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

Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms

Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.

Intra-examination agreement between multi-echo gradient echo and confounder-corrected chemical shift-encoded MR sequences for R2* estimation as a biomarker of liver iron content in patients with a wide range of T2*/R2* and proton density fat fraction values

PURPOSE

To investigate the intra-examination agreement between multi-echo gradient echo (MEGE) and confounder-corrected chemical shift-encoded (CSE) sequences for liver T2*/R2* estimations in a wide range of T2*/R2* and proton density fat fraction (PDFF) values. Exploratorily, to search for the T2*/R2* value where the agreement line breaks and examine differences between regions of low and high agreement.

METHODS

Consecutive patients at risk for liver iron overload who underwent MEGE and CSE sequences within the same exam at 1.5 T were retrospectively selected. Regions of interest were drawn in the right and one in the left liver lobes on post-processed images for R2*(sec-1) and PDFF (%) estimation. Agreement between MEGE-R2* and CSE-R2* was evaluated using intra-class correlation coefficient (ICC) and Bland-Altman analysis. 95% confidence intervals (CI) were computed. Segment-and-regression analysis was performed to find the point where the agreement between sequences is interrupted. Regions of low and high agreement were examined using tree-based partitioning analyses.

RESULTS

49 patients were included. Mean MEGE-R2* was 94.2 s-1 (range: 31.0-737.1) and mean CSE-R2* 87.7 (29.7-748.1). Mean CSE-PDFF was 9.12% (0.1-43.3). Agreement was strong for R2* estimations (ICC: 0.992,95%CI 0.987,0.996), but the relation was nonlinear and possibly heteroskedastic. Lower agreement occurred when MEGE-R2* > 235 s-1, with MEGE-R2* values consistently lower than CSE-R2*. Higher agreement was observed when PDFF < 14%.

CONCLUSION

MEGE-R2* and CSE-R2* strongly agree, though at higher iron content, MEGE-R2* is consistently lower than CSE-R2*. In this preliminary dataset, a breaking point for agreement was found at R2* > 235. Lower agreement was observed in patients with moderate to severe liver steatosis.

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.

Hepatic Iron Quantification Using a Free-Breathing 3D Radial Gradient Echo Technique and Validation With a 2D Biopsy-Calibrated R2* Relaxometry Method

BACKGROUND

Hepatic iron content (HIC) is an important parameter for the management of iron overload. Non-invasive HIC assessment is often performed using biopsy-calibrated two-dimensional breath-hold Cartesian gradient echo (2D BH GRE) R2* -MRI. However, breath-holding is not possible in most pediatric patients or those with respiratory problems, and three-dimensional free-breathing radial GRE (3D FB rGRE) has emerged as a viable alternative.

PURPOSE

To evaluate the performance of a 3D FB rGRE and validate its R2* and fat fraction (FF) quantification with 3D breath-hold Cartesian GRE (3D BH cGRE) and biopsy-calibrated 2D BH GRE across a wide range of HICs.

STUDY TYPE

Retrospective.

SUBJECTS

Twenty-nine patients with hepatic iron overload (22 females, median age: 15 [5-25] years).

FIELD STRENGTH/SEQUENCE

Three-dimensional radial and 2D and 3D Cartesian multi-echo GRE at 1.5 T.

ASSESSMENT

R2* and FF maps were computed for 3D GREs using a multi-spectral fat model and 2D GRE R2* maps were calculated using a mono-exponential model. Mean R2* and FF values were calculated via whole-liver contouring and T2* -thresholding by three operators.

STATISTICAL TESTS

Inter- and intra-observer reproducibility was assessed using Bland-Altman and intraclass correlation coefficient (ICC). Linear regression and Bland-Altman analysis were performed to compare R2* and FF values among the three acquisitions. One-way repeated-measures ANOVA and Wilcoxon signed-rank tests, respectively, were used to test for significant differences between R2* and FF values obtained with different acquisitions. Statistical significance was assumed at P < 0.05.

RESULTS

The mean biases and ICC for inter- and intra-observer reproducibility were close to 0% and >0.99, respectively for both R2* and FF. The 3D FB rGRE R2* and FF values were not significantly different (P > 0.44) and highly correlated (R2  ≥ 0.98) with breath-hold Cartesian GREs, with mean biases ≤ ±2.5% and slopes 0.90-1.12. In non-breath-holding patients, Cartesian GREs showed motion artifacts, whereas 3D FB rGRE exhibited only minimal streaking artifacts.

