New scores were developed to identify at-risk metabolic dysfunction-associated steatohepatitis (MASH) using multiparametric MRI (mpMRI).

A prospective study was conducted on 176 patients with suspected or diagnosed metabolic dysfunction-associated steatotic liver disease (MASLD) paired with an MR scan, vibration-controlled transient elastography (VCTE), and liver biopsy. Liver stiffness measurement (LSM) using magnetic resonance elastography (MRE), proton density fat fraction (PDFF), and mpMRI-based corrected T1 (cT1) were combined to develop a one-step strategy, named MPcT (MRE + PDFF + cT1, combined score), and a two-step strategy-MRE-based LSM followed by PDFF with cT1 (M-PcT, paired score) for diagnosing at-risk MASH. Each model was categorized using rule-in and rule-out criteria (three categorized analyses). To avoid overfitting, the diagnostic accuracies were evaluated based on 5-fold cross-validation.

PDFF + cT1 (PcT) had the highest diagnostic performance for severe activity (hepatic inflammation plus ballooning grade ≥ 3) and for NAS ≥ 4 (active MASH). Areas under receiver operating characteristic curves (AUROCs) of M-PcT (0.832) for detecting at-risk MASH were significantly higher than those of Fibroscan-AST (FAST) (0.744, p = 0.017), MRI-AST (MAST) (0.710, p = 0.002), and MPcT (0.695, p < 0.001) in three categorized analysis. Following the rule-in criteria, positive predictive values of M-PcT (84.5%) were higher than those of FAST (73.5%), MAST (70.0%), and MPcT (66.7%). Following the rule-out criteria, negative predictive values of M-PcT (88.7%) were higher than those of FAST (84.0%), MAST (73.9%), and MPcT (84.9%).

The two-step strategy, M-PcT (paired score), showed the reliability of rule-in/-out for at-risk MASH, with better predictive performance compared with FAST and MAST (combined score).

This study is registered with ClinicalTrials.gov (number, UMIN000012757).

Question There is no mpMRI-based method for detecting as-risk MASH (NAFLD activity score ≥ 4 with fibrosis stage ≥ 2) like FAST and MAST scores. Findings MRE-based LSMs followed by PDFF with cT1 (M-PcT) were more useful in detecting at-risk MASH than the combined score (FAST and MAST). Clinical relevance By combining MRE and PDFF with cT1, it becomes possible to evaluate the pathology of MASH without the need for a liver biopsy, assisting in prognosis prediction and decision-making for treatment options.

This study aims to present various tractography methods for delineating the Frontal Aslant Tract (FAT) and to quantify morphological features of FAT based on diffusion tensor imaging.

The study includes 68 patients, for which FAT was reconstructed using the Region Of Interest (ROI)-based approach. The ROIs were defined in either SFG - Superior Frontal Gyrus (ROI 1), or SMA-Supplementary Motor Area (ROI 2). The respective endpoints were located in the Inferior Frontal Gyrus (IFG)-either in pars opercularis or in pars triangularis. For each patient, FAT was delineated using four combinations of the above ROI-endpoint pairs.

The highest streamline counts and fiber volumes of FAT were obtained using ROI 1 (i.e., SFG) with the endpoint in IFG pars opercularis. All subjects expressed left dominance of the pathway quantified by the higher streamline counts and fiber volumes regardless of gender. Additionally, higher Mean Diffusivity (MD) and lower Fractional Anisotropy (FA) values were observed in patients above 55 years of age than in younger patients.

FAT is a neural pathway that can be tracked based on various anatomical landmarks. Clinically, it appears that delineating FAT between SFG and the pars opercularis region of IFG is optimal, as it is directly associated with the highest number of fibers and the greatest volume of the tract contained between these points.

