Reference Values for Water-Specific T1 of the Liver at 3 T: T2*-Compensation and the Confounding Effects of Fat

BACKGROUND

T1 mapping of the liver is confounded by the presence of fat. Multiparametric T1 mapping combines fat-water separation with T1-weighting to enable imaging of water-specific T1 (T1Water ), proton density fat fraction (PDFF), and T2* values. However, normative T1Water values in the liver and its dependence on age/sex is unknown.

PURPOSE

Determine normative values for T1Water in the liver with comparison to MOLLI and evaluate a T2*-compensation approach to reduce T1 variability.

STUDY TYPE

Prospective observational; phantoms.

POPULATIONS

One hundred twenty-four controls (56 male, 18-75 years), 50 patients at-risk for liver disease (18 male, 30-76 years).

FIELD STRENGTH/SEQUENCE

2.89 T; Saturation-recovery chemical-shift encoded T1 Mapping (SR-CSE); MOLLI.

ASSESSMENT

SR-CSE provided T1Water measurements, PDFF and T2* values in the liver across three slices in 6 seconds. These were compared with MOLLI T1 values. A new T2*-compensation approach to reduce T1 variability was evaluated test/re-test reproducibility.

STATISTICAL TESTS

Linear regression, ANCOVA, t-test, Bland and Altman, intraclass correlation coefficient (ICC). P < 0.05 was considered statistically significant.

RESULTS

Liver T1 values were significantly higher in healthy females (F) than males (M) for both SR-CSE (F-973 ± 78 msec, M-930 ± 72 msec) and MOLLI (F-802 ± 55 msec, M-759 ± 69 msec). T1 values were negatively correlated with age, with similar sex- and age-dependencies observed in T2*. The T2*-compensation model reduced the variability of T1 values by half and removed sex- and age-differences (SR-CSE: F-946 ± 36 msec, M-941 ± 43 msec; MOLLI: F-775 ± 35 msec, M-770 ± 35 msec). At-risk participants had elevated PDFF and T1 values, which became more distinct from the healthy cohort after T2*-compensation. MOLLI systematically underestimated liver T1 values by ~170 msec with an additional positive T1-bias from fat content (~11 msec/1% in PDFF). Reproducibility ICC values were ≥0.96 for all parameters.

DATA CONCLUSION

Liver T1Water values were lower in males and decreased with age, as observed for SR-CSE and MOLLI acquisitions. MOLLI underestimated liver T1 with an additional large positive fat-modulated T1 bias. T2*-compensation removed sex- and age-dependence in liver T1, reduced the range of healthy values and increased T1 group differences between healthy and at-risk groups.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

7 Tesla MRI Liver Fat Quantification in Mice: Data Quality Assessment

PURPOSE

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) via spatially resolved error estimation.

MATERIALS AND METHODS

Using standard T2* relaxation time measurement protocols, in-vivo and ex-vivo MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an in vivo mouse liver measurement.

RESULTS

The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For in vivo measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as in vivo occurring reasons for the increased errors.

CONCLUSION

The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.

SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase-cycled balanced SSFP signal profile

PURPOSE

To develop SPARCQ (Signal Profile Asymmetries for Rapid Compartment Quantification), a novel approach to quantify fat fraction (FF) using asymmetries in the phase-cycled balanced SSFP (bSSFP) profile.

METHODS

SPARCQ uses phase-cycling to obtain bSSFP frequency profiles, which display asymmetries in the presence of fat and water at certain TRs. For each voxel, the measured signal profile is decomposed into a weighted sum of simulated profiles via multi-compartment dictionary matching. Each dictionary entry represents a single-compartment bSSFP profile with a specific off-resonance frequency and relaxation time ratio. Using the results of dictionary matching, the fractions of the different off-resonance components are extracted for each voxel, generating quantitative maps of water and FF and banding-artifact-free images for the entire image volume. SPARCQ was validated using simulations, experiments in a water-fat phantom and in knees of healthy volunteers. Experimental results were compared with reference proton density FFs obtained with 1 H-MRS (phantoms) and with multiecho gradient-echo MRI (phantoms and volunteers). SPARCQ repeatability was evaluated in six scan-rescan experiments.

RESULTS

Simulations showed that FF quantification is accurate and robust for SNRs greater than 20. Phantom experiments demonstrated good agreement between SPARCQ and gold standard FFs. In volunteers, banding-artifact-free quantitative maps and water-fat-separated images obtained with SPARCQ and ME-GRE demonstrated the expected contrast between fatty and non-fatty tissues. The coefficient of repeatability of SPARCQ FF was 0.0512.

CONCLUSION

SPARCQ demonstrates potential for fat quantification using asymmetries in bSSFP profiles and may be a promising alternative to conventional FF quantification techniques.

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data

BACKGROUND. The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantification is not widely available. As an alternative, hepatic fat can be assessed by a two-point Dixon method to calculate signal fat fraction (FF) from conventional T1-weighted in- and opposed-phase (IOP) images, although signal FF is prone to biases, leading to inaccurate quantification. OBJECTIVE. The purpose of this study was to compare hepatic fat quantification by use of PDFF inferred from conventional T1-weighted IOP images and deep-learning convolutional neural networks (CNNs) with quantification by use of two-point Dixon signal FF with CSE-MRI PDFF as the reference standard. METHODS. This study entailed retrospective analysis of data from 292 participants (203 women, 89 men; mean age, 53.7 ± 12.0 [SD] years) enrolled at two sites from September 1, 2017, to December 18, 2019, in the Strong Heart Family Study (a prospective population-based study of American Indian communities). Participants underwent liver MRI (site A, 3 T; site B, 1.5 T) including T1-weighted IOP MRI and CSE-MRI (used to reconstruct CSE PDFF and CSE R2* maps). With CSE PDFF as reference, a CNN was trained in a random sample of 218 (75%) participants to infer voxel-by-voxel PDFF maps from T1-weighted IOP images; testing was performed in the other 74 (25%) participants. Parametric values from the entire liver were automatically extracted. Per-participant median CNN-inferred PDFF and median two-point Dixon signal FF were compared with reference median CSE-MRI PDFF by means of linear regression analysis, intraclass correlation coefficient (ICC), and Bland-Altman analysis. The code is publicly available at github.com/kang927/CNN-inference-of-PDFF-from-T1w-IOP-MR. RESULTS. In the 74 test-set participants, reference CSE PDFF ranged from 1% to 32% (mean, 11.3% ± 8.3% [SD]); reference CSE R2* ranged from 31 to 457 seconds-1 (mean, 62.4 ± 67.3 seconds-1 [SD]). Agreement metrics with reference to CSE PDFF for CNN-inferred PDFF were ICC = 0.99, bias = -0.19%, 95% limits of agreement (LoA) = (-2.80%, 2.71%) and for two-point Dixon signal FF were ICC = 0.93, bias = -1.11%, LoA = (-7.54%, 5.33%). CONCLUSION. Agreement with reference CSE PDFF was better for CNN-inferred PDFF from conventional T1-weighted IOP images than for two-point Dixon signal FF. Further investigation is needed in individuals with moderate-to-severe iron overload. CLINICAL IMPACT. Measurement of CNN-inferred PDFF from widely available T1-weighted IOP images may facilitate adoption of hepatic PDFF as a quantitative bio-marker for liver fat assessment, expanding opportunities to screen for hepatic steatosis and nonalcoholic fatty liver disease.

Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS): Assessment & validation of imaging modality performance across the NAFLD spectrum in a prospectively recruited cohort study (the LITMUS imaging study): Study protocol

Non-alcoholic fatty liver disease (NAFLD) is the liver manifestation of the metabolic syndrome with global prevalence reaching epidemic levels. Despite the high disease burden in the population only a small proportion of those with NAFLD will develop progressive liver disease, for which there is currently no approved pharmacotherapy. Identifying those who are at risk of progressive NAFLD currently requires a liver biopsy which is problematic. Firstly, liver biopsy is invasive and therefore not appropriate for use in a condition like NAFLD that affects a large proportion of the population. Secondly, biopsy is limited by sampling and observer dependent variability which can lead to misclassification of disease severity. Non-invasive biomarkers are therefore needed to replace liver biopsy in the assessment of NAFLD. Our study addresses this unmet need. The LITMUS Imaging Study is a prospectively recruited multi-centre cohort study evaluating magnetic resonance imaging and elastography, and ultrasound elastography against liver histology as the reference standard. Imaging biomarkers and biopsy are acquired within a 100-day window. The study employs standardised processes for imaging data collection and analysis as well as a real time central monitoring and quality control process for all the data submitted for analysis. It is anticipated that the high-quality data generated from this study will underpin changes in clinical practice for the benefit of people with NAFLD. Study Registration: clinicaltrials.gov: NCT05479721.