DATA CONCLUSION

Free-breathing 3D radial GRE is a viable alternative in non-breath-hold patients for accurate HIC estimation.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

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.

Quantification of pancreatic iron overload and fat infiltration and their correlation with glucose disturbance in pediatric thalassemia major patients

Background

Diabetes mellitus affects more than a quarter of patients with thalassemia major (TM) worldwide, and increases the risk for cardiac complications, contributing to significant morbidity. Pancreatic iron overload (IO) and fat infiltration have been correlated with this endocrinal complication in adult TM patients. It has been shown that in adult TM patients, iron accumulation and fat infiltration are found to be heterogeneous in the pancreatic head, body, and tail region. R2* and a fat fraction (FF) generated by gradient-echo imaging can be used as quantitative parameters to assess the iron and fat contents of the pancreas. This study aimed to determine the pattern of pancreatic iron accumulation and fat infiltration in pediatric TM patients with gradient-echo imaging and evaluate the association between pancreatic IO and fat infiltration and glucose disturbances.

Methods

A total of 90 children with TM (10.7±3.1 years) were included. All patients underwent pancreatic magnetic resonance imaging (MRI) using multi-echo gradient-echo sequences. IO was measured by R2* relaxometry in 90 patients, and FF values were measured using iterative decomposition of water and fat with echo asymmetry and the least-squares estimation (IDEAL) method in 40 patients. R2* and FF were assessed in the pancreatic head, body, and tail. The global R2* and global FF values were obtained by averaging the respective values from the pancreatic head, body, and tail. The correlations between global R2*, global FF, and fasting glucose were determined using Spearman's correlation analysis. The Friedman test was used to compare R2* and FF among different pancreatic regions. Receiver operating characteristic (ROC) analysis was used to determine the performance of global R2* and global FF in discriminating impaired fasting glucose from normal fasting glucose patients.

Results

The global R2* was positively correlated with the global FF in the pancreas (r=0.895, P<0.001). No significant differences were found in R2* among the 3 regions of the pancreas (χ²=4.050, P=0.132), but significant differences were found in FF among the 3 pancreatic regions (χ²=16.350, P<0.001). Both global pancreatic R2* (r=0.408, P<0.001) and global FF (r=0.523, P=0.001) were positively correlated with fasting glucose. ROC analysis showed that global pancreatic R2* and global FF had an area under the curve of 0.769 and 0.931 (both P<0.001), respectively, in discriminating between impaired and normal glucose function patients.

Conclusions

Pediatric TM patients can have homogeneous iron siderosis and heterogeneous fat infiltration in the pancreas as measured by gradient-echo imaging, both of which are risk factors for diabetes.

Evaluation of liver iron overload with R2* relaxometry with versus without fat suppression: both are clinically accurate but there are differences

OBJECTIVES

To assess clinically relevant difference in hepatic iron quantification using R2* relaxometry with (FS) and without (non-FS) fat saturation for the evaluation of patients with suspected hepatic iron overload.

METHODS

We prospectively enrolled 134 patients who underwent 1.5-T MRI R2* relaxometry with FS and non-FS gradient echo sequences (12 echoes, initial TE = 0.99 ms). Proton density fat fraction for the quantification of steatosis was assessed. Linear regression analyses and Bland-Altman plots including Lin's concordance correlation coefficient were performed for correlation of FS R2* with non-FS R2*. Patients were grouped into 4 severity classes of iron overload (EASL based), and agreement was evaluated by contingency tables and the proportion of overall agreement.

RESULTS

A total of 41.8% of patients showed hepatic iron overload; 67.9% had concomitant steatosis; and 58.2% revealed no iron overload of whom 60.3% had steatosis. The mean R2* value for all FS data was 102.86 1/s, for non-FS 108.16 1/s. Linear regression resulted in an R-squared value of 0.99 (p < 0.001); Bland-Altman plot showed a mean R2* difference of 5.26 1/s (SD 17.82). The concordance correlation coefficient was only slightly lower for patients with steatosis compared with non-steatosis (0.988 vs. 0.993). The overall agreement between FS and non-FS R2* measurements was 94.8% using either method to classify patients according to severity of iron storage. No correlation between R2* and proton density fat fraction was found for both methods.

