Fat Fraction Estimation Research Articles

290 - Simultaneous Estimation of Bone Morphology and Fat Fraction Using General-purpose Magnetic Resonance Imaging

290 - Simultaneous Estimation of Bone Morphology and Fat Fraction Using General-purpose Magnetic Resonance Imaging

Simultaneous 3D , , and fat-signal-fraction mapping with respiratory-motion correction for comprehensive liver tissue characterization at 0.55 T.

To develop a framework for simultaneous three-dimensional (3D) mapping of , , and fat signal fraction in the liver at 0.55 T. The proposed sequence acquires four interleaved 3D volumes with a two-echo Dixon readout. and are encoded into each volume via preparation modules, and dictionary matching allows simultaneous estimation of , , and 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. , , and fat-signal-fraction measurements in phantoms correlated strongly (adjusted ) with reference measurements. Mean liver tissue parameter values in 10 healthy volunteers were , , and for , , and fat signal fraction, giving biases of , , and percentage points, respectively, when compared to conventional methods. A novel sequence for comprehensive characterization of liver tissue at 0.55 T was developed. The sequence provides co-registered 3D , , 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.

Validation of quantitative magnetic resonance imaging techniques in head and neck healthy structures involved in the salivary and swallowing function: Accuracy and repeatability

Background and PurposeRadiation-induced damage to the organs at risk (OARs) in head-and-neck cancer (HNC) patient can result in long-term complications. Quantitative magnetic resonance imaging (qMRI) techniques such as diffusion-weighted imaging (DWI), DIXON for fat fraction (FF) estimation and T2 mapping could potentially provide a spatial assessment of such damage. The goal of this study is to validate these qMRI techniques in terms of accuracy in phantoms and repeatability in-vivo across a broad selection of healthy OARs in the HN region. Materials and MethodsScanning was performed at a 3 T diagnostic MRI scanner, including the calculation of apparent diffusion coefficient (ADC) from DWI, FF and T2 maps. Phantoms were scanned to estimate the qMRI techniques bias using Bland-Altman statistics. Twenty-six healthy subjects were scanned twice in a test–retest study to determine repeatability. Repeatability coefficients (RC) were calculated for the parotid, submandibular, sublingual and tubarial salivary glands, oral cavity, pharyngeal constrictor muscle and brainstem. Additionally, a linear mixed-effect model analysis was used to evaluate the effect of subject-specific characteristics on the qMRI values. ResultsBias was 0.009x10-3 mm2/s for ADC, -0.7 % for FF and -7.9 ms for T2. RCs ranged 0.11–0.25x10-3 mm2/s for ADC, 1.2–6.3 % for FF and 2.5–6.3 ms for T2. A significant positive linear relationship between age and the FF and T2 for some of the OARs was found. ConclusionThese qMRI techniques are feasible, accurate and repeatable, which is promising for treatment response monitoring and/or differentiating between healthy and unhealthy tissues due to radiation-induced damage in HNC patients.

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

Background and objectiveMagnetic resonance imaging (MRI) has emerged as a noninvasive clinical tool for assessment of hepatic steatosis. Multi-spectral fat-water MRI models, incorporating single or dual transverse relaxation decay rate(s) (R2*) have been proposed for accurate fat fraction (FF) estimation. However, it is still unclear whether single- or dual-R2* model accurately mimics in vivo signal decay for precise FF estimation and the impact of signal-to-noise ratio (SNR) on each model performance. Hence, this study aims to construct virtual steatosis models and synthesize MRI signals with different SNRs to systematically evaluate the accuracy of single- and dual-R2* models for FF and R2* estimations at 1.5T and 3.0T. MethodsRealistic hepatic steatosis models encompassing clinical FF range (0–60 %) were created using morphological features of fat droplets (FDs) extracted from human liver biopsy samples. MRI signals were synthesized using Monte Carlo simulations for noise-free (SNRideal) and varying SNR conditions (5–100). Fat-water phantoms were scanned with different SNRs to validate simulation results. Fat water toolbox was used to calculate R2* and FF for both single- and dual-R2* models. The model accuracies in R2* and FF estimates were analyzed using linear regression, bias plot and heatmap analysis. ResultsThe virtual steatosis model closely mimicked in vivo fat morphology and Monte Carlo simulation produced realistic MRI signals. For SNRideal and moderate-high SNRs, water R2* (R2*W) by dual-R2* and common R2* (R2*com) by single-R2* model showed an excellent agreement with slope close to unity (0.95–1.01) and R2 > 0.98 at both 1.5T and 3.0T. In simulations, the R2*com-FF and R2*W-FF relationships exhibited slopes similar to in vivo calibrations, confirming the accuracy of our virtual models. For SNRideal, fat R2* (R2*F) was similar to R2*W and dual-R2* model showed slightly higher accuracy in FF estimation. However, in the presence of noise, dual-R2* produced higher FF bias with decreasing SNR, while leading to only marginal improvement for high SNRs and in regions dominated by fat and water. In contrast, single-R2* model was robust and produced accurate FF estimations in simulations and phantom scans with clinical SNRs. ConclusionOur study demonstrates the feasibility of creating virtual steatosis models and generating MRI signals that mimic in vivo morphology and signal behavior. The single-R2* model consistently produced lower FF bias for clinical SNRs across entire FF range compared to dual-R2* model, hence signifying that single-R2* model is optimal for assessing hepatic steatosis.