Different Ultrasound Shear Wave Elastography Techniques as Novel Imaging-Based Approaches for Quantitative Evaluation of Hepatic Steatosis-Preliminary Findings

BACKGROUND

Modern ultrasound (US) shear-wave dispersion (SWD) and attenuation imaging (ATI) can be used to quantify changes in the viscosity and signal attenuation of the liver parenchyma, which are altered in hepatic steatosis. We aimed to evaluate modern shear-wave elastography (SWE), SWD and ATI for the assessment of hepatic steatosis.

METHODS

We retrospectively analyzed the US data of 15 patients who underwent liver USs and MRIs for the evaluation of parenchymal disease/liver lesions. The USs were performed using a multifrequency convex probe (1-8 MHz). The quantitative US measurements for the SWE (m/s/kPa), the SWD (kPa-m/s/kHz) and the ATI (dB/cm/MHz) were acquired after the mean value of five regions of interest (ROIs) was calculated. The liver MRI (3T) quantification of hepatic steatosis was performed by acquiring proton density fat fraction (PDFF) mapping sequences and placing five ROIs in artifact-free areas of the PDFF scan, measuring the fat-signal fraction. We correlated the SWE, SWD and ATI measurements to the PDFF results.

RESULTS

Three patients showed mild steatosis, one showed moderate steatosis and eleven showed no steatosis in the PDFF sequences. The calculated SWE cut-off (2.5 m/s, 20.4 kPa) value identified 3/4 of patients correctly (AUC = 0.73, p > 0.05). The SWD cut-off of 18.5 m/s/kHz, which had a significant correlation (r = 0.55, p = 0.034) with the PDFF results (AUC = 0.73), identified four patients correctly (p < 0.001). The ideal ATI (AUC = 0.53 (p < 0.05)) cut-off was 0.59 dB/cm/MHz, which showed a significantly good correlation with the PDFF results (p = 0.024).

CONCLUSION

Hepatic steatosis can be accurately detected using all the US-elastography techniques applied in this study, although the SWD and the SWE showed to be more sensitive than the PDFF.

Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow

This work proposes a method for automatic standardized assessment of bone marrow volume and spatial distribution of the proton density fat fraction (PDFF) in vertebral bodies. Intra- and interindividual variability in size and shape of vertebral bodies is a challenge for comparable interindividual evaluation and monitoring of changes in the composition and distribution of bone marrow due to aging and/or intervention. Based on deep learning image segmentation, bone marrow PDFF of single vertebral bodies is mapped to a cylindrical template and corrected for the inclination with respect to the horizontal plane. The proposed technique was applied and tested in a cohort of 60 healthy (30 males, 30 females) individuals. Obtained bone marrow volumes and mean PDFF values are comparable to former manual and (semi-)automatic approaches. Moreover, the proposed method allows shape-independent characterization of the spatial PDFF distribution inside vertebral bodies.

Beyond mean value analysis - a voxel-based analysis of the quantitative MR biomarker water T2 in the presence of fatty infiltration in skeletal muscle tissue of patients with neuromuscular diseases

The main pathologies in the muscles of patients with neuromuscular diseases (NMD) are fatty infiltration and edema. Recently, quantitative magnetic resonance (MR) imaging for determination of the MR biomarkers proton density fat fraction (PDFF) and water T₂ (T_2w ) has been advanced. Biophysical effects or pathology can have different effects on MR biomarkers. Thus, for heterogeneously affected muscles, the routinely performed mean or median value analyses of MR biomarkers are questionable. Our work presents a voxel-based histogram analysis of PDFF and T_2w images to point out potential quantification errors. In 12 patients with NMD, chemical-shift encoding-based water-fat imaging for PDFF and T₂ mapping with spectral adiabatic inversion recovery (SPAIR) for T_2w determination was performed. Segmentation of nine thigh muscles was performed bilaterally (n = 216). PDFF and T₂ maps were coregistered. A voxel-based comparison of PDFF and T_2w showed a decreased T_2w with increasing PDFF. Mean T_2w and mean T_2w ^{without fatty voxels} (PDFF < 10%) show good agreement, whereas standard deviation (σ) T_2w and σ T_2w ^{without fatty voxels} show increasing difference with increasing values of σ. Thereby two subgroups can be observed, referring to muscles in which the exclusion of fatty voxels has a negligible influence versus muscles in which a strong dependency of the T_2w value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T_2w in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T_2w elevation, and (ii) a dependency of T_2w on the PDFF leading to decreased T_2w , the T_2w distribution within a muscle might be heterogenous and the routine mean or median analysis can lead to a misinterpretation of the muscle health. It was concluded that muscle T_2w mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T_2w histograms is a potential alternative.

Clinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) is increasing in prevalence worldwide. NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH).

METHODS

Data from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC).

RESULTS

A stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74-0.82), for liver fat was 0.78 (95% CI, 0.73-0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78-0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74-0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64-0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75-0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers.

CONCLUSIONS

This study shows that quantitative MRI-derived biomarkers cT1 and liver fat are suitable for identifying patients with NASH, and cT1 is a better noninvasive technology than liver fat to identify NASH patients at greatest risk of disease progression. Therefore, MRI cT1 and liver fat have important clinical utility to help guide the appropriate use of interventions in NAFLD and NASH clinical care pathways.

Quantification of Hepatic Steatosis by Ultrasound: Prospective Comparison With MRI Proton Density Fat Fraction as Reference Standard

BACKGROUND. Multiple ultrasound platforms now provide quantitative measures of hepatic steatosis. One such measure is the ultrasound-derived fat fraction (UDFF), which combines attenuation and backscatter quantification. OBJECTIVE. The purpose of this study was to characterize agreement between UDFF and MRI proton-density fat fraction (PDFF) measurements. METHODS. This prospective cross-sectional study enrolled 56 overweight and obese adolescents and adults (age ≥ 16 years) who underwent investigational ultrasound (deep abdominal transducer) and MRI examinations of the liver during a single visit from August 2020 to October 2020. Ultrasound examinations included three UDFF acquisitions of five measurements each (15 measurements total), and an overall median of medians was computed (UDFF_overall). MRI examinations included three PDFF acquisitions with calculation of an overall median PDFF. Spearman rank-order correlation was computed between UDFF and MRI PDFF measurements. Intraclass correlation coefficients and Bland-Altman difference plots were used to assess agreement. ROC curves were used to assess diagnostic performance of UDFF for detecting MRI PDFF of 5.5% or more. RESULTS. Median participant age was 32.5 years (IQR, 24.0-39.0 years); 40 participants were female, and 16 were male. A total of 34 (60.7%) participants had an MRI PDFF of 5.5% or more. UDFF_overall was 10.5% (IQR, 5.0-20.0%); median MRI PDFF was 6.1% (IQR, 3.4-13.7%). UDFF_overall was positively associated with MRI PDFF (ρ, 0.82; p < .001; intraclass correlation coefficient, 0.84 [95% CI, 0.59-0.93]). Mean bias between UDFF and PDFF was 4.0% (95% limits of agreement, -7.9% to 15.9%), with similar bias if summarizing UDFF by the first five measurements (4.4%), first three measurements (4.4%), or first measurement (4.6%). UDFF_overall AUC was 0.90 (95% CI, 0.79-0.96) for MRI PDFF of 5.5% or more; AUC was not significantly different when it was based on the number of UDFF measurements (p = .11-.97 for all pairwise AUC comparisons). UDFF_overall cutoff of more than 5% had sensitivity of 94.1% and specificity of 63.6% for diagnosing MRI PDFF of 5.5% or more. CONCLUSION. Measurements of hepatic steatosis using UDFF show strong agreement with measurements by MRI PDFF. A UDFF_overall cutoff of more than 5% provides high AUC and sensitivity, albeit low specificity, for detection of MRI PDFF of 5.5% or more. CLINICAL IMPACT. UDFF may have a clinical role in detection of hepatic steatosis. A reduced number of individual measurements is likely sufficient for determining an overall UDFF value. TRIAL REGISTRATION. ClinicalTrials.gov: NCT04523584.

