Noise properties of proton density fat fraction estimated using chemical shift-encoded MRI

Simultaneous 3D T 1 $$ {\mathrm{T}}_1 $$ , T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction mapping with respiratory-motion correction for comprehensive liver tissue characterization at 0.55 T

PURPOSE

To develop a framework for simultaneous three-dimensional (3D) mapping ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction in the liver at 0.55 T.

METHODS

The proposed sequence acquires four interleaved 3D volumes with a two-echo Dixon readout.T 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ are encoded into each volume via preparation modules, and dictionary matching allows simultaneous estimation ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , andM 0 $$ {M}_0 $$ for water and fat separately. 2D image navigators permit respiratory binning, and motion fields from nonrigid registration between bins are used in a nonrigid respiratory-motion-corrected reconstruction, enabling 100% scan efficiency from a free-breathing acquisition. The integrated nature of the framework ensures the resulting maps are always co-registered.

RESULTS

T 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction measurements in phantoms correlated strongly (adjustedr 2 > 0 . 98 $$ {r}^2>0.98 $$ ) with reference measurements. Mean liver tissue parameter values in 10 healthy volunteers were427 ± 22 $$ 427\pm 22 $$ ,47 . 7 ± 3 . 3   ms $$ 47.7\pm 3.3\;\mathrm{ms} $$ , and7 ± 2 % $$ 7\pm 2\% $$ forT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction, giving biases of71 $$ 71 $$ ,- 30 . 0   ms $$ -30.0\;\mathrm{ms} $$ , and- 5 $$ -5 $$ percentage points, respectively, when compared to conventional methods.

CONCLUSION

A novel sequence for comprehensive characterization of liver tissue at 0.55 T was developed. The sequence provides co-registered 3DT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction maps with full coverage of the liver, from a single nine-and-a-half-minute free-breathing scan. Further development is needed to achieve accurate proton-density fat fraction (PDFF) estimation in vivo.

Accelerated Chemical Shift Encoded Cardiac MRI with Use of Resolution Enhancement Network

BACKGROUND

Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (FastCSE) to accelerate CSE.

METHODS

FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5mm2, 2.5 × 1.5 mm2, and 3.8 × 1.9mm2 resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5mm2 and 3.8 × 1.9 mm2 to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with 5-way analysis of variance.

RESULTS

FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm², reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm², from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm2 (0.31 ± 0.03 vs. 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm2 (0.31 ± 0.03 vs. 0.42 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm² resolution (0.32 ±0.03 vs. 0.31 ± 0.03, P = 0.78; 0.32 ± 0.03 vs. 0.31 ± 0.03, P = 0.90).

CONCLUSION

We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

Application and research progress of magnetic resonance proton density fat fraction in metabolic dysfunction-associated steatotic liver disease: a comprehensive review

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), is a chronic liver disorder associated with disturbances in lipid metabolism. The disease is prevalent worldwide, particularly closely linked with metabolic syndromes such as obesity and diabetes. Magnetic Resonance Proton Density Fat Fraction (MRI-PDFF), serving as a non-invasive and highly quantitative imaging assessment tool, holds promising applications in the diagnosis and research of MASLD. This paper aims to comprehensively review and summarize the applications and research progress of MRI-PDFF technology in MASLD, analyze its strengths and challenges, and anticipate its future developments in clinical practice.

Comparison of Vendor-Independent Software Tools for Liver Proton Density Fat Fraction Estimation at 1.5 T

(1) Background: Open-source software tools are available to estimate proton density fat fraction (PDFF). (2) Methods: We compared four algorithms: complex-based with graph cut (GC), magnitude-based (MAG), magnitude-only estimation with Rician noise modeling (MAG-R), and multi-scale quadratic pseudo-Boolean optimization with graph cut (QPBO). The accuracy and reliability of the methods were evaluated in phantoms with known fat/water ratios and a patient cohort with various grades (S0-S3) of steatosis. Image acquisitions were performed at 1.5 Tesla (T). (3) Results: The PDFF estimates showed a nearly perfect correlation (Pearson r = 0.999, p < 0.001) and inter-rater agreement (ICC = from 0.995 to 0.999, p < 0.001) with true fat fractions. The absolute bias was low with all methods (0.001-1%), and an ANCOVA detected no significant difference between the algorithms in vitro. The agreement across the methods was very good in the patient cohort (ICC = 0.891, p < 0.001). However, MAG estimates (-2.30% ± 6.11%, p = 0.005) were lower than MAG-R. The field inhomogeneity artifacts were most frequent in MAG-R (70%) and GC (39%) and absent in QPBO images. (4) Conclusions: The tested algorithms all accurately estimate PDFF in vitro. Meanwhile, QPBO is the least affected by field inhomogeneity artifacts in vivo.

Confidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver

PURPOSE

The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) andR 2 * $$ {R}_2^{\ast } $$ maps of the liver, generated with chemical shift-encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms.

METHODS

Confidence maps for both PDFF andR 2 * $$ {R}_2^{\ast } $$ maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF andR 2 * $$ {R}_2^{\ast } $$ analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements.

RESULTS

Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF andR 2 * $$ {R}_2^{\ast } $$ maps, as identified by confidence maps.

CONCLUSION

A proposed confidence map algorithm that identifies reliable areas of PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease

Background Standardized manual region of interest (ROI) sampling strategies for hepatic MRI steatosis and iron quantification are time consuming, with variable results. Purpose To evaluate the performance of automatic MRI whole-liver segmentation (WLS) for proton density fat fraction (PDFF) and iron estimation (transverse relaxometry [R2*]) versus manual ROI, with liver biopsy as the reference standard. Materials and Methods This prospective, cross-sectional, multicenter study recruited participants with chronic liver disease who underwent liver biopsy and chemical shift-encoded 3.0-T MRI between January 2017 and January 2021. Biopsy evaluation included histologic grading and digital pathology. MRI liver sampling strategies included manual ROI (two observers) and automatic whole-liver (deep learning algorithm) segmentation for PDFF- and R2*-derived measurements. Agreements between segmentation methods were measured using intraclass correlation coefficients (ICCs), and biases were evaluated using Bland-Altman analyses. Linear regression analyses were performed to determine the correlation between measurements and digital pathology. Results A total of 165 participants were included (mean age ± standard deviation, 55 years ± 12; 96 women; 101 of 165 participants [61%] with nonalcoholic fatty liver disease). Agreements between mean measurements were excellent, with ICCs of 0.98 for both PDFF and R2*. The median bias was 0.5% (interquartile range, -0.4% to 1.2%) for PDFF and 2.7 sec^-1 (interquartile range, 0.2-5.3 sec^-1) for R2* (P < .001 for both). Margins of error were lower for WLS than ROI-derived parameters (-0.03% for PDFF and -0.3 sec^-1 for R2*). ROI and WLS showed similar performance for steatosis (ROI AUC, 0.96; WLS AUC, 0.97; P = .53) and iron overload (ROI AUC, 0.85; WLS AUC, 0.83; P = .09). Correlations with digital pathology were high (P < .001) between the fat ratio and PDFF (ROI r = 0.89; WLS r = 0.90) and moderate (P < .001) between the iron ratio and R2* (ROI r = 0.65; WLS r = 0.64). Conclusion Proton density fat fraction and transverse relaxometry measurements derived from MRI automatic whole-liver segmentation (WLS) were accurate for steatosis and iron grading in chronic liver disease and correlated with digital pathology. Automated WLS estimations were higher, with a lower margin of error than manual region of interest estimations. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moura Cunha and Fowler in this issue.

Quantification of Liver Fat Content with CT and MRI: State of the Art

Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation.

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.

Impact of iron deposit on the accuracy of quantifying liver fat fraction using multi-material decomposition algorithm in dual-energy spectral computed tomography

Objectives

To investigate the accuracy of using multi‐material decomposition (MMD) algorithm in dual‐energy spectral computed tomography (CT) for quantifying fat fraction (FF) in the presence of iron.

Materials

Nine tubes with various proportions of fat and iron were prepared. FF were divided into three levels (10%, 20%, and 30%), recorded as references (FF_ref). Iron concentrations (in mg/100 g) were divided into three ranges (25.25–25.97, 50.38–51.55 and 75.57–77.72). The nine‐tube phantom underwent dual‐energy CT and MR. CT attenuation was measured and FF were determined using MMD in CT (FF_CT) and Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation (IDEAL‐IQ) in MR (FF_MR) for each tube. Statistical analyses used were: Spearman rank correlation for correlations between FF_ref and CT attenuation, FF_CT, and FF_MR; one‐way ANOVA, and one‐sample t‐test for the differences between FF_CT and FF_ref and between FF_MR and FF_ref. A multivariate linear regression model was established to analyze the differences between the corresponding values with different iron concentrations under the same FF_ref.

Results

Fat fraction on CT (FFCT) and FF_MR were positively correlated with FF_ref (all p < 0.001), while the CT attenuation was negatively correlated with FF_ref in the three iron concentration ranges. For a given FF_ref, FF_CT decreased and FF_MR increased as the iron concentration increased. The mean difference between FF_CT and FF_ref over the nine tube measurements was 0.25 ± 2.45%, 5.7% lower the 5.98 ± 3.33% value between FF_MR and FF_ref (F = 310.017, p < 0.01).

Conclusion

The phantom results indicate that MMD in dual‐energy CT can directly quantify volumetric FF and is less affected by iron concentration than MR IDEAL‐IQ method.