Comparison of the accuracy of commercial two-point and multi-echo Dixon MRI for quantification of fat in liver, paravertebral muscles, and vertebral bone marrow

PurposeExcess fat accumulation contributes significantly to metabolic dysfunction and diseases. This study aims to systematically compare the accuracy of commercially available Dixon techniques for quantification of fat fraction in liver, skeletal musculature, and vertebral bone marrow (BM) of healthy individuals, investigating biases and sex-specific influences. Method100 healthy White individuals (50 women) underwent abdominal MRI using two-point and multi-echo Dixon sequences. Fat fraction (FF), proton density fat fraction (PDFF) and T2* values were calculated for liver, paravertebral muscles (PVM) and vertebral BM (Th8–L5). Agreement and systematic deviations were assessed using linear correlation and Bland-Altman plots. ResultsHigh correlations between FF and PDFF were observed in liver (r = 0.98 for women; r = 0.96 for men), PVM (r = 0.92 for women; r = 0.93 for men) and BM (r = 0.97 for women; r = 0.95 for men). Relative deviations between FF and PDFF in liver (18.92 % for women; 13.32 % for men) and PVM (1.96 % for women; 11.62 % for men) were not significant. Relative deviations in BM were significant (38.13 % for women; 27.62 % for men). Bias correction using linear models reduced discrepancies. T2* times were significantly shorter in BM (8.72 ms for women; 7.26 ms for men) compared to PVM (13.45 ms for women; 13.62 ms for men) and liver (29.47 ms for women; 26.35 ms for men). ConclusionWhile no significant differences were observed for liver and PVM, systematic errors in BM FF estimation using two-point Dixon imaging were observed. These discrepancies – mainly resulting from organ-specific T2* times – have to be considered when applying two-point Dixon approaches for assessment of fat content. As suitable correction tools, linear models could provide added value in large-scale epidemiological cohort studies. Sex-specific differences in T2* should be considered.

Confidence maps for reliable estimation of proton density fat fraction and in the liver.

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) and 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. Confidence maps for both PDFF and 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 and 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 and measurements. Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and maps, as identified by confidence maps. A proposed confidence map algorithm that identifies reliable areas of PDFF and measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.

Lipid Storage, Lipolysis, and Lipotoxicity in Obesity.