Detection of fatty liver using virtual non-contrast dual-energy CT

PURPOSE

Determine whether liver attenuation measured on dual-energy CT (DECT) virtual non-contrast examinations predicts the presence of fatty liver.

METHODS

Single-institution retrospective review from 2016 to 2020 found patients with DECT and proton density fat fraction MRI (MRI PDFF) within 30 days. MRI PDFF was the reference standard for determining hepatic steatosis. Attenuation measurements from VNC and mixed 120 kVp-like images were compared to MRI PDFF in the right and left lobes. Performance of VNC was compared to measurement of the liver-spleen attenuation difference (LSAD).

RESULTS

128 patients were included (69 men, 59 women) with mean age 51.6 years (range 14-98 years). > 90% of patients received CT and MRI in the emergency department or as inpatients. Median interval between DECT and MRI PDFF was 2 days (range 0-28 days). Prevalence of fatty liver using the reference standard (MRI PDFF > 6%) was 24%. Pearson correlation coefficient between VNC and MRI- DFF was -0.64 (right) and -0.68 (left, both p < 0.0001). For LSAD, correlation was - 0.43 in both lobes (p < 0.0001). Considering MRI PDFF > 6% as diagnostic of steatosis, area under the receiver operator characteristic curve (AUC) was 0.834 and 0.872 in the right and left hepatic lobes, with an optimal threshold of 54.8 HU (right) and 52.5 HU (left), yielding sensitivity/specificity of 57%/93.9% (right) and 67.9%/90% (left). For LSAD, AUC was 0.808 (right) and 0.767 (left) with optimal sensitivity/specificity of 93.3%/57.1% (right) and 78.6%/68% (left).

CONCLUSION

Attenuation measured at VNC CT was moderately correlated with liver fat content and had > 90% specificity for diagnosis of fatty liver.

Associations of incidental vertebral fractures and longitudinal changes of MR-based proton density fat fraction and T2* measurements of vertebral bone marrow

BACKGROUND

Quantitative magnetic resonance imaging (MRI) techniques such as chemical shift encoding-based water-fat separation techniques (CSE-MRI) are increasingly applied as noninvasive biomarkers to assess the biochemical composition of vertebrae. This study aims to investigate the longitudinal change of proton density fat fraction (PDFF) and T2* derived from CSE-MRI of the thoracolumbar vertebral bone marrow in patients that develop incidental vertebral compression fractures (VCFs), and whether PDFF and T2* enable the prediction of an incidental VCF.

METHODS

In this study we included 48 patients with CT-derived bone mineral density (BMD) measurements at baseline. Patients that presented an incidental VCF at follow up (N=12, mean age 70.5 ± 7.4 years, 5 female) were compared to controls without incidental VCF at follow up (N=36, mean age 71.1 ± 8.6 years, 15 females). All patients underwent 3T MRI, containing a significant part of the thoracolumbar spine (Th11-L4), at baseline, 6-month and 12 month follow up, including a gradient echo sequence for chemical shift encoding-based water-fat separation, from which PDFF and T2* maps were obtained. Associations between changes in PDFF, T2* and BMD measurements over 12 months and the group (incidental VCF vs. no VCF) were assessed using multivariable regression models. Mixed-effect regression models were used to test if there is a difference in the rate of change in PDFF, T2* and BMD between patients with and without incidental VCF.

RESULTS

Prior to the occurrence of an incidental VCF, PDFF in vertebrae increased in the VCF group (Δ_PDFF=6.3 ± 3.1%) and was significantly higher than the change of PDFF in the group without VCF (Δ_PDFF=2.1 ± 2.5%, P=0.03). There was no significant change in T2* (Δ_T2*=1.7 ± 1.1ms vs. Δ_T2*=1.1 ± 1.3ms, P=0.31) and BMD (Δ_BMD=-1.2 ± 11.3mg/cm³ vs. Δ_BMD=-11.4 ± 24.1mg/cm³, P= 0.37) between the two groups over 12 months. At baseline, no significant differences were detected in the average PDFF, T2* and BMD of all measured vertebrae (Th11-L4) between the VCF group and the group without VCF (P=0.66, P=0.35 and P= 0.21, respectively). When assessing the differences in rates of change, there was a significant change in slope for PDFF (2.32 per 6 months, 95% confidence interval (CI) 0.31-4.32; P=0.03) but not for T2* (0.02 per 6 months, CI -0.98-0.95; P=0.90) or BMD (-4.84 per 6 months, CI -23.4-13.7; P=0.60).

CONCLUSIONS

In our study population, the average change of PDFF over 12 months is significantly higher in patients that develop incidental fractures at 12-month follow up compared to patients without incidental VCF, while T2* and BMD show no significant changes prior to the occurrence of the incidental vertebral fractures. Therefore, a longitudinal increase in bone marrow PDFF may be predictive for vertebral compression fractures.

Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T₁ . These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.

Limits of Fat Quantification in the Presence of Iron Overload

BACKGROUND

Chemical shift encoded magnetic resonance imaging (CSE-MRI)-based tissue fat quantification is confounded by increased R2* signal decay rate caused by the presence of excess iron deposition.

PURPOSE

To determine the upper limit of R2* above which it is no longer feasible to quantify proton density fat fraction (PDFF) reliably, using CSE-MRI.

STUDY TYPE

Prospective.

POPULATION

Cramér-Rao lower bound (CRLB) calculations, Monte Carlo simulations, phantom experiments, and a prospective study in 26 patients with known or suspected liver iron overload.

FIELD STRENGTH/SEQUENCE

Multiecho gradient echo at 1.5 T and 3.0 T.

ASSESSMENT

CRLB calculations were used to develop an empirical relationship between the maximum R2* value above which PDFF estimation will achieve a desired number of effective signal averages. A single voxel multi-TR, multi-TE stimulated echo acquisition mode magnetic resonance spectroscopy acquisition was used as a reference standard to estimate PDFF. Reconstructed PDFF and R2* maps were analyzed by one analyst using multiple regions of interest drawn in all nine Couinaud segments.

STATISTICAL TESTS

None.

RESULTS

Simulations, phantom experiments, and in vivo measurements demonstrated unreliable PDFF estimates with increased R2*, with PDFF errors as large as 20% at an R2* of 1000 s-1 . For typical optimized Cartesian acquisitions (TE1 = 0.75 msec, ΔTE = 0.67 msec at 1.5 T, TE1 = 0.65 msec, ΔTE = 0.58 msec at 3.0 T), an empirical relationship between PDFF estimation errors and acquisition parameters was developed that suggests PDFF estimates are unreliable above an R2* of ~538 s-1 and ~779 s-1 at 1.5 T and 3 T, respectively. This empirical relationship was further investigated with phantom experiments and in vivo measurements, with PDFF errors at an R2* of 1000 s-1 at 3.0 T as large as 10% with TE1 = 1.24 msec, ΔTE = 1.01 msec compared to 3% with TE1 = 0.65 msec, ΔTE = 0.58 msec.

DATA CONCLUSION

We successfully developed a theoretically-based empirical formula that may provide an easily calculable guideline to identify R2* values above which PDFF is not reliable in research and clinical applications using CSE-MRI to quantify PDFF in the presence of iron overload.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1.