Background: Clinical adoption of ultrasound attenuation coefficient (AC) measurements has been hindered by lack of uniform measurement protocol and a range of factors that may cause variability. Objective: To evaluate associations of ROI depth, ROI size, and confidence map threshold with interobserver agreement and diagnostic performance of ultrasound AC measurements in detecting and grading hepatic steatosis using MRI proton-density fat fraction (PDFF) as the reference standard. Methods: This prospective study enrolled adults with known steatosis or at risk for steatosis from October 2023 to August 2024. One of two operators obtained videos of AC acquisitions using a single ultrasound unit. Both operators independently reviewed all videos and placed circular ROIs to obtain AC measurements for all 24 possible combinations of four ROI depths (2.0, 2.5, 3.0, and 4.0 cm from liver capsule to ROI outer edge), three ROI sizes (3.0, 3.5, and 4.0 cm), and two confidence map thresholds (20% and 40%). Participants underwent MRI PDFF measurement as reference. Results: The analysis included 101 participants (mean age, 54.5±12.1 years; 62 female, 39 male). Interoperator agreement was excellent for all combinations (intraclass correlation coefficient: 0.92-0.98). AC measurements showed strongest correlations (Spearman rho, 0.81 and 0.80 for operators 1 and 2, respectively) with MRI PDFF at a ROI depth of 4.0 cm. The optimal combination considering correlations with MRI PDFF and AUC across steatosis grades included a depth of 4.0 cm, size of 4.0 cm, and threshold of 40%. This combination had AUC for detecting steatosis with grade >0, >1, and >2 for operator 1 of 0.93, 0.88, and 0.81, respectively, and operator 2 of 0.92, 0.86, and 0.81, respectively. However, accuracy for detecting steatosis (grade >0) was highest for the combination of depth of 3.0 cm, size of 4.0 cm, and threshold of 40% (operator 1, 90.1%; operator 2, 82.2%). Conclusion: AC measurements showed excellent interoperator agreement across parameter combinations. Correlations with MRI PDFF were strongest at a depth of 4.0 cm. Combinations yielding highest diagnostic performance were identified. Clinical Impact: These results will help determine a standardized optimal protocol for ultrasound AC measurements, facilitating clinical adoption for liver fat quantification.

Cardiac-induced liver motion can bias liver ADC measurements and compromise reproducibility. The purpose of this work was to enable motion-robust DWI on multiple MR scanners and assess reproducibility of the resulting liver ADC measurements.

First moment-optimized diffusion imaging (MODI) was implemented on three MR scanners with various gradient performances and field strengths. MODI-DWI and conventional Stejskal-Tanner monopolar (MONO) DWI were acquired in eight (N = 8) healthy volunteers on each scanner, and DWI repetitions were combined using three different averaging methods. For each combination of scanner, acquisition, and averaging method, ADC measurements from each liver segment were collected. Systematic differences in ADC values between scanners and methods were assessed with linear mixed effects modeling, and reproducibility was quantified via reproducibility coefficients.

MODI reduced left-right liver lobe ADC bias from 0.43 × 10-3 mm2/s (MONO) to 0.19 × 10-3 mm2/s (MODI) when simple (unweighted) repetition averaging was used. The bias was reduced from 0.23 × 10-3 mm2/s to 0.06 × 10-3 mm2/s using weighted averaging, and 0.14 × 10-3 mm2/s to 0.01 × 10-3 mm2/s using squared weighted averaging. There was no significant difference in ADC measurements between field strengths or scanner gradient performance. MODI improved reproducibility coefficients compared to MONO: 0.84 × 10-3 mm2/s vs. 0.63 × 10-3 mm2/s (MODI vs. MONO) for simple averaging, 0.66 × 10-3 mm2/s vs. 0.50 × 10-3 mm2/s for weighted averaging, and 0.61 × 10-3 mm2/s vs. 0.47 × 10-3 mm2/s for squared weighted averaging.

The feasibility of motion-robust liver DWI using MODI was demonstrated on multiple MR scanners. MODI improved interlobar agreement and reproducibility of ADC measurements in a healthy cohort.