The ratio of free fatty acid (FFA) turnover decreases significantly with the expansion of white adipose tissue. Adipose tissue and dietary saturated fatty acid levels significantly correlate with an increase in fat cell size and number. The G0/G1 switch gene 2 increases lipid content in adipocytes and promotes adipocyte hypertrophy through the restriction of triglyceride (triacylglycerol: TAG) turnover. Hypoxia in obese adipose tissue due to hypertrophic adipocytes results in excess deposition of extracellular matrix (ECM) components. Cluster of differentiation (CD) 44, as the main receptor of the extracellular matrix component regulates cell-cell and cell-matrix interactions including diet-induced insulin resistance. Excess TAGs, sterols, and sterol esters are surrounded by the phospholipid monolayer surface and form lipid droplets (LDs). Once LDs are formed, they grow up because of the excessive amount of intracellular FFA stored and reach a final size. The ratio of FFA turnover/lipolysis decreases significantly with increases in the degree of obesity. Dysfunctional adipose tissue is unable to expand further to store excess dietary lipids, increased fluxes of plasma FFAs lead to ectopic fatty acid deposition and lipotoxicity. Reduced neo-adipogenesis and dysfunctional lipid-overloaded adipocytes are hallmarks of hypertrophic obesity linked to insulin resistance. Obesity-associated adipocyte death exhibits feature of necrosis-like programmed cell death. Adipocyte death is a prerequisite for the transition from hypertrophic to hyperplastic obesity. Increased adipocyte number in obesity has life-long effects on white adipose tissue mass. The positive correlation between the adipose tissue volume and magnetic resonance imaging proton density fat fraction estimation is used for characterization of the obesity phenotype, as well as the risk stratification and selection of appropriate treatment strategies. In obese patients with type 2 diabetes, visceral adipocytes exposed to chronic/intermittent hyperglycemia develop a new microRNAs' (miRNAs') expression pattern. Visceral preadipocytes memorize the effect of hyperglycemia via changes in miRNAs' expression profile and contribute to the progression of diabetic phenotype. Nonsteroidal anti-inflammatory drugs, metformin, and statins can be beneficial in treating the local or systemic consequences of white adipose tissue inflammation. Rapamycin inhibits leptin-induced LD formation. Collectively, in this chapter, the concept of adipose tissue remodeling in response to adipocyte death or adipogenesis, and the complexity of LD interactions with the other cellular organelles are reviewed. Furthermore, clinical perspective of fat cell turnover in obesity is also debated.

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined. We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength. We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.

Association between hepatic steatosis and fibrosis with measures of insulin sensitivity in patients with severe obesity and type 2 diabetes - a cross-sectional study

BackgroundObesity, non-alcoholic fatty liver disease (NAFLD) and insulin resistance are three pathological conditions highly correlated, but this relationship is not fully elucidated. Hence, we aimed to assess the association of hepatic steatosis and fibrosis with different measures of insulin sensitivity in patients with severe obesity and type 2 diabetes mellitus (T2DM).MethodsA cross-sectional study (Oseberg trial) including patients with T2DM referred for bariatric surgery at Vestfold Hospital Trust, Norway. Magnetic resonance imaging (MRI) and the enhanced liver fibrosis (ELF) test was used for estimation of liver fat fraction (LFF) and degree of fibrosis, respectively. Oral and intravenous glucose tolerance tests were applied for estimation of insulin sensitivity (HOMA2S, Matsuda ISI and MinMod SI).ResultsA total of 100 patients (mean [SD] age 47.5 [9.7] years, 65% women, BMI 42.0 [5.3] kg/m2 and 98% with metabolic syndrome) were included in the analyses. The mean (SD) LFF in the total population was 19.1 (11.5), and the mean (SD) ELF score was 8.46 (0.84), a value representing moderate fibrosis. LFF was inversely associated with HOMA2S and Matsuda ISI, and both measures were significantly higher in the no or low-grade steatosis group compared with the medium-to-high grade steatosis group (mean difference [95% CI] 5.9 [2.2-9.6]%, Cohen’s d = 0.75), and (0.7 [0.3-1.1], Cohen’s d = 0.80, respectively). There was no association between LFF, as a categorical or continuous variable, and MinMod SI. The proportions of patients with none to mild fibrosis, moderate fibrosis and severe fibrosis were 14, 78 and 6%, respectively, and there were no significant associations between level of fibrosis and measures of insulin sensitivity.ConclusionsPatients with morbid obesity and T2DM demonstrated high levels of liver fat fraction, and we showed that hepatic steatosis, but not the degree of liver fibrosis, was associated with different measures of insulin sensitivity in patients with severe obesity and T2DM. Further, our results might indicate that the LFF is primarily associated with hepatic, and not peripheral insulin sensitivity. To improve the diagnosis of NAFLD and the prediction of its progression, more studies are needed to reveal the pathological mechanistic pathways involved in NAFLD and insulin sensitivity.Trial registrationClinicalTrials.gov: NCT01778738