Feasibility of Hepatic Fat Quantification Using Proton Density Fat Fraction by Multi-Echo Chemical-Shift-Encoded MRI at 7T

Fat fraction quantification and assessment of its distribution in the hepatic tissue become more important with the growing epidemic of obesity, and the increasing prevalence of diabetes mellitus type 2 and non-alcoholic fatty liver disease. At 3Tesla, the multi-echo, chemical-shift-encoded magnetic resonance imaging (CSE-MRI)-based acquisition allows the measurement of proton density fat-fraction (PDFF) even in clinical protocols. Further improvements in SNR can be achieved by the use of phased array coils and increased static magnetic field. The purpose of the study is to evaluate the feasibility of PDFF imaging using a multi-echo CSE-MRI technique at ultra-high magnetic field (7Tesla). Thirteen volunteers (M/F) with a broad range of age, body mass index, and hepatic PDFF were measured at 3 and 7T by multi-gradient-echo MRI and single-voxel spectroscopy MRS. All measurements were performed in breath-hold (exhalation); the MRI protocols were optimized for a short measurement time, thus minimizing motion-related problems. 7T data were processed off-line using Matlab® (MRI:multi-gradient-echo) and jMRUI (MRS), respectively. For quantitative validation of the PDFF results, a similar protocol was performed at 3T, including on-line data processing provided by the system manufacturer, and correlation analyses between 7 and 3T data were performed off-line. The multi-echo CSE-MRI measurements at 7T with a phased-array coil configuration and an optimal post-processing yielded liver volume coverage ranging from 30 to 90% for high- and low-BMI subjects, respectively. PDFFs ranged between 1 and 20%. We found significant correlations between 7T MRI and -MRS measurements (R² ≅ 0.97; p < 0.005), and between MRI-PDFF at 7T and 3T fields (R² ≅ 0.94; p < 0.005) in the evaluated volumes. Based on the measurements and analyses performed, the multi-echo CSE-MRI method using a 32-channel coil at 7T showed its aptitude for MRI-based quantitation of PDFF in the investigated volumes. The results are the first step toward qMRI of the whole liver at 7T with further improvements in hardware.

Automated Analysis of Multiparametric Magnetic Resonance Imaging/Magnetic Resonance Elastography Exams for Prediction of Nonalcoholic Steatohepatitis

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) affects 25% of the global population. The standard of diagnosis, biopsy, is invasive and affected by sampling error and inter-reader variability. We hypothesized that widely available rapid MRI techniques could be used to predict nonalcoholic steatohepatitis (NASH) noninvasively by measuring liver stiffness, with magnetic resonance elastography (MRE), and liver fat, with chemical shift-encoded (CSE) MRI. Besides, we validate an automated image analysis technique to maximize the utility of these methods.

PURPOSE

To implement and test an automated system for analyzing CSE-MRI and MRE data coupled with model-based prediction of NASH.

STUDY TYPE

Prospective.

SUBJECTS

Eighty-three patients with suspected NAFLD.

FIELD STRENGTH/SEQUENCE

A 1.5 T using a flow-compensated motion-encoded gradient echo MRE sequence and a multiecho CSE-MRI sequence.

ASSESSMENTS

The MRE and CSE-MRI data were analyzed by two readers (5+ and 1 years of experience) and an automated algorithm. A logistic regression model to predict pathology-diagnosed NASH was trained based on stiffness and proton density fat fraction, and the area under the receiver operating characteristic curve (AUROC) was calculated using 10-fold cross validation for models based on both automated and manual measurements. A separate model was trained to predict the NASH severity score (NAS).

STATISTICAL TESTS

Pearson's correlation, Bland-Altman, AUROC, C-statistic.

RESULTS

The agreement between automated measurements and the more experienced reader (R²  = 0.87 for stiffness and R²  = 0.99 for proton density fat fraction [PDFF]) was slightly better than the agreement between readers (R²  = 0.85 and 0.98). The model for predicting biopsy-diagnosed NASH had an AUROC of 0.87. The NAS-prediction model had a C-statistic of 0.85.

DATA CONCLUSION

We demonstrated a workflow that used a limited MRI acquisition protocol and fully automated analysis to predict NASH with high accuracy. These methods show promise to provide a reliable noninvasive alternative to biopsy for NASH-screening in populations with NAFLD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 2.

Multiparametric MR mapping in clinical decision-making for diffuse liver disease

Accurate diagnosis, monitoring and treatment decisions in patients with chronic liver disease currently rely on biopsy as the diagnostic gold standard, and this has constrained early detection and management of diseases that are both varied and can be concurrent. Recent developments in multiparametric magnetic resonance imaging (mpMRI) suggest real potential to bridge the diagnostic gap between non-specific blood-based biomarkers and invasive and variable histological diagnosis. This has implications for the clinical care and treatment pathway in a number of chronic liver diseases, such as haemochromatosis, steatohepatitis and autoimmune or viral hepatitis. Here we review the relevant MRI techniques in clinical use and their limitations and describe recent potential applications in various liver diseases. We exemplify case studies that highlight how these techniques can improve clinical practice. These techniques could allow clinicians to increase their arsenals available to utilise on patients and direct appropriate treatments.

Effect of noise and estimator type on bias for analysis of liver proton density fat fraction

PURPOSE

Proton-density fat-fraction (PDFF) is typically measured from PDFF maps by calculating the mean PDFF value within a region of interest (ROI). However, the mean estimator has been shown to result in bias when signal-to-noise ratio (SNR) is low, resulting from a skewed distribution of PDFF noise statistics. Thus, the purpose of this work was to determine the relative performance of three estimation methods (mean, median, maximum likelihood estimators (MLE)) for analysis of liver PDFF maps.

METHODS

Observational study of adult patients (n = 56) undergoing abdominal MRI. Both 2D-sequential CSE-MRI ('low-SNR') and 3D CSE-MRI ('high-SNR') acquisitions were obtained. Single-voxel MRS formed the independent reference measurement of hepatic PDFF. Intra-class correlation was tested on a subset of 'low-SNR' acquisitions. ROIs were semi-automatically co-registered across all acquisitions. Bland-Altman analysis and intra-class correlation coefficients were used for statistical analysis. A p-value of <0.05 was considered significant.

RESULTS

For in vivo low-SNR acquisitions, the mean estimator had a larger error than either the median or MLE values (bias ~ -1% absolute PDFF). The intra-class correlation coefficient was significantly greater for median and maximum likelihood estimators (0.992 and 0.993, respectively) compared to the mean estimator (0.973).

CONCLUSION

Alternative ROI analysis strategies, such as MLE or median estimators, are useful to avoid SNR-related PDFF bias. Median may be the most clinically practical strategy given its ease of calculation.

Simultaneous proton density fat-fraction and R 2 ∗ imaging with water-specific T1 mapping (PROFIT1 ): application in liver

PURPOSE

To describe and validate a simultaneous proton density fat-fraction (PDFF) imaging and water-specific T₁ mapping (T_1(Water) ) approach for the liver (PROFIT₁ ) with R 2 ∗ mapping and low sensitivity to B 1 + calibration or inhomogeneity.

METHODS

A multiecho gradient-echo sequence, with and without saturation preparation, was designed for simultaneous imaging of liver PDFF, R 2 ∗ , and T_1(Water) (three slices in ~13 seconds). Chemical-shift-encoded MRI processing yielded fat-water separated images and R 2 ∗ maps. T_1(Water)  calculation utilized saturation and nonsaturation-recovery water-separated images. Several variable flip angle schemes across k-space (increasing flip angles in sequential RF pulses) were evaluated for minimization of T₁ weighting, to reduce the B 1 + dependence of T_1(Water)  and PDFF (reduced flip angle dependence). T_1(Water)  accuracy was validated in mixed fat-water phantoms, with various PDFF and T₁ values (3T). In vivo application was illustrated in five volunteers and five patients with nonalcoholic fatty liver disease (PDFF, T_1(Water) , R 2 ∗ ).

RESULTS

A sin³ (θ) flip angle pattern (0 < θ < π/2 over k-space) yielded the largest PROFIT₁ signal yield with negligible B 1 + dependence for both T_1(Water) and PDFF. Mixed fat-water phantom experiments illustrated excellent agreement between PROFIT₁ and gold-standard spectroscopic evaluation of PDFF and T_1(Water)  (<1% T₁ error). In vivo PDFF, T_1(Water) , and R 2 ∗ maps illustrated independence of the PROFIT₁ values from B 1 + inhomogeneity and significant differences between volunteers and patients with nonalcoholic fatty liver disease for T_1(Water) (927 ± 56 ms vs. 1033 ± 23 ms; P < .05) and PDFF (2.0% ± 0.8% vs. 13.4% ± 5.0%, P < .05).  R 2 ∗ was similar between groups.

CONCLUSION

The PROFIT₁ pulse sequence provides fast simultaneous quantification of PDFF, T_1(Water) , and R 2 ∗ with minimal sensitivity to B 1 + miscalibration or inhomogeneity.

Effect of respiratory motion on free-breathing 3D stack-of-radial liver R 2 ∗ relaxometry and improved quantification accuracy using self-gating

PURPOSE

To develop an accurate free-breathing 3D liver R 2 ∗ mapping approach and to evaluate it in vivo.