Water-fat magnetic resonance imaging of adipose tissue compartments in the normal third trimester fetus

BACKGROUND

Assessment of fetal adipose tissue gives information about the future metabolic health of an individual, with evidence that the development of this tissue has regional heterogeneity.

OBJECTIVE

To assess differences in the proton density fat fraction (PDFF) between fetal adipose tissue compartments in the third trimester using water-fat magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Water-fat MRI was performed in a 1.5-T scanner. Fetal adipose tissue was segmented into cheeks, thorax, abdomen, upper arms, forearms, thighs and lower legs. PDFF and R2* values were measured in each compartment.

RESULTS

Twenty-eight women with singleton pregnancies were imaged between 28 and 38 weeks of gestation. At 30 weeks' gestation (n=22), the PDFF was statistically different between the compartments (P<0.0001), with the highest PDFF in cheeks, followed by upper arms, thorax, thighs, forearms, lower legs and abdomen. There were no statistical differences in the rate of PDFF change with gestational age between the white adipose tissue compartments (P=0.97). Perirenal brown adipose tissue had a different PDFF and R2* compared to white adipose tissue, while the rate of R2* change did not significantly change with gestational age between white adipose tissue compartments (P=0.96).

CONCLUSION

Fetal adipose tissue accumulates lipids at a similar rate in all white adipose tissue compartments. PDFF variances between the compartments suggest that accumulation begins at different gestational ages, starting with cheeks, followed by extremities, trunk and abdomen. Additionally, MRI was able to detect differences in the PDFF between fetal brown adipose tissue and white adipose tissue.

Long-term inter-platform reproducibility, bias, and linearity of commercial PDFF MRI methods for fat quantification: a multi-center, multi-vendor phantom study

OBJECTIVES

Proton density fat fraction (PDFF) is a validated biomarker of tissue fat quantification. However, validation has been limited to single-center or multi-center series using non-FDA-approved software. Thus, we assess the bias, linearity, and long-term reproducibility of PDFF obtained using commercial PDFF packages from several vendors.

METHODS

Over 35 months, 438 subjects and 16 volunteers from a multi-center observational trial underwent PDFF MRI measurements using a 3-T MR system from one of three different vendors or a 1.5-T system from one vendor. Fat-water phantom sets were measured as part of each subject's examination. Manual region-of-interest measurements on the %fat image, then cross-sectional bias, linearity, and long-term reproducibility were assessed.

RESULTS

Three hundred ninety-two phantom measurements were evaluable (90%). Bias ranged from 2.4 to - 3.8% for the lowest to the highest weight %fat. Regression fits of PDFF against synthesis weight %fat showed negligible non-linear effects and a linear slope of 0.94 (95% confidence interval: 0.938, 0.947). We observed significant vendor (p < 0.001) and field strength (p < 0.001) differences in bias and longitudinal variability. When the results were pooled across sites, vendors, and field strengths, the estimated reproducibility coefficient was 6.93% (95% CI: 6.25%, 7.81%).

CONCLUSIONS

This study demonstrated good linearity, accuracy, and reproducibility for all investigated manufacturers and field strengths. However, significant vendor-dependent and field strength-dependent bias were found. While longitudinal PDFF measurements may be made using different field strength or vendor MR systems, if the MR system is not the same, based on these results, only PDFF changes ≥ 7% can be considered a true difference.

KEY POINTS

• Phantom fat fraction (PDFF) MRI measurements over 35 months demonstrated good linearity, accuracy, and reproducibility for the vendor systems investigated. • Non-linear effects were negligible (linear slope of 0.94) over 0-100% fat; however, significant vendor (p < 0.001) and field strength (p<0.001) differences in bias and longitudinal variability were identified. Bias ranged from 2.4 to - 3.8% for 0-100 weight% fat, respectively. • Measurement bias could affect the accuracy of PDFF in clinical use. As the reproducibility coefficient was 6.93%, only greater changes in % fat can be considered true differences when making longitudinal PDFF measurements on different MR systems.

Evaluation of hepatic steatosis before liver transplantation in ex vivo by volumetric quantitative PDFF-MRI

PURPOSE

Over the last two decades, extended criteria have promoted an increased number of donor livers available for liver transplantation. But posttransplant graft loss is still a major concern. Macrovesicular hepatic steatosis (MHS) is recognized as the most significant prognostic histologic parameter in predicting posttransplant graft loss. We aimed to evaluate the utility of ex vivo volumetric quantitative MRI for quantifying MHS before liver transplantation using proton density fat-fraction (PDFF-MRI) histogram analysis.

METHODS

PDFF-MRI was performed at 3.0T in 40 livers. We obtained histogram parameters of whole-liver volume of interest, including the mean, median, 5th, 10th, 25th, 75th, 90th, and 95th percentile PDFF; skewness; kurtosis; entropy; and volume.