The need for early non-invasive diagnostic tools for chronic liver fibrosis is growing, particularly in individuals with obesity, non-alcoholic fatty liver disease (NAFLD), and the metabolic syndrome (MetS) since prevalence of these conditions is increasing. This case-control study compared non-invasive liver fibrosis markers in obesity with NAFLD and MetS (NAFLD-MetS, n = 33), in obese (n = 28) and lean (n = 27) control groups. We used MRI (T1 relaxation times (T1) and liver stiffness), circulating biomarkers (CK18, PIIINP, and TIMP1), and algorithms (FIB-4 index, Forns score, FNI, and MACK3 score) to assess their potential in predicting liver fibrosis risk. We found that T1 (892 ± 81 ms vs. 818 ± 64 ms, p < 0.001), FNI (15 ± 12% vs. 9 ± 7%, p = 0.018), CK18 (166 ± 110 U/L vs. 113 ± 41 U/L, p = 0.019), and MACK3 (0.18 ± 0.15 vs. 0.05 ± 0.04, p < 0.001) were higher in the NAFLD-MetS group compared with the obese control group. Moreover, correlations were found between CK18 and FNI (r = 0.69, p < 0.001), CK18 and T1 (r = 0.41, p < 0.001), FNI and T1 (r = 0.33, p = 0.006), MACK3 and FNI (r = 0.79, p < 0.001), and MACK3 and T1 (r = 0.50, p < 0.001). We show that liver fibrosis markers are increased in obese individuals with NAFLD and MetS without clinical signs of liver fibrosis. More studies are needed to validate the use of these non-invasive biomarkers for early identification of liver fibrosis risk.

Noninvasive detection of liver steatosis and monitoring its progression are essential for effective therapeutic management. We validated the diagnostic performance of an ultrasonography (US)-based steatotic liver scoring system, derived from the B-mode method and the hepatic steatosis index (HSI) for the detection of liver steatosis, as identified by proton density fat fraction (PDFF) measurements on magnetic resonance imaging (MRI).

A total of 916 patients with chronic liver disease were included in the analysis.

The median MRI-PDFF value was 4.0% (interquartile range: 2.0-9.7). The distribution of scores (0/1/2/3/4/5/6) according to the US-based steatotic liver scoring system was as follows: 475, 36, 99, 78, 141, 66, and 21, respectively. The median HSI was 32.8 (28.9-37.4). A significant positive association between advancing scores of the US-based steatotic liver scoring system and increasing MRI-PDFF values was observed (p < 0.001). The diagnostic performance of the US-based steatotic liver scoring system and HSI for detecting steatosis grades ≥1, ≥2, and 3, as determined by MRI-PDFF, showed areas under the receiver operating characteristic curve of 0.940 and 0.842 (p < 0.001), 0.949 and 0.847 (p < 0.001), and 0.945 and 0.864 (p < 0.001), respectively. When the cutoff values of the scoring system were set at 2, 3, and 4 for steatosis grades ≥1, ≥2, and 3, the sensitivity and specificity were 90.6% and 90.5%, 92.9% and 83.0%, and 93.3% and 84.0%, respectively.

The B-mode US-based steatotic liver scoring system demonstrated robust diagnostic capability in detecting liver steatosis.

Prior work has shown that MRI-derived proton density fat fraction (PDFF) can diagnose metabolic dysfunction-associated steatotic liver disease (MASLD) noninvasively, but there is a paucity of data on the performance of PDFF to classify more advanced forms of the MASLD spectrum. The purpose of this study was to assess the diagnostic performance of PDFF for the diagnoses of MASLD, metabolic dysfunction-associated steatohepatitis (MASH), and fibrotic MASH in adults with obesity undergoing bariatric surgery, using contemporaneous intraoperative liver biopsy as a reference.

PDFF was evaluated alone and with other potential classifiers (imaging, serum and anthropometric), using Bayesian Information Criterion-based stepwise logistic regression models. Areas under the receiver operating characteristic (ROC) curves (AUC) were computed for all models and single classifiers. Cross-validated sensitivity and specificity were calculated at Youden-based PDFF classification thresholds. Data analysis from 140 patients demonstrated that PDFF was the most accurate single classifier, with high AUC for MASLD (0.95), MASH (0.85), and fibrotic MASH (0.82) (all p <0.001). Multivariable models, including PDFF, outperformed those without PDFF. The Youden-based threshold for PDFF was 4.4% for MASLD (sensitivity: 87%, specificity: 86%), 6.9% for MASH (sensitivity: 77%, specificity: 66%), and 13.5% for fibrotic MASH (sensitivity: 67%, specificity: 85%).

PDFF was the most accurate single classifier for diagnosing MASLD, MASH, and fibrotic MASH. The most accurate multivariable classification models for MASLD, MASH, and fibrotic MASH included PDFF, demonstrating the central importance of PDFF for noninvasive assessment of the MASLD spectrum.