MRI correlates of motoneuron loss in SMA

BackgroundMagnetic resonance imaging (MRI) is currently explored as supplemental tool to monitor disease progression and treatment response in various neuromuscular disorders. We here assessed the utility of a multi-parametric magnetic resonance imaging (MRI) protocol including quantitative water T2 mapping, Dixon-based proton density fat fraction (PDFF) estimation and diffusion tensor imaging (DTI) to detect loss of spinal motor neurons and subsequent muscle damage in adult SMA patients.MethodsSixteen SMA patients and 13 age-matched controls were enrolled in this prospective, longitudinal study. All participants underwent MRI imaging including measurements of Dixon-based PDFF and DTI of the sciatic nerve. SMA patients furthermore underwent measurements of muscle water T2 (T2w) of the biceps femoris muscle (BFM) and quadriceps femoris muscle (QFM). Ten participants returned for a second scan six months later. MRI parameter were correlated with clinical data. All patients were on nusinersen treatment.ResultsThere were significantly higher intramuscular fat fractions in the BFM and QFM of SMA patients compared to healthy controls at baseline and after 6 months. Furthermore, T2 values significantly correlated positively with intramuscular fat fractions. The Hammersmith functional motor scale significantly correlated with the QFM’s intramuscular fat fractions. DTI scans of the sciatic nerve were not significantly different between the two groups.ConclusionThis study demonstrates that, water T2 mapping and Dixon-based PDFF estimation may distinguish between adult SMA patients and controls, due to massive intramuscular fat accumulation in SMA. More extensive long-term studies are warranted to further evaluate these two modalities as surrogate markers in SMA patients during treatment.

Quantification of fat fraction in subchondral bone marrow in knee osteoarthritis using Dixon MRI and image registration

To evaluate fat fraction of subchondral bone marrow oedema-like lesions (BMLs) and nearby marrow in knee osteoarthritis (OA) using Dixon MR imaging and to compare conventional volume measurements of BML change with an alternative technique using Dixon MRI and image registration. We analyzed 3T MR images from 17 symptomatic knee OA subjects (mean age: 61.3 years). BML volume was measured by manually segmenting sagittal fat-suppressed proton density (PD) weighted images at two visits. In addition, regions of interest (ROIs) from BMLs and nearby non-BML marrow were manually segmented on Dixon water images at either the initial or follow-up visit; image registration of the Dixon images at different time points was performed to allow calculation of median fat fraction from corresponding ROIs at both visits. The fat fraction of BMLs was compared with non-BML marrow using the Wilcoxon signed rank test and Spearman's correlation coefficient was used to assess the association between change in BML volume and change in fat fraction. Fat fraction within BMLs was significantly lower than that of non-BML marrow (72.5% vs 92.1%, p= 0.003). The change in volume of BMLs over time was negatively correlated with change in fat fraction (ρ= -0.58; p= 0.016). Estimation of BML fat fraction in patients with knee OA using Dixon imaging combined with image registration provides a potential alternative approach to volumetric BML assessment.

Feasibility of Dual-Energy Cone-Beam CT of Bone Marrow Edema Using Dual-Layer Flat Panel Detectors.

We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector (FPD) and three-material decomposition. A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The CBCT system featured 975 mm source-axis distance, 1,362 mm source-detector distance and a 430 × 430 mm2 dual-layer FPD (top layer: 0.20 mm CsI:Tl, bottom layer: 0.55 mm CsI:Tl; a 1 mm Cu filter between the layers to improve spectral separation). Tube settings were 120 kV (+2 mm Al, +0.2 mm Cu) and 10 mAs per exposure. The digital phantom consisted of a 160 mm water cylinder with inserts containing mixtures of water (volume fraction ranging 0.18 to 0.46) - fat (0.5 to 0.7) - Ca (0.04 to 0.12); decreasing fractions of fat indicated increasing degrees of BME. A two-stage three-material DE decomposition was applied to DE CBCT projections: first, projection-domain decomposition (PDD) into fat-aluminum basis, followed by CBCT reconstruction of intermediate base images, followed by image-domain change of basis into fat, water and bone. Sensitivity to scatter was evaluated by i) adjusting source collimation (12 to 400 mm width) and ii) subtracting various fractions of the true scatter from the projections at 400 mm collimation. The impact of spectral calibration was studied by shifting the effective beam energy (± 2 keV) when creating the PDD lookup table. We further simulated a realistic BME imaging framework, where the scatter was estimated using a fast Monte Carlo (MC) simulation from a preliminary decomposition of the object; the object was a realistic wrist phantom with an 0.85 mL BME stimulus in the radius. The decomposition is sensitive to scatter: approx. <20 mm collimation width or <10% error of scatter correction in a full field-of-view setting is needed to resolve BME. A mismatch in PDD decomposition calibration of ± 1 keV results in ~25% error in fat fraction estimates. In the wrist phantom study with MC scatter corrections, we were able to achieve ~0.79 mL true positive and ~0.06 mL false positive BME detection (compared to 0.85 mL true BME volume). Detection of BME using DE CBCT with dual-layer FPD is feasible, but requires scatter mitigation, accurate scatter estimation, and robust spectral calibration.

