METHODS

In general, QSM is faced with the challenges of background field removal and dipole inversion. Respiratory motion, the presence of fat, and severe iron overload further complicate QSM in the abdomen. We propose a technique for QSM in the abdomen that addresses these challenges. Data were acquired from 10 subjects without hepatic iron overload and 33 subjects with known or suspected iron overload. The proposed technique was used to estimate the susceptibility map in the abdomen, from which hepatic iron overload was measured. As a reference, spin-echo data were acquired for R2-based LIC estimation. Liver R2* was measured for correlation with liver susceptibility estimates.

RESULTS

Correlation between susceptibility and R2-based LIC estimation was R(2) = 0.76 at 1.5 Tesla (T) and R(2) = 0.83 at 3T. Furthermore, high correlation between liver susceptibility and liver R2* (R(2) = 0.94 at 1.5T; R(2) = 0.93 at 3T) was observed.

CONCLUSION

We have developed and demonstrated initial validation of QSM in the abdomen as an imaging biomarker of hepatic iron overload.

Use of magnetic resonance imaging to monitor iron overload

Treatment of iron overload requires robust estimates of total-body iron burden and its response to iron chelation therapy. Compliance with chelation therapy varies considerably among patients, and individual reporting is notoriously unreliable. Even with perfect compliance, intersubject variability in chelator effectiveness is extremely high, necessitating reliable iron estimates to guide dose titration. In addition, each chelator has a unique profile with respect to clearing iron stores from different organs. This article presents the tools available to clinicians to monitor their patients, focusing on noninvasive magnetic resonance imaging methods because they have become the de facto standard of care.