The role of MRI to estimate liver iron concentration (LIC) for identifying patients with iron overload and guiding the titration of chelation therapy is increasingly established for routine clinical practice. However, the existence of multiple MRI-based LIC quantification techniques limits standardization and widespread clinical adoption. In this article, we review the existing and widely accepted MRI-based LIC estimation methods at 1.5 T and 3 T: signal intensity ratio (SIR) and relaxometry (R2 and R2*) and discuss the basic principles, acquisition and analysis protocols, and MRI-LIC calibrations for each technique. Further, we provide an up-to-date information on MRI vendor implementations and available offline commercial and free software for each MRI-based LIC quantification approach. We also briefly review the emerging and advanced MRI techniques for LIC estimation and their current limitations for clinical use. Lastly, we discuss the implications of MRI-based LIC measurements on clinical use and decision-making in the management of patients with iron overload. Some of the key highlights from this review are as follows: 1) Both R2 and R2* can estimate accurate and reproducible LIC, when validated acquisition parameters and analysis protocols are applied, 2) Although the Ferriscan R2 method has been widely used, recent consensus and guidelines endorse R2*-MRI as the most accurate and reproducible method for LIC estimation, 3) Ongoing efforts aim to establish R2*-MRI as the standard approach for quantifying LIC, and 4) Emerging R2*-MRI techniques employ radial sampling strategies and offer improved motion compensation and broader dynamic range for LIC estimation. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Preoperative imaging evaluation of liver volume and hepatic steatosis for the donor affects transplantation outcomes. However, computed tomography (CT) for liver volumetry and magnetic resonance spectroscopy (MRS) for hepatic steatosis are time consuming. Therefore, we investigated the correlation of automated 3D-multi-echo-Dixon sequence magnetic resonance imaging (ME-Dixon MRI) and its derived proton density fat fraction (MRI-PDFF) with CT liver volumetry and MRS hepatic steatosis measurements in living liver donors.

This retrospective cross-sectional study was conducted from December 2017 to November 2022. We enrolled donors who received a dynamic CT scan and an MRI exam within 2 days. First, the CT volumetry was processed semiautomatically using commercial software, and ME-Dixon MRI volumetry was automatically measured using an embedded sequence. Next, the signal intensity of MRI-PDFF volumetric data was correlated with MRS as the gold standard.

We included the 165 living donors. The total liver volume of ME-Dixon MRI was significantly correlated with CT (r = 0.913, p < 0.001). The fat percentage measured using MRI-PDFF revealed a strong correlation between automatic segmental volume and MRS (r = 0.705, p < 0.001). Furthermore, the hepatic steatosis group (MRS ≥5%) had a strong correlation than the non-hepatic steatosis group (MRS <5%) in both volumetric (r = 0.906 vs. r = 0.887) and fat fraction analysis (r = 0.779 vs. r = 0.338).

Automated ME-Dixon MRI liver volumetry and MRI-PDFF were strongly correlated with CT liver volumetry and MRS hepatic steatosis measurements, especially in donors with hepatic steatosis.

The quantity of regions of interest (ROIs) constitutes the primary determinant of the time investment in image analysis. In the context of proton density fat-fraction (PDFF) magnetic resonance imaging (MRI) conducted on liver grafts in ex vivo conditions, this research systematically examines various ROI sampling strategies. The findings of this study furnish essential insights, offering a foundation for optimizing time efficiency while ensuring precise assessment of hepatic steatosis before the crucial process of liver transplantation.

This was a retrospective analysis of a prospective study and included 35 liver grafts with histopathological steatosis that underwent 3T PDFF MRI in ex vivo. One ROI of 1 cm2 was selected for each hepatic segment, and any combination of ROIs in 1-8 liver segments was used, resulting in 511 combinations. Using intraclass correlation coefficients (ICCs) and Bland-Altman analyses, the PDFFs of all these combinations were compared with the 9-ROI average PDFF. There was a moderate correlation between the average PDFF and the histological findings (R = 0.47, P＜0.01).