CONCLUSION

R2* relaxometry showed an excellent overall agreement between FS and non-FS acquisition. Both variants can therefore be used in daily routine. However, clinically relevant differences might result when switching between the two methods or during patient follow-up, when fat content changes over time. We therefore recommend choosing a method and keeping it straight in the context of follow-up examinations.

KEY POINTS

• Both variants of R2* relaxometry (FS and non-FS) may be used in daily routine. • Clinically relevant differences might result when switching between the two methods or during patient follow-up, when fat content changes over time. • It seems advisable choosing one method and keeping it straight in the context of follow-up examinations.

GSTM1 and Liver Iron Content in Children with Sickle Cell Anemia and Iron Overload

Chronic blood transfusions in patients with sickle cell anemia (SCA) cause iron overload, which occurs with a degree of interpatient variability in serum ferritin and liver iron content (LIC). Reasons for this variability are unclear and may be influenced by genes that regulate iron metabolism. We evaluated the association of the copy number of the glutathione S-transferase M1 (GSTM1) gene and degree of iron overload among patients with SCA. We compared LIC in 38 children with SCA and ≥12 lifetime erythrocyte transfusions stratified by GSTM1 genotype. Baseline LIC was measured using magnetic resonance imaging (MRI), R2*MRI within 3 months prior to, and again after, starting iron unloading therapy. After controlling for weight-corrected transfusion burden (mL/kg) and splenectomy, mean pre-chelation LIC (mg/g dry liver dry weight) was similar in all groups: GSTM1 wild-type (WT) (11.45, SD±6.8), heterozygous (8.2, SD±4.52), and homozygous GSTM1 deletion (GSTM1-null; 7.8, SD±6.9, p = 0.09). However, after >12 months of chelation, GSTM1-null genotype subjects had the least decrease in LIC compared to non-null genotype subjects (mean LIC change for GSTM1-null = 0.1 (SD±3.3); versus -0.3 (SD±3.0) and -1.9 (SD±4.9) mg/g liver dry weight for heterozygous and WT, respectively, p = 0.047). GSTM1 homozygous deletion may prevent effective chelation in children with SCA and iron overload.

Practical guide to quantification of hepatic iron with MRI

Abstract

Our intention is to demystify the MR quantification of hepatic iron (i.e., the liver iron concentration) and give you a step-by-step approach by answering the most pertinent questions. The following article should be more of a manual or guide for every radiologist than a classic review article, which just summarizes the literature. Furthermore, we provide important background information for professional communication with clinicians. The information regarding the physical background is reduced to a minimum. After reading this article, you should be able to perform adequate MR measurements of the LIC with 1.5-T or 3.0-T scanners.

Key Points

• MRI is widely accepted as the primary approach to non-invasively determine liver iron concentration (LIC).

• This article is a guide for every radiologist to perform adequate MR measurements of the LIC.

• When using R2* relaxometry, some points have to be considered to obtain correct measurements—all explained in this article.

Evaluation of hepatic iron concentration heterogeneities using the MRI R2* mapping method

OBJECTIVE

To measure hepatic iron concentration (HIC) heterogeneities using a magnetic resonance R2* mapping method.

PATIENTS AND METHODS

Ninety-four patients with suspected hepatic iron overload and 10 volunteers were included prospectively. A multi-echo R2* sequence with fat saturation and with three post-processing fitting methods (a single exponential decay model with or without truncation, SED and SEDt, and a constant offset model, COS) was compared to a signal intensity ratio method (SIR), considered as the reference. HIC heterogeneity was evaluated from R2* mapping after placing a ROI on each liver segment.

RESULTS

A strong linear correlation between SIR and R2* methods using the SEDt and COS models was observed (r = 0.973 and 0.955, respectively). Volunteers and patient liver variabilities, quantified by mean intra-liver standard deviation (SD) were 1.58 μmol/g (mean range 5.06 μmol/g) and 4.73 μmol/g (mean range 19.08 μmol/g), respectively. For the patient group, the highest HIC was observed in the IV^th segment. Heterogeneity increased for patients with an HIC > 60 μmol/g (mean intra-liver SD = 13.90 μmol/g; mean range = 50.60 μmol/g).

CONCLUSION

This study is the first to demonstrate in vivo HIC heterogeneities using whole-liver mapping analysis. These preliminary results require confirmation through further studies, but might be useful in cases of single ROI analysis.