METHODS

A free-breathing multi-echo stack-of-radial sequence was applied in 5 normal subjects and 6 patients at 3 Tesla. Respiratory motion compensation was implemented using the inherent self-gating signal. A breath-hold Cartesian acquisition was the reference standard. Proton density fat fraction and R 2 ∗ were measured and compared between radial and Cartesian methods using Bland-Altman plots. The normal subject results were fitted to a linear mixed model (P < .05 considered significant).

RESULTS

Free-breathing stack-of-radial without self-gating exhibited signal attenuation in echo images and artifactually elevated apparent R 2 ∗ values. In the Bland-Altman plots of normal subjects, compared to breath-hold Cartesian, free-breathing stack-of-radial acquisitions of 22, 30, 36, and 44 slices, had mean R 2 ∗ differences of 27.4, 19.4, 10.9, and 14.7 s^-1 with 800 radial views, and they had 18.4, 11.9, 9.7, and 27.7 s^-1 with 404 views, which were reduced to 0.4, 0.9, -0.2, and -0.7 s^-1 and to -1.7, -1.9, -2.1, and 0.5 s^-1 with self-gating, respectively. No substantial proton density fat fraction differences were found. The linear mixed model showed free-breathing radial R 2 ∗ results without self-gating were significantly biased by 17.2 s^-1 averagely (P = .002), which was eliminated with self-gating (P = .930). Proton density fat fraction results were not different (P > .234). For patients, Bland-Altman plots exhibited mean R 2 ∗ differences of 14.4 and 0.1 s^-1 for free-breathing stack-of-radial without self-gating and with self-gating, respectively, but no substantial proton density fat fraction differences.

CONCLUSION

The proposed self-gating method corrects the respiratory motion bias and enables accurate free-breathing stack-of-radial quantification of liver R 2 ∗ .

MRI Reveals Human Brown Adipose Tissue Is Rapidly Activated in Response to Cold

Context

In rodents, cold exposure induces the activation of brown adipose tissue (BAT) and the induction of intracellular triacylglycerol (TAG) lipolysis. However, in humans, the kinetics of supraclavicular (SCV) BAT activation and the potential importance of TAG stores remain poorly defined.

Objective

To determine the time course of BAT activation and changes in intracellular TAG using MRI assessment of the SCV (i.e., BAT depot) and fat in the posterior neck region (i.e., non-BAT).

Design

Cross-sectional.

Setting

Clinical research center.

Patients or Other Participants

Twelve healthy male volunteers aged 18 to 29 years [body mass index = 24.7 ± 2.8 kg/m² and body fat percentage = 25.0% ± 7.4% (both, mean ± SD)].

Intervention(s)

Standardized whole-body cold exposure (180 minutes at 18°C) and immediate rewarming (30 minutes at 32°C).

Main Outcome Measure(s)

Proton density fat fraction (PDFF) and T2* of the SCV and posterior neck fat pads. Acquisitions occurred at 5- to 15-minute intervals during cooling and subsequent warming.

Results

SCV PDFF declined significantly after only 10 minutes of cold exposure [−1.6% (SE: 0.44%; P = 0.007)] and continued to decline until 35 minutes, after which time it remained stable until 180 minutes. A similar time course was also observed for SCV T2*. In the posterior neck fat (non-BAT), there were no cold-induced changes in PDFF or T2*. Rewarming did not result in a change in SCV PDFF or T2*.

Conclusions

The rapid cold-induced decline in SCV PDFF suggests that in humans BAT is activated quickly in response to cold and that TAG is a primary substrate.

Relationship between abdominal fat stores and liver fat, pancreatic fat, and metabolic comorbidities in a pediatric population with non-alcoholic fatty liver disease

PURPOSE

To define the relationship between compartmental abdominal fat stores, liver and pancreatic fat fractions, and type 2 diabetes mellitus (T2DM) in children with non-alcoholic fatty liver disease (NAFLD).

METHODS

This was a retrospective study of patients with NAFLD who underwent abdominal MRI between August 2015 and July 2017. Using an axial multi-echo Dixon-based sequence, liver fat fraction (LFF) and pancreatic fat fraction (PFF) were measured. The fat image was used to quantify abdominal fat depots (thickness, cross-sectional area) at the L2 vertebral level. Multivariable models with stepwise selection were created for prediction of LFF, PFF, and T2DM status based upon variables of clinical interest.

RESULTS

86 patients (70% male, 25% Hispanic, 58% Caucasian, 11% African American) with a mean age of 14.2 ± 3.2 years were included. 19 (22%) patients were pre-diabetic or diabetic. Only ethnicity was a predictor of LFF (P = 0.0023) with Hispanic ethnicity associated with the highest LFF. Depending on the model, either total abdominal fat area (P = 0.0003) or patient weight (P = 0.008) were the only predictors of PFF. No patient variable predicted T2DM status.

CONCLUSIONS

In our population, there was an association between ethnicity and LFF, with the highest LFF in Hispanics. The presence or severity of hepatic steatosis could not be predicted based on patient size or the distribution of abdominal fat in our cohort. Neither LFF nor PFF were predictive of T2DM.

Decreased water T2 in fatty infiltrated skeletal muscles of patients with neuromuscular diseases

Quantitative imaging techniques are emerging in the field of magnetic resonance imaging of neuromuscular diseases (NMD). T₂ of water (T_2w ) is considered an important imaging marker to assess acute and chronic alterations of the muscle fibers, being generally interpreted as an indicator for "disease activity" in the muscle tissue. To validate the accuracy and robustness of quantitative imaging methods, ¹ H magnetic resonance spectroscopy (MRS) can be used as a gold standard. The purpose of the present work was to investigate T_2w of remaining muscle tissue in regions of higher proton density fat fraction (PDFF) in 40 patients with defined NMD using multi-TE single-voxel ¹ H MRS. Patients underwent MR measurements on a 3 T system to perform a multi-TE single-voxel stimulated echo acquisition method (STEAM) MRS (TE = 11/15/20/25(/35) ms) in regions of healthy, edematous and fatty thigh muscle tissue. Muscle regions for MRS were selected based on T₂ -weighted water and fat images of a two-echo 2D Dixon TSE. MRS results were confined to regions with qualitatively defined remaining muscle tissue without edema and high fat content, based on visual grading of the imaging data. The results showed decreased T_2w values with increasing PDFF with R²  = 0.45 (p < 10^-3 ) (linear fit) and with R²  = 0.51 (exponential fit). The observed dependence of T_2w on PDFF should be considered when using T_2w as a marker in NMD imaging and when performing single-voxel MRS for T_2w in regions enclosing edematous, nonedematous and fatty infiltrated muscle tissue.

Measuring liver fat fraction with complex-based chemical shift MRI: the effect of simplified sampling protocols on accuracy

BACKGROUND

The assessment of liver percentage fat fraction (%FF) using proton density fat fraction sequences is becoming increasingly accessible. Previous studies have tended to use multiple small ROIs that focus on Couinaud segments. In an effort to simplify day-to-day analysis, this study assesses the impact of using larger, elliptical ROIs focused on a single hepatic lobe. Additionally, we assess the impact of sampling fewer transhepatic slices when measuring %FF.

METHODS

Retrospective analysis of prospectively obtained images from 34 volunteers using an IDEAL IQ sequence. Two observers independently measured %FF using three different protocols: freehand whole-liver ROI (fh-ROI), elliptical-ROI on the right lobe (rt-ROI) and elliptical-ROI on the left lobe (lt-ROI).

RESULTS

Inter-observer reliability for all measurements techniques was 'excellent' (Spearman's rank correlation coefficients 0.81-0.98). There was a significant difference (Paired Wilcoxon Test: p < 0.001) between the median %FF obtained using fh-ROI when compared to the rt-ROI method, the maximum mean difference between the two techniques was 2.79% (95% CI). For all sampling methods a Kruskall-Wallis analysis demonstrated no significant difference in mean %FF when the number of slices sampled was reduced from 11 to 1. The mean coefficient of variance increased when more slices were sampled (3 slices = 0.1, 11 slices = 0.17, p < 0.001).

