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

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients

PURPOSE

To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver.

METHODS

A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots.

RESULTS

Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets.

CONCLUSIONS

The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

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.

Cardiac MRI at Low Field Strengths

Cardiac MR imaging is well established for assessment of cardiovascular structure and function, myocardial scar, quantitative flow, parametric mapping, and myocardial perfusion. Despite the clear evidence supporting the use of cardiac MRI for a wide range of indications, it is underutilized clinically. Recent developments in low-field MRI technology, including modern data acquisition and image reconstruction methods, are enabling high-quality low-field imaging that may improve the cost-benefit ratio for cardiac MRI. Studies to-date confirm that low-field MRI offers high measurement concordance and consistent interpretation with clinical imaging for several routine sequences. Moreover, low-field MRI may enable specific new clinical opportunities for cardiac imaging such as imaging near metal implants, MRI-guided interventions, combined cardiopulmonary assessment, and imaging of patients with severe obesity. In this review, we discuss the recent progress in low-field cardiac MRI with a focus on technical developments and early clinical validation studies. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

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.

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.

Characterization and correction of the effects of hepatic iron on T1ρ relaxation in the liver at 3.0T

PURPOSE

Quantitative T_1ρ imaging is an emerging technique to assess the biochemical properties of tissues. In this paper, we report our observation that liver iron content (LIC) affects T_1ρ quantification of the liver at 3.0T field strength and develop a method to correct the effect of LIC.

THEORY AND METHODS

On-resonance R_1ρ (1/T_1ρ ) is mainly affected by the intrinsic R₂ (1/T₂ ), which is influenced by LIC. As on-resonance R_1ρ is closely related to the Carr-Purcell-Meiboom-Gill (CPMG) R₂ , and because the calibration between CPMG R₂ and LIC has been reported at 1.5T, a correction method was proposed to correct the R₂ contribution to the R_1ρ . The correction coefficient was obtained from the calibration results and related transformed factors. To compensate for the difference between CPMG R₂ and R_1ρ , a scaling factor was determined using the values of CPMG R₂ and R_1ρ , obtained simultaneously from a single breath-hold from volunteers. The livers of 110 subjects were scanned to validate the correction method.

RESULTS

LIC was significantly correlated with R_1ρ in the liver. However, when the proposed correction method was applied to R_1ρ , LIC and the iron-corrected R_1ρ were not significantly correlated.

CONCLUSION

LIC can affect T_1ρ in the liver. We developed an iron-correction method for the quantification of T_1ρ in the liver at 3.0T.

Magnetic susceptibility and R2* of myocardial reperfusion injury at 3T and 7T

PURPOSE

Magnetic susceptibility (Δχ) alterations have shown association with myocardial infarction (MI) iron deposition, yet there remains limited understanding of the relationship between relaxation rates and susceptibility or the effect of magnetic field strength. Hence, Δχ and R 2 ∗ in MI were compared at 3T and 7T.

METHODS

Subacute MI was induced by coronary artery ligation in male Yorkshire swine. 3D multiecho gradient echo imaging was performed at 1-week postinfarction at 3T and 7T. Quantitative susceptibility mapping images were reconstructed using a morphology-enabled dipole inversion. R 2 ∗ maps and quantitative susceptibility mapping were generated to assess the relationship between R 2 ∗ , Δχ, and field strength. Infarct histopathology was investigated.

RESULTS

Magnetic susceptibility was not significantly different across field strengths (7T: 126.8 ± 41.7 ppb; 3T: 110.2 ± 21.0 ppb, P = NS), unlike R 2 ∗ (7T: 247.0 ± 14.8 Hz; 3T: 106.1 ± 6.5 Hz, P < .001). Additionally, infarct Δχ and R 2 ∗ were significantly higher than remote myocardium. Magnetic susceptibility at 7T versus 3T had a significant association (β = 1.02, R2 = 0.82, P < .001), as did R 2 ∗ (β = 2.35, R2 = 0.98, P < .001). Infarct pathophysiology and iron deposition were detected through histology and compared with imaging findings.

CONCLUSION

R 2 ∗ showed dependence and Δχ showed independence of field strength. Histology validated the presence of iron and supported imaging findings.

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.

Spectroscopy-based multi-parametric quantification in subjects with liver iron overload at 1.5T and 3T

PURPOSE

To evaluate the precision profile (repeatability and reproducibility) of quantitative STEAM-MRS and to determine the relationships between multiple MR biomarkers of chronic liver disease in subjects with iron overload at both 1.5 Tesla (T) and 3T.