Autoregressive moving average modeling for hepatic iron quantification in the presence of fat

BACKGROUND

Measuring hepatic R2* by fitting a monoexponential model to the signal decay of a multigradient-echo (mGRE) sequence noninvasively determines hepatic iron content (HIC). Concurrent hepatic steatosis introduces signal oscillations and confounds R2* quantification with standard monoexponential models.

PURPOSE

To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference.

STUDY TYPE

Phantom study and in vivo cohort.

POPULATION

Twenty iron-fat phantoms covering clinically relevant R2* (30-800 s^-1 ) and fat fraction (FF) ranges (0-40%), and 10 patients (four male, six female, mean age 18.8 years).

FIELD STRENGTH/SEQUENCE

2D mGRE acquisitions at 1.5 T and 3 T.

ASSESSMENT

Phantoms were scanned at both field strengths. In vivo data were analyzed using the ARMA model to determine R2* and FF values, and compared with biopsy results.

STATISTICAL TESTS

Linear regression analysis was used to compare ARMA R2* and FF results with those obtained using a conventional monoexponential model, complex-domain nonlinear least squares (NLSQ) fat-water model, and biopsy.

RESULTS

In phantoms and in vivo, all models produced R2* and FF values consistent with expected values in low iron and low/high fat conditions. For high iron and no fat phantoms, monoexponential and ARMA models performed excellently (slopes: 0.89-1.07), but NLSQ overestimated R2* (slopes: 1.14-1.36) and produced false FFs (12-17%) at 1.5 T; in high iron and fat phantoms, NLSQ (slopes: 1.02-1.16) outperformed monoexponential and ARMA models (slopes: 1.23-1.88). The results with NLSQ and ARMA improved in phantoms at 3 T (slopes: 0.96-1.04). In patients, mean R2*-HIC estimates for monoexponential and ARMA models were close to biopsy-HIC values (slopes: 0.90-0.95), whereas NLSQ substantially overestimated HIC (slope 1.4) and produced false FF values (4-28%) with very high SDs (15-222%) in patients with high iron overload and no steatosis.

DATA CONCLUSION

ARMA is superior in quantifying R2* and FF under high iron and no fat conditions, whereas NLSQ is superior for high iron and concurrent fat at 1.5 T. Both models give improved R2* and FF results at 3 T.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1620-1632.

Ultrashort echo time imaging for quantification of hepatic iron overload: Comparison of acquisition and fitting methods via simulations, phantoms, and in vivo data

BACKGROUND

Current R2*-MRI techniques for measuring hepatic iron content (HIC) use various acquisition types and fitting models.

PURPOSE

To evaluate the accuracy and precision of R2*-HIC acquisition and fitting methods.

STUDY TYPE

Signal simulations, phantom study, and prospective in vivo cohort.

POPULATION

In all, 132 patients (58/74 male/female, mean age 17.7 years).

FIELD STRENGTH/SEQUENCE

2D-multiecho gradient-echo (GRE) and ultrashort echo time (UTE) acquisitions at 1.5T.

ASSESSMENT

Synthetic MR signals were created to mimic published GRE and UTE methods, using different R2* values (25-2000 s^-1 ) and signal-to-noise ratios (SNR). Phantoms with varying iron concentrations were scanned at 1.5T. In vivo data were analyzed from 132 patients acquired at 1.5T. R2* was estimated by fitting using three signal models. Accuracy and precision of R2* measurements for UTE acquisition parameters (SNR, echo spacing [ΔTE], maximum echo time [TE_max ]) and fitting methods were compared for simulated, phantom, and in vivo datasets.

STATISTICAL TESTS

R2* accuracy was determined from the relative error and by linear regression analysis. Precision was evaluated using coefficient of variation (CoV) analysis.

RESULTS

In simulations, all models had high R2* accuracy (error <5%) and precision (CoV <10%) for all SNRs, shorter ΔTE (≤0.5 msec), and longer TE_max (≥10.1 msec); except the constant offset model overestimated R2* at the lowest SNR. In phantoms and in vivo, all models produced similar R2* values for different SNRs and shorter ΔTEs (slopes: 0.99-1.06, R² > 0.99, P < 0.001). In all experiments, R2* results degraded for high R2* values with longer ΔTE (≥1 msec). In vivo, shorter and longer TE_max gave similar R2* results (slopes: 1.02-1.06, R² > 0.99, P < 0.001) for the noise subtraction model for 25≤R2*≤2000 s^-1 . However, both quadratic and constant offset models, using shorter TE_max (≤4.7 msec) overestimated R2* and yielded high CoVs up to ∼170% for low R2* (<250 s^-1 ).