CONCLUSION

Simplified ROIs focused on one hepatic lobe provide %FF measurements that are unlikely to be sufficiently accurate for use in clinical practice. Freehand whole-liver ROIs should be used in preference. A single freehand ROI measurement taken at the level of the hepatic hilum yields a %FF that is representative of the mean whole liver % FF. Multiple slices are needed to measure heterogeneity.

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.

IDEAL-IQ in an oncologic population: meeting the challenge of concomitant liver fat and liver iron

Background

Cancer patients often have a history of chemotherapy, putting them at increased risk of liver toxicity and pancytopenia, leading to elevated liver fat and elevated liver iron respectively. T1-in-and-out-of-phase, the conventional MR technique for liver fat assessment, fails to detect elevated liver fat in the presence of concomitantly elevated liver iron. IDEAL-IQ is a more recently introduced MR fat quantification method that corrects for multiple confounding factors, including elevated liver iron.

Methods

This retrospective study was approved by the institutional review board with a waiver for informed consent. We reviewed the MRI studies of 50 cancer patients (30 males, 20 females, 50–78 years old) whose exams included (1) T1-in-and-out-of-phase, (2) IDEAL-IQ, and (3) T2* mapping. Two readers independently assessed fat and iron content from conventional and IDEAL-IQ MR methods. Intraclass correlation coefficient (ICC) was estimated to evaluate agreement between conventional MRI and IDEAL-IQ in measuring R2* level (a surrogate for iron level), and in measuring fat level. Agreement between the two readers was also assessed. Wilcoxon signed rank test was employed to compare iron level and fat fraction between conventional MRI and IDEAL-IQ.

Results

Twenty percent of patients had both elevated liver iron and moderate/severe hepatic steatosis. Across all patients, there was high agreement between readers for IDEAL-IQ fat fraction (ICC = 0.957) and IDEAL R2* (ICC = 0.971) measurements, but lower agreement for conventional fat fraction measurements (ICC = 0.626). The fat fractions calculated with IOP were statistically significantly different from those calculated with IDEAL-IQ (reader 1: p < 0.001, reader 2: p < 0.001).

Conclusion

Fat measurements using IDEAL-IQ and IOP diverged in patients with concomitantly elevated liver fat and liver iron. Given prior work validating IDEAL-IQ, these diverging measurements indicate that IOP is inadequate to screen for hepatic steatosis in our cancer population.

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

BACKGROUND

The liver R2* value is widely used as a measure of liver iron but may be confounded by the presence of hepatic steatosis and other covariates.

PURPOSE

To identify the most influential covariates for liver R2* values in patients with nonalcoholic fatty liver disease (NAFLD).

STUDY TYPE

Retrospective analysis of prospectively acquired data.

POPULATION

Baseline data from 204 subjects enrolled in NAFLD/NASH (nonalcoholic steatohepatitis) treatment trials.

FIELD STRENGTH

1.5T and 3T; chemical-shift encoded multiecho gradient echo.

ASSESSMENT

Correlation between liver proton density fat fraction and R2*; assessment for demographic, metabolic, laboratory, MRI-derived, and histological covariates of liver R2*.

STATISTICAL TESTS

Pearson's and Spearman's correlations; univariate analysis; gradient boosting machines (GBM) multivariable machine-learning method.

RESULTS

Hepatic proton density fat fraction (PDFF) was the most strongly correlated covariate for R2* at both 1.5T (r = 0.652, P < 0.0001) and at 3T (r = 0.586, P < 0.0001). In the GBM analysis, hepatic PDFF was the most influential covariate for hepatic R2*, with relative influences (RIs) of 61.3% at 1.5T and 47.5% at 3T; less influential covariates had RIs of up to 11.5% at 1.5T and 16.7% at 3T. Nonhepatocellular iron was weakly associated with R2* at 3T only (RI 6.7%), and hepatocellular iron was not associated with R2* at either field strength.

DATA CONCLUSION

Hepatic PDFF is the most influential covariate for R2* at both 1.5T and 3T; nonhepatocellular iron deposition is weakly associated with liver R2* at 3T only.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1456-1466.

MRI liver fat quantification in an oncologic population: the added value of complex chemical shift-encoded MRI

INTRODUCTION

Chemotherapy prolongs the survival of patients with advanced and metastatic tumors. Since the liver plays an active role in the metabolism of chemotherapy agents, hepatic injury is a common adverse effect. The purpose of this study is to compare a novel quantitative chemical shift encoded magnetic resonance imaging (CSE-MRI) method with conventional T1-weighted In and Out of phase (T1 IOP) MR for evaluating the reproducibility of the methods in an oncologic population exposed to chemotherapy.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board with a waiver for informed consent. The study included patients who underwent chemotherapy, no suspected liver iron overload, and underwent upper abdomen MRI. Two radiologists independently draw circular ROIsin the liver parenchyma. The fat fraction was calculated from IOP imaging and measured from IDEAL-IQ fat fraction maps. Two different equations were used to estimate fat with IOP sequences. Intra-class correlation coefficient and repeatability coefficient were estimated to evaluate agreement between two readers on iron level and fat fraction measurement.

RESULTS

CSE-MRI showed a higher reliability in fat quantification compared with both IOP methods, with a substantially higher inter-reader agreement (0.961 vs 0.372). This has important clinical implications.

CONCLUSION

The novel CSE-MRI method described here provides increased reproducibility and confidence in diagnosing hepatic steatosis in a oncologic clinical setting. IDEAL-IQ has been proved to be more reproducible than conventional IOP imaging.

Assessment of a high-SNR chemical-shift-encoded MRI with complex reconstruction for proton density fat fraction (PDFF) estimation overall and in the low-fat range

BACKGROUND

Improving the signal-to-noise ratio (SNR) of chemical-shift-encoded MRI acquisition with complex reconstruction (MRI-C) may improve the accuracy and precision of noninvasive proton density fat fraction (PDFF) quantification in patients with hepatic steatosis.

PURPOSE

To assess the accuracy of high SNR (Hi-SNR) MRI-C versus standard MRI-C acquisition to estimate hepatic PDFF in adult and pediatric nonalcoholic fatty liver disease (NAFLD) using an MR spectroscopy (MRS) sequence as the reference standard.

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

In all, 231 adult and pediatric patients with known or suspected NAFLD.

FIELD STRENGTH/SEQUENCE

PDFF estimated at 3T by three MR techniques: standard MRI-C; a Hi-SNR MRI-C variant with increased slice thickness, decreased matrix size, and no parallel imaging; and MRS (reference standard).

ASSESSMENT

MRI-PDFF was measured by image analysts using a region of interest coregistered with the MRS-PDFF voxel.

STATISTICAL TESTS

Linear regression analyses were used to assess accuracy and precision of MRI-estimated PDFF for MRS-PDFF as a function of MRI-PDFF using the standard and Hi-SNR MRI-C for all patients and for patients with MRS-PDFF <10%.

RESULTS

In all, 271 exams from 231 patients were included (mean MRS-PDFF: 12.6% [SD: 10.4]; range: 0.9-41.9). High agreement between MRI-PDFF and MRS-PDFF was demonstrated across the overall range of PDFF, with a regression slope of 1.035 for the standard MRI-C and 1.008 for Hi-SNR MRI-C. Hi-SNR MRI-C, compared to standard MRI-C, provided small but statistically significant improvements in the slope (respectively, 1.008 vs. 1.035, P = 0.004) and mean bias (0.412 vs. 0.673, P < 0.0001) overall. In the low-fat patients only, Hi-SNR MRI-C provided improvements in the slope (1.058 vs. 1.190, P = 0.002), mean bias (0.168 vs. 0.368, P = 0.007), intercept (-0.153 vs. -0.796, P < 0.0001), and borderline improvement in the R² (0.888 vs. 0.813, P = 0.01).

DATA CONCLUSION

Compared to standard MRI-C, Hi-SNR MRI-C provides slightly higher MRI-PDFF estimation accuracy across the overall range of PDFF and improves both accuracy and precision in the low PDFF range.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:229-238.

Optimization of region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification

BACKGROUND

Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment.

PURPOSE

To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy.

STUDY TYPE

Retrospective secondary analysis of prospectively acquired clinical research data.

POPULATION

A total of 391 adults (173 men, 218 women) with known or suspected NAFLD.

FIELD STRENGTH/SEQUENCE

Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradient-recalled echo technique.