METHODS

MRS data were acquired in patients with known or suspected liver iron overload. Two STEAM-MRS sequences (multi-TE and multi-TE-TR) were acquired at both 1.5T and 3T (same day), including test-retest acquisition. Each acquisition enabled estimation of R1, R2, and FWHM (each separately for water and fat); and proton density fat fraction. The test-retest repeatability and reproducibility across acquisition modes (multi-TE vs. multi-TE-TR) of the estimates were evaluated using intraclass correlation coefficients, linear regression, and Bland-Altman analyses. Multi-parametric relationships between parameters at each field strength, across field strengths, and with liver iron concentration were also evaluated using linear and nonlinear regression.

RESULTS

Fifty-six (n = 56) subjects (10 to 73 years, 37 males/19 females) were successfully recruited. Both STEAM-MRS sequences demonstrated good-to-excellent precision (intraclass correlation coefficient ≥ 0.81) for the quantification of R1_water , R2_water , FWHM_water , and proton density fat fraction at both 1.5T and 3T. Additionally, several moderate (R² = 0.50 to 0.69) to high (R² ≥ 0.70) correlations were observed between biomarkers, across field strengths, and with liver iron concentration.

CONCLUSIONS

Over a broad range of liver iron concentration, STEAM-MRS enables rapid and precise measurement of multiple biomarkers of chronic liver disease. By evaluating the multi-parametric relationships between biomarkers, this work may advance the comprehensive MRS-based assessment of chronic liver disease and may help establish biomarkers of chronic liver disease.

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.

Complex confounder-corrected R2* mapping for liver iron quantification with MRI

OBJECTIVES

MRI-based R2* mapping may enable reliable and rapid quantification of liver iron concentration (LIC). However, the performance and reproducibility of R2* across acquisition protocols remain unknown. Therefore, the objective of this work was to evaluate the performance and reproducibility of complex confounder-corrected R2* across acquisition protocols, at both 1.5 T and 3.0 T.

METHODS

In this prospective study, 40 patients with suspected iron overload and 10 healthy controls were recruited with IRB approval and informed written consent and imaged at both 1.5 T and 3.0 T. For each subject, acquisitions included four different R2* mapping protocols at each field strength, and an FDA-approved R2-based method performed at 1.5 T as a reference for LIC. R2* maps were reconstructed from the complex data acquisitions including correction for noise effects and fat signal. For each subject, field strength, and R2* acquisition, R2* measurements were performed in each of the nine liver Couinaud segments and the spleen. R2* measurements were compared across protocols and field strength (1.5 T and 3.0 T), and R2* was calibrated to LIC for each acquisition and field strength.

RESULTS

R2* demonstrated high reproducibility across acquisition protocols (p > 0.05 for 96/108 pairwise comparisons across 2 field strengths and 9 liver segments, ICC > 0.91 for each field strength/segment combination) and high predictive ability (AUC > 0.95 for four clinically relevant LIC thresholds). Calibration of R2* to LIC was LIC = - 0.04 + 2.62 × 10^-2 R2* at 1.5 T and LIC = 0.00 + 1.41 × 10^-2 R2* at 3.0 T.

CONCLUSIONS

Complex confounder-corrected R2* mapping enables LIC quantification with high reproducibility across acquisition protocols, at both 1.5 T and 3.0 T.

KEY POINTS

• Confounder-corrected R2* of the liver provides reproducible R2* across acquisition protocols, including different spatial resolutions, echo times, and slice orientations, at both 1.5 T and 3.0 T. • For all acquisition protocols, high correlation with R2-based liver iron concentration (LIC) quantification was observed. • The calibration between confounder-corrected R2* and LIC, at both 1.5 T and 3.0 T, is determined in this study.

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.

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.

Improved Liver R2* Mapping by Averaging Decay Curves

Liver R2* mapping is often degraded by the low signal-to-noise ratio (SNR) especially in the presence of severe iron. This study aims to improve liver R2* mapping at low SNRs by averaging decay curves before the process of curve-fitting. Independently filtering echo images by nonlocal means (NLM) demonstrated improved quality of R2* mapping, but may introduce new errors due to the nonlinear nature of the NLM filter, during which the averaging weights may vary with different image contents at multiple echo times. In addition, the image denoising effect of the NLM may decline when no sufficient similar patches are available. To overcome these drawbacks, we proposed to filter decay curves instead of images. In this novel scheme, decay curves were averaged in a local window, each with a weight assigned according to the curve-similarity measured by the distance between one of the neighboring curves and the targeted one. The proposed method was tested on simulated, phantom and patient data. The results demonstrate that the proposed method can provide more accurate R2* mapping compared with the NLM algorithm, and hence has the potential to improve diagnosis and therapy in patients with liver iron.