DATA CONCLUSION

UTE with TE_max ≥ 10.1 msec and ΔTE ≤ 0.5 msec yields accurate R2* estimates over the entire clinical HIC range. Monoexponential fitting with noise subtraction is the most robust signal model to changes in UTE parameters and achieves the highest R2* accuracy and precision.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1475-1488.

Quantifying iron content in magnetic resonance imaging

Measuring iron content has practical clinical indications in the study of diseases such as Parkinson's disease, Huntington's disease, ferritinopathies and multiple sclerosis as well as in the quantification of iron content in microbleeds and oxygen saturation in veins. In this work, we review the basic concepts behind imaging iron using T2, T2*, T2', phase and quantitative susceptibility mapping in the human brain, liver and heart, followed by the applications of in vivo iron quantification in neurodegenerative diseases, iron tagged cells and ultra-small superparamagnetic iron oxide (USPIO) nanoparticles.

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.

Quantitative susceptibility mapping in combination with water-fat separation for simultaneous liver iron and fat fraction quantification

PURPOSES

To evaluate the feasibility of simultaneous quantification of liver iron concentration (LIC) and fat fraction (FF) using water-fat separation and quantitative susceptibility mapping (QSM).

METHODS

Forty-five patients suspected of liver iron overload (LIO) were included. A volumetric interpolated breath-hold examination sequence for QSM and FF, a fat-saturated gradient echo sequence for R₂^*, a spin echo sequence for LIC measurements and MRS analyses for FF (FF-MRS) were performed. Magnetic susceptibility and FF were calculated using a water-fat separation method (FF-MRI). Correlation and receiver operating characteristic analyses were performed.

RESULTS

Magnetic susceptibility showed strong correlation with LIC (r_s=0.918). The optimal susceptibility cut-off values were 0.34, 0.63, 1.29 and 2.23 ppm corresponding to LIC thresholds of 1.8, 3.2, 7.0 and 15.0 mg/g dry weight. The area under the curve (AUC) were 0.948, 0.970, 1 and 1, respectively. No difference in AUC was found between susceptibility and R₂^* at all LIC thresholds. Correlation was found between FF-MRI and FF-MRS (R²=0.910).

CONCLUSIONS

QSM has a high diagnostic performance for LIC quantification, similar to that of R₂^*. FF-MRI provides simultaneous fat quantification. Findings suggest QSM in combination with water-fat separation has potential value for evaluating LIO, especially in cases with coexisting steatosis.

KEY POINTS

• Magnetic susceptibility showed strong correlation with LIC (r _s =0.918). • QSM showed high diagnostic performance for LIC, similar to that of R ₂^* . • Simultaneously estimated FF-MRI showed strong correlation with MR-Spectroscopy-based FF (R ² =0.910). • QSM combining water-fat separation has quantitative value for LIO with coexisted steatosis.

Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2*-MRI

BACKGROUND

Extraction of liver parenchyma is an important step in the evaluation of R2*-based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole-liver contouring and T2*-thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time-consuming, and susceptible to interreviewer variability.

PURPOSE

To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of R2*-based HIC.

STUDY TYPE

Retrospective analysis of clinical data.

SUBJECTS

Data from 511 MRI exams performed on 257 patients were analyzed.

FIELD STRENGTH/SEQUENCE

All patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC.

ASSESSMENT

An automated method based on a multiscale vessel enhancement filter was investigated for three input data types-contrast-optimized composite image, T2* map, and R2* map-to segment blood vessels and extract liver tissue for R2*-based HIC assessment. Segmentation and R2* results obtained using this automated technique were compared with those from a reference T2*-thresholding technique performed by a radiologist.

STATISTICAL TESTS

The Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland-Altman analyses were performed to compare the R2* results, obtained with the automated and reference techniques.

RESULTS

Mean liver R2* values estimated from all three filter-based methods showed excellent agreement with the reference method (slopes 1.04-1.05, R² > 0.99, P < 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87-88%. The T2*-thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in R2* values compared to the automated method.

DATA CONCLUSION

The excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist's workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1542-1551.