ASSESSMENT

An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average.

STATISTICAL TESTING

Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland-Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland-Altman analyses.

RESULTS

The study population's mean whole-liver PDFF was 10.1 ± 8.9% (range: 1.1-44.1%). Although there was no significant difference in average segmental (P = 0.452) or lobar (P = 0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) <1.5%, including four-ROI strategies with two ROIs from each lobe, which all had LOA <1.5%. 14/36 (39%) of two-ROI strategies and 74/84 (88%) of three-ROI strategies had ICC >0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%.

DATA CONCLUSION

Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:988-994.

Sources of systematic error in proton density fat fraction (PDFF) quantification in the liver evaluated from magnitude images with different numbers of echoes

The purpose of this work was to investigate sources of bias in magnetic resonance imaging (MRI) liver fat quantification that lead to a dependence of the proton density fat fraction (PDFF) on the number of echoes. This was a retrospective analysis of liver MRI data from 463 subjects. The magnitude signal variation with TE from spoiled gradient echo images was curve fitted to estimate the PDFF using a model that included monoexponential R₂ * decay and a multi-peak fat spectrum. Additional corrections for non-exponential decay (Gaussian), bi-exponential decay, degree of fat saturation, water frequency shift and noise bias were introduced. The fitting error was minimized with respect to 463 × 3 = 1389 subject-specific parameters and seven additional parameters associated with these corrections. The effect on PDFF was analyzed, notably the dependence on the number of echoes. The effects on R₂ * were also analyzed. The results showed that the inclusion of bias corrections resulted in an increase in the quality of fit (r² ) in 427 of 463 subjects (i.e. 92.2%) and a reduction in the total fitting error (residual norm) of 43.6%. This was largely a result of the Gaussian decay (57.8% of the reduction), fat spectrum (31.0%) and biexponential decay (8.8%) terms. The inclusion of corrections was also accompanied by a decrease in the dependence of PDFF on the number of echoes. Similar analysis of R₂ * showed a decrease in the dependence on the number of echoes. Comparison of PDFF with spectroscopy indicated excellent agreement before and after correction, but the latter exhibited lower bias on a Bland-Altman plot (1.35% versus 0.41%). In conclusion, correction for known and expected biases in PDFF quantification in liver reduces the fitting error, decreases the dependence on the number of echoes and increases the accuracy.

Effect of intravenous gadoxetate disodium and flip angle on hepatic proton density fat fraction estimation with six-echo, gradient-recalled-echo, magnitude-based MR imaging at 3T

PURPOSE

The aim of the study was to determine in patients undergoing gadoxetate disodium (Gx)-enhanced MR exams whether proton density fat fraction (PDFF) estimation accuracy of magnitude-based multi-gradient-echo MRI (MRI-M) could be improved by using high flip angle (FA) on post-contrast images.

MATERIALS AND METHODS

Thirty-one adults with known or suspected hepatic steatosis undergoing 3T clinical Gx-enhanced liver MRI were enrolled prospectively. MR spectroscopy (MRS), the reference standard, was performed before Gx to measure MRS-PDFF. Low (10°)- and high (50°)-flip angle (FA) MRI-M sequences were acquired before and during the hepatobiliary phase after Gx administration; MRI-PDFF was estimated in the MRS-PDFF voxel location. Linear regression parameters (slope, intercept, average bias, R ²) were calculated for MRS-PDFF as a function of MRI-PDFF for each MRI-M sequence (pre-Gx low-FA, pre-Gx high-FA, post-Gx low-FA, post-Gx high-FA) for all patients and for patients with MRS-PDFF <10%. Regression parameters were compared (Bonferroni-adjusted bootstrap-based tests).

RESULTS

Three of the four MRI-M sequences (pre-Gx low-FA, post-Gx low-FA, post-Gx high-FA) provided relatively unbiased PDFF estimates overall and in the low-PDFF range, with regression slopes close to 1 and intercepts and biases close to zero. Pre-Gx high-FA MRI overestimated PDFF in proportion to MRS-PDFF, with slopes of 0.72 (overall) and 0.63 (low-PDFF range). Based on regression bias closest to 0, the post-Gx high-FA sequence was the most accurate overall and in the low-PDFF range. This sequence provided statistically significant improvements in at least two regression parameters compared to every other sequence.

CONCLUSION

In patients undergoing Gx-enhanced MR exams, PDFF estimation accuracy of MRI-M can be improved by using high-FA on post-contrast images.

Accuracy of PDFF estimation by magnitude-based and complex-based MRI in children with MR spectroscopy as a reference

PURPOSE

To assess and compare the accuracy of magnitude-based magnetic resonance imaging (MRI-M) and complex-based MRI (MRI-C) for estimating hepatic proton density fat fraction (PDFF) in children, using MR spectroscopy (MRS) as the reference standard. A secondary aim was to assess the agreement between MRI-M and MRI-C.

MATERIALS AND METHODS

This was a HIPAA-compliant, retrospective analysis of data collected in children enrolled in prospective, Institutional Review Board (IRB)-approved studies between 2012 and 2014. Informed consent was obtained from 200 children (ages 8-19 years) who subsequently underwent 3T MR exams that included MRI-M, MRI-C, and T₁ -independent, T₂ -corrected, single-voxel stimulated echo acquisition mode (STEAM) MRS. Both MRI methods acquired six echoes at low flip angles. T2*-corrected PDFF parametric maps were generated. PDFF values were recorded from regions of interest (ROIs) drawn on the maps in each of the nine Couinaud segments and three ROIs colocalized to the MRS voxel location. Regression analyses assessing agreement with MRS were performed to evaluate the accuracy of each MRI method, and Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to assess agreement between the MRI methods.

RESULTS

MRI-M and MRI-C PDFF were accurate relative to the colocalized MRS reference standard, with regression intercepts of 0.63% and -0.07%, slopes of 0.998 and 0.975, and proportion-of-explained-variance values (R² ) of 0.982 and 0.979, respectively. For individual Couinaud segments and for the whole liver averages, Bland-Altman biases between MRI-M and MRI-C were small (ranging from 0.04 to 1.11%) and ICCs were high (≥0.978).

CONCLUSION

Both MRI-M and MRI-C accurately estimated hepatic PDFF in children, and high intermethod agreement was observed.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1641-1647.

Agreement between region-of-interest- and parametric map-based hepatic proton density fat fraction estimation in adults with chronic liver disease

PURPOSE

To compare agreement between region-of-interest (ROI)- and parametric map-based methods of hepatic proton density fat fraction (PDFF) estimation in adults with known or suspected hepatic steatosis secondary to chronic liver disease over a range of imaging and analysis conditions.

MATERIALS AND METHODS

In this IRB approved HIPAA compliant prospective single-site study, 31 adults with chronic liver disease undergoing clinical gadoxetic acid-enhanced liver magnetic resonance imaging at 3 T were recruited. Multi-echo gradient-echo imaging at flip angles of 10° and 50° was performed before and after administration of gadoxetic acid. Six echoes were acquired at successive nominally out-of-phase and in-phase echo times. PDFF was estimated with a nonlinear fitting algorithm using the first two, three, four, five, and (all) six echoes. Hence, 20 different imaging and analysis conditions were used (pre/post contrast x low/high flip angle x 2/3/4/5/6 echoes). For each condition, PDFF estimation was done in corresponding liver locations using two methods: a region-of-interest (ROI)-based method in which mean signal intensity values within ROIs were run through the fitting algorithm, and a parametric map-based method in which individual signal intensities were run through the fitting algorithm pixel by pixel. Agreement between ROI- and map-based PDFF estimation was assessed by Bland-Altman and intraclass correlation (ICC) analysis.

RESULTS

Depending on the condition and method, PDFF ranged from -2.52% to 45.57%. Over all conditions, mean differences between ROI- and map-based PDFF estimates ranged from 0.04% to 0.24%, with all ICCs ≥0.999.

CONCLUSION

Agreement between ROI- and parametric map-based PDFF estimation is excellent over a wide range of imaging and analysis conditions.

The effects of concomitant gradients on chemical shift encoded MRI

PURPOSE

The purpose of this work was to characterize the effects of concomitant gradients (CGs) on chemical shift encoded (CSE)-based estimation of B₀ field map, proton density fat fraction (PDFF), and R2*.