Addressing concomitant gradient phase errors in time-interleaved chemical shift-encoded MRI fat fraction and R2 * mapping with a pass-specific phase fitting method.

Concomitant gradients induce phase errors that increase quadratically with distance from isocenter. This work proposes a complex-based fitting method that addresses concomitant gradient phase errors in chemical shift encoded (CSE) MRI estimation of proton density fat fraction (PDFF) and R2 * through joint estimation of pass-specific phase terms. This method is applicable to time-interleaved multi-echo gradient-echo acquisitions (i.e., multi-pass acquisitions) and does not require prior knowledge of gradient waveforms typically needed to address concomitant gradient phase errors. A CSE-MRI spoiled gradient echo signal model, with pass-specific phase terms, is introduced for non-linear least squares estimation of PDFF and R2 * in the presence of concomitant gradient phase errors. Cramér-Rao lower bound analysis was used to determine noise performance tradeoffs of the proposed fitting method, which was then validated in both phantom and in vivo experiments. The proposed fitting method removed PDFF and R2 * estimation errors up to 12% and 10 s-1 , respectively, at ±12 cm off isocenter (S/I) in a water phantom. In healthy volunteers, PDFF and R2 * bias was reduced by ~10% (12 cm off-isocenter) and ~30 s-1 (16 cm off-isocenter), respectively. An evaluation in 29 clinical liver datasets demonstrated reduced PDFF bias and variability (8.4% improvement in the coefficient of variation), even with the imaging volume centered at isocenter. Concomitant gradient induced phase errors in multi-pass CSE-MRI acquisitions can result in PDFF and R2 * estimation biases away from isocenter. The proposed fitting method enables accurate PDFF and R2 * quantification in the presence of concomitant gradient phase errors without knowledge of imaging gradient waveforms.

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 and muscular fat using contrast-enhanced Dual Source Dual Energy Computed Tomography compared to an established multi-echo Dixon MRI sequence

PurposeTo investigate the feasibility of liver fat quantification in contrast-enhanced dual source dual energy computed tomography (DECT) using multi-echo Dixon magnetic resonance imaging (MRI) as reference standard. MethodPatients who underwent MRI of the liver including a multi-echo Dixon sequence for estimation of proton density fat fraction in 2017 as well as contrast-enhanced DECT imaging of the abdomen were included in this retrospective, monocentric IRB approved study. Furthermore, patients with a hepatic fat amount >5% who were examined in 2018 with MRI and DECT were included. The final study group consisted of 81 patients with 90 pairs of examinations. Analysis of parameter maps was performed manually using congruent regions of interest which were placed in the liver parenchyma, in the erector spinae muscles, and psoas major muscles. ResultsMean patient age was 61 ± 13 years. Median time between MRI and DECT was 48 days. MRI liver fat quantification resulted in a median of 3.8% (IQR: 2.2–8.2%) compared to 1.8% (IQR: 0–6.3%) in DECT (p < 0.001), with a Spearman correlation of 0.73. Bland-Altman analysis resulted in a systematic underestimation of liver fat in DECT, with a mean difference of −1.7%. Fat quantification in the erector spinae muscles (p = 0.257) and the psoas major muscles (p = 0.208) was not significantly different in DECT compared to MRI. ConclusionsLiver and muscular fat quantification in portal-venous phase DECT is feasible with good to excellent correlation compared to a multi-echo Dixon MRI sequence analysis. While there is an underestimation of the liver fat content in DECT, there are no significant differences between DECT and MRI fat quantification of the erector spinae and psoas major muscles.

Influence of Radiation Dose and Reconstruction Kernel on Fat Fraction Analysis in Dual-energy CT: A Phantom Study.