Radial Ultrashort TE Imaging Removes the Need for Breath-Holding in Hepatic Iron Overload Quantification by R2* MRI

OBJECTIVE

The objective of this study is to evaluate radial free-breathing (FB) multiecho ultrashort TE (UTE) imaging as an alternative to Cartesian FB multiecho gradient-recalled echo (GRE) imaging for quantitative assessment of hepatic iron content (HIC) in sedated patients and subjects unable to perform breath-hold (BH) maneuvers.

MATERIALS AND METHODS

FB multiecho GRE imaging and FB multiecho UTE imaging were conducted for 46 test group patients with iron overload who could not complete BH maneuvers (38 patients were sedated, and eight were not sedated) and 16 control patients who could complete BH maneuvers. Control patients also underwent standard BH multiecho GRE imaging. Quantitative R2* maps were calculated, and mean liver R2* values and coefficients of variation (CVs) for different acquisitions and patient groups were compared using statistical analysis.

RESULTS

FB multiecho GRE images displayed motion artifacts and significantly lower R2* values, compared with standard BH multiecho GRE images and FB multiecho UTE images in the control cohort and FB multiecho UTE images in the test cohort. In contrast, FB multiecho UTE images produced artifact-free R2* maps, and mean R2* values were not significantly different from those measured by BH multiecho GRE imaging. Motion artifacts on FB multiecho GRE images resulted in an R2* CV that was approximately twofold higher than the R2* CV from BH multiecho GRE imaging and FB multiecho UTE imaging. The R2* CV was relatively constant over the range of R2* values for FB multiecho UTE, but it increased with increases in R2* for FB multiecho GRE imaging, reflecting that motion artifacts had a stronger impact on R2* estimation with increasing iron burden.

CONCLUSION

FB multiecho UTE imaging was less motion sensitive because of radial sampling, produced excellent image quality, and yielded accurate R2* estimates within the same acquisition time used for multiaveraged FB multiecho GRE imaging. Thus, FB multiecho UTE imaging is a viable alternative for accurate HIC assessment in sedated children and patients who cannot complete BH maneuvers.

3D Multiecho Dixon for the Evaluation of Hepatic Iron and Fat in a Clinical Setting

PURPOSE

To prospectively evaluate a new 3D-multiecho-Dixon (3D-ME-Dixon) sequence for the quantification of hepatic iron and fat in a clinical setting.

MATERIALS AND METHODS

In all, 120 patients underwent 1.5T magnetic resonance imaging of the liver between December 2013 and June 2015 including the following three sequences: 3D-ME-Dixon with inline calculation of R2* and proton-density fat-fraction (PDFF) maps, single-voxel-spectroscopy (SVS), 2D multigradient-echo sequence (2D-ME-GRE). SVS and 2D-ME-GRE were used as reference for PDFF and R2*, respectively. R2*- and PDFF-values from 3D-ME-Dixon were compared with those of the reference. Linear regression analysis, Bland-Altman plots, and agreement parameters were calculated.

RESULTS

In total, 103 patients were finally included (87 men and 16 women; mean age, 50.51 years); 17/120 were excluded due to fat/water-swaps or R2*-values exceeding the constraint of 400 1/s for 3D-ME-Dixon. A strong correlation (r = 0.992, P < 0.001) between R2* of 3D-ME-Dixon and the reference 2D-ME-GRE was found. Bland-Altman analysis revealed systematically lower values for 3D-ME-Dixon (16.499%). Using an adapted threshold of 57 1/s, 3D-ME-Dixon obtained a positive/negative percentage agreement (PPA/NPA) of 84.4%/91.4% for detecting hepatic iron overload. For hepatic fat the correlation between 3D-ME-Dixon and the reference SVS was strong (r = 0.957, P < 0.001); PPA/NPA was 88.3%/91.4%.

CONCLUSION

The 3D-ME-Dixon sequence is a valuable tool for the evaluation of hepatic iron and fat in a clinical setting. Fat/water-swaps remain a drawback requiring improvements to the implementation and making it necessary to have proven conventional sequences at hand in case of an eventual occurrence.

LEVEL OF EVIDENCE

1. Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:793-800.

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.

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

BACKGROUND

Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients.

OBJECTIVE

To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*.

MATERIALS AND METHODS

This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images.

RESULTS

Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1.

CONCLUSION

We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be interchangeably used in existing R2*-HIC calibrations.

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.