THEORY

A theoretical framework was used to determine the effects of CG-induced phase errors on CSE-MRI data.

METHODS

Simulations, phantom experiments, and in vivo experiments were conducted at 3 Tesla to assess the effects of CGs on quantitative CSE-MRI techniques. Correction of phase errors attributable to CGs was also investigated to determine whether these effects could be removed.

RESULTS

Phase errors attributed to CGs introduce errors in the estimation of B₀ field map, PDFF, and R2*. Phantom and in vivo experiments demonstrated that CGs can introduce estimation errors greater than 30 Hz in the B₀ field map, 10% in PDFF, and 16 s^-1 in R2*, 16 cm off isocenter. However, CG phase correction before parameter estimation was able to reduce estimation errors to less than 10 Hz in the B₀ field map, 1% in PDFF, and 2 s^-1 in R2*.

CONCLUSION

CG effects can impact CSE-MRI, leading to inaccurate estimation of B₀ field map, PDFF, and R2*. However, correction for phase errors caused by CGs improve the accuracy of quantitative parameters estimated from CSE-MRI acquisitions. Magn Reson Med 78:730-738, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Intra- and inter-examination repeatability of magnetic resonance spectroscopy, magnitude-based MRI, and complex-based MRI for estimation of hepatic proton density fat fraction in overweight and obese children and adults. ACTA ACUST UNITED AC. 2015;40:3070-3077. [PMID: 26350282 DOI: 10.1007/s00261-015-0542-5]

PURPOSE

Determine intra- and inter-examination repeatability of magnitude-based magnetic resonance imaging (MRI-M), complex-based magnetic resonance imaging (MRI-C), and magnetic resonance spectroscopy (MRS) at 3T for estimating hepatic proton density fat fraction (PDFF), and using MRS as a reference, confirm MRI-M and MRI-C accuracy.

METHODS

Twenty-nine overweight and obese pediatric (n = 20) and adult (n = 9) subjects (23 male, 6 female) underwent three same-day 3T MR examinations. In each examination MRI-M, MRI-C, and single-voxel MRS were acquired three times. For each MRI acquisition, hepatic PDFF was estimated at the MRS voxel location. Intra- and inter-examination repeatability were assessed by computing standard deviations (SDs) and intra-class correlation coefficients (ICCs). Aggregate SD was computed for each method as the square root of the average of first repeat variances. MRI-M and MRI-C PDFF estimation accuracy was assessed using linear regression with MRS as a reference.

RESULTS

For MRI-M, MRI-C, and MRS acquisitions, respectively, mean intra-examination SDs were 0.25%, 0.42%, and 0.49%; mean intra-examination ICCs were 0.999, 0.997, and 0.995; mean inter-examination SDs were 0.42%, 0.45%, and 0.46%; and inter-examination ICCs were 0.995, 0.992, and 0.990. Aggregate SD for each method was <0.9%. Using MRS as a reference, regression slope, intercept, average bias, and R (2), respectively, for MRI-M were 0.99%, 1.73%, 1.61%, and 0.986, and for MRI-C were 0.96%, 0.43%, 0.40%, and 0.991.

CONCLUSION

MRI-M, MRI-C, and MRS showed high intra- and inter-examination hepatic PDFF estimation repeatability in overweight and obese subjects. Longitudinal hepatic PDFF change >1.8% (twice the maximum aggregate SD) may represent real change rather than measurement imprecision. Further research is needed to assess whether examinations performed on different days or with different MR technologists affect repeatability of MRS voxel placement and MRS-based PDFF measurements.

Feasibility of and agreement between MR imaging and spectroscopic estimation of hepatic proton density fat fraction in children with known or suspected nonalcoholic fatty liver disease

PURPOSE

To assess feasibility of and agreement between magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) for estimating hepatic proton density fat fraction (PDFF) in children with known or suspected nonalcoholic fatty liver disease (NAFLD).

MATERIALS AND METHODS

Children were included in this study from two previous research studies in each of which three MRI and three MRS acquisitions were obtained. Sequence acceptability, and MRI- and MRS-estimated PDFF were evaluated. Agreement of MRI- with MRS-estimated hepatic PDFF was assessed by linear regression and Bland-Altman analysis. Age, sex, BMI-Z score, acquisition time, and artifact score effects on MRI- and MRS-estimated PDFF agreement were assessed by multiple linear regression.

RESULTS

Eighty-six children (61 boys and 25 girls) were included in this study. Slope and intercept from regressing MRS-PDFF on MRI-PDFF were 0.969 and 1.591%, respectively, and the Bland-Altman bias and 95% limits of agreement were 1.17% ± 2.61%. MRI motion artifact score was higher in boys than girls (by 0.21, p = 0.021). Higher BMI-Z score was associated with lower agreement between MRS and MRI (p = 0.045).

CONCLUSION

Hepatic PDFF estimation by both MRI and MRS is feasible, and MRI- and MRS-estimated PDFF agree closely in children with known or suspected NAFLD.

Accuracy of multiecho magnitude-based MRI (M-MRI) for estimation of hepatic proton density fat fraction (PDFF) in children

PURPOSE

To assess accuracy of magnitude-based magnetic resonance imaging (M-MRI) in children to estimate hepatic proton density fat fraction (PDFF) using two to six echoes, with magnetic resonance spectroscopy (MRS) -measured PDFF as a reference standard.

METHODS

This was an IRB-approved, HIPAA-compliant, single-center, cross-sectional, retrospective analysis of data collected prospectively between 2008 and 2013 in children with known or suspected nonalcoholic fatty liver disease (NAFLD). Two hundred eighty-six children (8-20 [mean 14.2 ± 2.5] years; 182 boys) underwent same-day MRS and M-MRI. Unenhanced two-dimensional axial spoiled gradient-recalled-echo images at six echo times were obtained at 3T after a single low-flip-angle (10°) excitation with ≥ 120-ms recovery time. Hepatic PDFF was estimated using the first two, three, four, five, and all six echoes. For each number of echoes, accuracy of M-MRI to estimate PDFF was assessed by linear regression with MRS-PDFF as reference standard. Accuracy metrics were regression intercept, slope, average bias, and R(2) .

RESULTS

MRS-PDFF ranged from 0.2-40.4% (mean 13.1 ± 9.8%). Using three to six echoes, regression intercept, slope, and average bias were 0.46-0.96%, 0.99-1.01, and 0.57-0.89%, respectively. Using two echoes, these values were 2.98%, 0.97, and 2.72%, respectively. R(2) ranged 0.98-0.99 for all methods.

CONCLUSION

Using three to six echoes, M-MRI has high accuracy for hepatic PDFF estimation in children.

Inter-examination precision of magnitude-based MRI for estimation of segmental hepatic proton density fat fraction in obese subjects

PURPOSE

To prospectively describe magnitude-based multi-echo gradient-echo hepatic proton density fat fraction (PDFF) inter-examination precision at 3 Tesla (T).

MATERIALS AND METHODS

In this prospective, Institutional Review Board-approved, Health Insurance Portability and Accountability Act (HIPAA) compliant study, written informed consent was obtained from 29 subjects (body mass indexes > 30 kg/m2). Three 3T MRI examinations were obtained over 75-90 min. Segmental, lobar, and whole liver PDFF were estimated (using three, four, five, or six echoes) by magnitude-based multi-echo MRI in colocalized regions of interest. For estimate (using three, four, five, or six echoes), at each anatomic level (segmental, lobar, whole liver), three inter-examination precision metrics were computed: intra-class correlation coefficient (ICC), standard deviation (SD), and range.

RESULTS

Magnitude-based PDFF estimates using each reconstruction method showed excellent inter-examination precision for each segment (ICC ≥ 0.992; SD ≤ 0.66%; range ≤ 1.24%), lobe (ICC ≥ 0.998; SD ≤ 0.34%; range ≤ 0.64%), and the whole liver (ICC = 0.999; SD ≤ 0.24%; range ≤ 0.45%). Inter-examination precision was unaffected by whether PDFF was estimated using three, four, five, or six echoes.

CONCLUSION

Magnitude-based PDFF estimation shows high inter-examination precision at segmental, lobar, and whole liver anatomic levels, supporting its use in clinical care or clinical trials. The results of this study suggest that longitudinal hepatic PDFF change greater than 1.6% is likely to represent signal rather than noise.