The quantitative evaluation of fat tissue, mainly for the determination of liver steatosis, is possible by using dual-energy computed tomography. Different photon energy acquisitions allow for estimation of attenuation coefficients. The effect of variation in radiation doses and reconstruction kernels on fat fraction estimation was investigated. A six-probe-phantom with fat concentrations of 0%, 20%, 40%, 60%, 80%, and 100% were scanned in Sn140/100 kV with radiation doses ranging between 20 and 200 mAs before and after calibration. Images were reconstructed using iterative kernels (I26,Q30,I70). Fat fractions measured in dual-energy computed tomography (DECT) were consistent with the 20%-stepwise varying actual concentrations. Variation in radiation dose resulted in 3.1% variation of fat fraction. Softer reconstruction kernel (I26) underestimated the fat fraction (-9.1%), while quantitative (Q30) and sharper kernel (I70) overestimated fat fraction (10,8% and 13,1, respectively). The fat fraction in DECT approaches the actual fat concentration when calibrated to the reconstruction kerneö. Variation of radiation dose caused an acceptable 3% variation.

Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat\u2013water decomposition MRI

BackgroundTime-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat–water decomposition MRI.ResultsThis study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs.ConclusionsThis automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.

Differences in multi-echo chemical shift encoded MRI proton density fat fraction estimation based on multifrequency fat peaks selection in non-alcoholic fatty liver disease patients

To compare the performance of multi-echo chemical-shift-encoded (MECSE) magnetic resonance imaging (MRI) proton density fat fraction (PDFF) estimation, considering three different fat frequency peak combinations, for the quantification of steatosis in patients with non-alcoholic fatty liver disease (NAFLD). The present study was a prospective cross-sectional research of 121 patients with metabolic syndrome and evidence of hepatic steatosis on ultrasound, who underwent a 3 T MRI examination. All patients were studied with a multifrequency MECSE sequence. The PDFF was calculated using six peaks (MECSEp123456), three peaks (MECSEp456), and a single peak (MECSEp5) model. The two simpler fat peak models were compared to the six peaks model, which was considered the reference standard. Linearity was evaluated using linear regression while agreement was described using Bland-Altman analysis. The mean age was 47 (±9) years and BMI was 29.9 (±2.9) kg/m2. Steatosis distribution was 15%/31%/54% (S1/S2/S3, respectively). Compared to MECSEp123456, both models provided linear PDFF measurements (R2= 0.99 and 0.97, MECSEp456 and MECSEp5 respectively). Regression slope (0.92; p<0.001) and mean Bland-Altman bias (-1.5%; 95% limits of agreement: -3.19%, 0.22%) indicated minimal underestimation by using PDFF-MECSEp456. Nonetheless, mean differences in PDFF estimations varied from -1.5% (MECSEp456,p=0.006) to -2.2% (MECSEp5,p<0.001) when compared to full six fat frequencies model. Although simpler spectral fat MECSE analysis shows a linear relationship with the standard six peaks model, their variation in estimated PDFF values introduces a low but clinically significant bias in fat quantification and steatosis grading in NAFLD patients.

FRI022 - Three is enough: optimization of regions of interest selection for accurate estimation of proton density fat fraction in liver from magnetic resonance images

FRI022 - Three is enough: optimization of regions of interest selection for accurate estimation of proton density fat fraction in liver from magnetic resonance images

Determination of the R2* relaxation rate constant for estimating hepatic iron concentration: A customized approach that considers liver fat infiltration

PurposeΑ customized approach to determine R2* relaxation rate for hepatic iron concentration (HIC) estimation is presented, and is evaluated in the context of concurrent liver fat infiltration. MethodsThe proposed method employs a customized acquisition protocol, featuring a 16-echo, gradient-echo sequence, and a bi-exponential least squares fitting that considers baseline noise and uses a cosine function to correct for fat-induced signal oscillation. 193 patients with wide-ranging HIC and liver fat fraction (FF) were imaged at 1.5 T. In severely iron-overload patients, a four-echo train technique was applied to enforce all 16 echoes in the 1.2–4.0 ms range. Acquired data were compared to corresponding results obtained with the IDEAL IQ method. ResultsTechniques employed to counter the rapid signal decay in iron-overloaded liver, such as the offset and the truncation methods, have to be combined with the appropriate calibration curve to provide reliable HIC estimation. When high grade steatosis and siderosis co-exist, fat-suppression may downgrade siderosis. A high correlation was observed between data obtained with the proposed technique and the IDEAL IQ method, except from the high R2* region. However, systematic differences were detected. In the concurrent presence of high FF and non-severe iron overload, it is postulated that the bi-exponential model may attribute patient siderosis grading more accurately than IDEAL IQ, while simultaneously providing reliable FF estimation. ConclusionsThe proposed approach is widely available and seems capable of providing reliable R2* measurements regardless of liver steatosis grading, whilst it succeeds in averting significant R2* underestimation in severely iron-overloaded liver.