RESULTS

Livers from 40 cadaveric donors were included, and histologic ex vivo fat quantification was available for 33 livers. Ten livers had MHS and 23 had normal fat content. The MHS group had higher mean, median, 5th, 10th, 25th, 75th, 90th, and 95th percentile PDFF, and entropy than the group with normal fat content (P < .05). Median PDFF had greater area under the curve value than other parameters. Mean PDFF showed an excellent correlation with entropy and a moderate correlation with MHS quantification on histology.

CONCLUSIONS

Ex vivo volumetric quantitative PDFF-MRI histogram analysis is a very useful and noninvasive method to detect MHS before liver transplantation. Median PDFF was the best predictor of the presence of MHS. Entropy is a very promising parameter.

Liver fat quantification: where do we stand?

Excessive intracellular accumulation of triglycerides in the liver, or hepatic steatosis, is a highly prevalent condition affecting approximately one billion people worldwide. In the absence of secondary cause, the term nonalcoholic fatty liver disease (NAFLD) is used. Hepatic steatosis may progress into nonalcoholic steatohepatitis, the more aggressive form of NAFLD, associated with hepatic complications such as fibrosis, liver failure and hepatocellular carcinoma. Hepatic steatosis is associated with metabolic syndrome, cardiovascular disease and represents an independent risk factor for type 2 diabetes, cardiovascular disease and malignancy. Percutaneous liver biopsy is the current reference standard for NAFLD assessment; however, it is an invasive procedure associated with complications and suffers from high sampling variability, impractical for clinical routine and drug efficiency studies. Therefore, noninvasive imaging methods are increasingly used for the diagnosis and monitoring of NAFLD. Among the methods quantifying liver fat, chemical-shift-encoded MRI (CSE-MRI)-based proton density fat-fraction (PDFF) has shown the most promise. MRI-PDFF is increasingly accepted as quantitative imaging biomarker of liver fat that is transforming daily clinical practice and influencing the development of new treatments for NAFLD. Furthermore, CT is an important imaging method for detection of incidental steatosis, and the practical advantages of quantitative ultrasound hold great promise for the future. Understanding the disease burden of NAFLD and the role of imaging may initiate important interventions aimed at avoiding the hepatic and extrahepatic complications of NAFLD. This article reviews clinical burden of NAFLD, and the role of noninvasive imaging techniques for quantification of liver fat.

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.

Constraints in estimating the proton density fat fraction

The study evaluates four physically motivated constraints in the estimation of the proton density fat fraction (PDFF). Least squares approaches were developed for constraining the parameters in PDFF quantification based on the physics of magnetic resonance imaging. These were smooth fieldmap, smooth initial phase, nonnegative proton density and moderate R₂^∗ values. The constraints were evaluated in terms of their influence on the bias and standard deviation of the estimated parameters using numerical simulations and in vivo data acquired at 0.35 T. Results show that unconstrained least squares estimation is noisy and biased and that constraints can be effective at reducing both the standard deviation and bias.

Measurement of spleen fat on MRI-proton density fat fraction arises from reconstruction of noise

PURPOSE

This study compares splenic proton density fat fraction (PDFF) measured using confounder-corrected chemical shift-encoded (CSE)-MRI to magnetic resonance spectroscopy (MRS) in human patients at 3T.

METHODS

This was a prospectively designed ancillary study to various previously described single-center studies performed in adults and children with known or suspected nonalcoholic fatty liver disease. Patients underwent magnitude-based MRI (MRI-M), complex-based MRI (MRI-C), high signal-to-noise variants (Hi-SNR MRI-M and Hi-SNR MRI-C), and MRS at 3T for spleen PDFF estimation. PDFF from CSE-MRI methods were compared to MRS-PDFF using Wilcoxon signed-rank tests. Demographics were summarized descriptively. Spearman's rank correlations were computed pairwise between CSE-MRI methods. Individual patient measurements were plotted for qualitative assessment. A significance level of 0.05 was used.

RESULTS

Forty-seven patients (20 female, 27 male) including 12 adults (median 55 years old) and 35 children (median 12 years old). Median PDFF estimated by MRS, MRI-M, Hi-SNR MRI-M, MRI-C, and Hi-SNR MRI-C was 1.0, 2.3, 1.9, 2.2, and 2.0%. The four CSE-MRI methods estimated statistically significant higher spleen PDFF values compared to MRS (p < 0.0001 for all). Pairwise associations in spleen PDFF values measured by different CSE-MRI methods were weak, with the highest Spearman's rank correlations being 0.295 between MRI-M and Hi-SNR MRI-M; none were significant after correction for multiple comparisons. No qualitative relationship was observed between PDFF measurements among the various methods.

CONCLUSION

Overestimation of PDFF by CSE-MRI compared to MRS and poor agreement between related CSE-MRI methods suggest that non-zero PDFF values in human spleen are artifactual.

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.

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.