Ultra-short echo time images quantify high liver iron

PURPOSE

1.5T gradient echo-based R2∗ estimates are standard-of-care for assessing liver iron concentration (LIC). Despite growing popularity of 3T, echo time (TE) limitations prevent 3T liver iron quantitation in the upper half of the clinical range (LIC ⪆20 mg/g). In this work, a 3D radial pulse sequence was assessed to double the dynamic range of 3T LIC estimates.

THEORY AND METHODS

The minimum TE limits the dynamic range of pulse sequences to estimate R2∗. 23 chronically-transfused human volunteers were imaged with 1.5T Cartesian gradient echo (1.5T-GRE), 3T Cartesian gradient echo (3T-GRE), and 3T ultrashort TE radial (3T-UTE) pulse sequences; minimum TEs were 0.96, 0.76, and 0.19 ms, respectively. R2∗ was estimated with an exponential signal model, normalized to 1.5T equivalents, and converted to LIC. Bland-Altman analysis compared 3T-based estimates to 1.5T-GRE.

RESULTS

LIC by 3T-GRE was unbiased versus 1.5T-GRE for LIC ≤ 25 mg/g (sd = 9.6%); 3T-GRE failed to quantify LIC > 25 mg/g. At high iron loads, 3T-UTE was unbiased (sd = 14.5%) compared to 1.5T-GRE. Further, 3T-UTE estimated LIC up to 50 mg/g, exceeding 1.5T-GRE limits.

CONCLUSION

3T-UTE imaging can reliably estimate high liver iron burdens. In conjunction with 3T-GRE, 3T-UTE allows clinical LIC estimation across a wide range of liver iron loads. Magn Reson Med 79:1579-1585, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Assessment of liver iron overload by 3 T MRI

PURPOSE

To evaluate the performance and limitations of the signal intensity ratio method for quantifying liver iron overload at 3 T.

METHODS

Institutional review board approval and written informed consent from all participants were obtained. One hundred and five patients were included prospectively. All patients underwent a liver biopsy with biochemical assessment of hepatic iron concentration and a 3 T MRI scan with 5 breath-hold single-echo gradient-echo sequences. Linear correlation between liver-to-muscle signal intensity ratio and liver iron concentration was calculated. The algorithm for calculating magnetic resonance hepatic iron concentration was adapted from the method described by Gandon et al. with echo times divided by 2. Sensitivity and specificity were calculated.

RESULTS

Five patients were excluded (coil selection failure or missing sequence) and 100 patients were analyzed, 64 men and 36 women, 52 ± 13.3 years old, with a biochemical hepatic iron concentration range of 0-630 µmol/g. Linear correlation between biochemical hepatic iron concentration and MR-hepatic iron concentration was excellent with a correlation coefficient = 0.96, p < 0.0001. Sensitivity and specificity were, respectively, 83% (0.70-0.92) and 96% (0.85-0.99), with a pathological threshold of 36 µmol/g.

CONCLUSION

Signal intensity ratio method for quantifying liver iron overload can be used at 3 T with echo times divided by 2.

Quantitative ultrashort echo time imaging for assessment of massive iron overload at 1.5 and 3 Tesla

PURPOSE

Hepatic iron content (HIC) quantification via transverse relaxation rate (R2*)-MRI using multi-gradient echo (mGRE) imaging is compromised toward high HIC or at higher fields due to the rapid signal decay. Our study aims at presenting an optimized 2D ultrashort echo time (UTE) sequence for R2* quantification to overcome these limitations.

METHODS

Two-dimensional UTE imaging was realized via half-pulse excitation and radial center-out sampling. The sequence includes chemically selective saturation pulses to reduce streaking artifacts from subcutaneous fat, and spatial saturation (sSAT) bands to suppress out-of-slice signals. The sequence employs interleaved multi-echo readout trains to achieve dense temporal sampling of rapid signal decays. Evaluation was done at 1.5 Tesla (T) and 3T in phantoms, and clinical applicability was demonstrated in five patients with biopsy-confirmed massively high HIC levels (>25 mg Fe/g dry weight liver tissue).

RESULTS

In phantoms, the sSAT pulses were found to remove out-of-slice contamination, and R2* results were in excellent agreement to reference mGRE R2* results (slope of linear regression: 1.02/1.00 for 1.5/3T). UTE-based R2* quantification in patients with massive iron overload proved successful at both field strengths and was consistent with biopsy HIC values.

CONCLUSION

The UTE sequence provides a means to measure R2* in patients with massive iron overload, both at 1.5T and 3T. Magn Reson Med 78:1839-1851, 2017. © 2017 Wiley Periodicals, Inc.

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.

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.