The average 9-ROI PDFF of all liver grafts was 4.07 ± 4.35 % (0.870-20.904). All strategies with ≥5 ROIs had intraclass correlation coefficient (ICC) ≥ 0.995 and absolute limits of agreement (|LOA|)≤ 1.5 %. Overall, 54 of 84 (67.5 %) 3-ROI sampling strategy had ICC ≥0.995, and 70 of 84 (70 %) had |LOA|≤ 1.5 %. A total of 111 of 126 (88.1 %) 4-ROI sampling strategy had ICC ≥0.995, and 125 of 126 (99.2 %) had |LOA| ≤ 1.5 %.

The employment of the 5-ROI sampling strategy proves instrumental in both time conservation and precise assessment of hepatic steatosis within liver grafts during the ex vivo phase preceding liver transplantation.

To evaluate the relationship between serum gamma-glutamyl transferase (GGT) levels and fatty pancreas in subjects with concurrent obesity, insulin resistance and metabolic dysfunction-associated steatotic liver disease (MASLD) without a history of pancreatitis. From March 2019 to September 2021, 31 adult subjects with concurrent obesity and MASLD were recruited as part of the study investigating the biological impact of bariatric surgery and lifestyle modification on obesity. Chemical shift encoded MRI of the abdomen, LiverMultiScan, anthropometric, clinical and blood biochemistry analyses were performed prior to any intervention at baseline. GGT (p <.001) was significantly different between those 'with fatty pancreas' and 'without fatty pancreas' groups. GGT (p <.001) was significantly different between those 'with both metabolic syndrome and fatty pancreas' and those 'with metabolic syndrome but without fatty pancreas.' GGT (p <.001) was also significantly different between those 'with both diabetes and fatty pancreas' and those 'with diabetes but without fatty pancreas'. Logistic regression analysis showed that abnormal GGT levels (p = .010) and Hypertension (p = .045) were significant independent predictors of fatty pancreas. GGT was associated with fatty pancreas by an odds ratio 7.333 (95% [CI]: 1.467-36.664), while the AUROC of GGT in determining fatty pancreas was 0.849. Elevation in serum GGT might be a potential marker to identify fatty pancreas.

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.

Recent multicenter, multivendor MRI-based R2* vs. liver iron concentration (LIC) calibrations (i.e., MCMV calibrations) may facilitate broad clinical dissemination of R2*-based LIC quantification. However, these calibrations are based on a centralized offline R2* reconstruction, and their applicability with vendor-provided R2* maps is unclear.

To determine R2* ranges of agreement between the centralized and three MRI vendors' R2* reconstructions.

Prospective.

Two hundred and seven subjects (mean age 37.6 ± 19.6 years; 117 male) with known or suspected iron overload from four academic medical centers.

Standardized multiecho spoiled gradient echo sequence at 1.5 T and 3.0 T for R2* mapping and a multiple spin-echo sequence at 1.5 T for LIC quantification. MRI vendors: GE Healthcare, Philips Healthcare, and Siemens Healthineers.

R2* maps were generated using both the centralized and vendor reconstructions, and ranges of agreement were determined. R2*-LIC linear calibrations were determined for each site, field strength, and reconstruction and compared with the MCMV calibrations.

Bland-Altman analysis to determine ranges of agreement. Linear regression, analysis of covariance F tests, and Tukey's multiple comparison testing to assess reproducibility of calibrations across sites and vendors. A P value <0.05 was considered significant.

The upper limits of R2* ranges of agreement were approximately 500, 375, and 330 s-1 for GE, Philips, and Siemens reconstructions, respectively, at 1.5 T and approximately 700 and 800 s-1 for GE and Philips, respectively, at 3.0 T. Within the R2* ranges of agreement, vendor R2*-LIC calibrations demonstrated high reproducibility (no significant differences between slopes or intercepts; P ≥ 0.06) and agreed with the MCMV calibrations (overlapping 95% confidence intervals).

Based on the determined upper limits, R2* measurements obtained from vendor-provided R2* maps may be reliably and practically used to quantify LIC less than approximately 8-13 mg/g using the MCMV calibrations and similar acquisition parameters as this study.

1 TECHNICAL EFFICACY: Stage 3.

To evaluate reproducibility and interlobar agreement of intravoxel incoherent motion (IVIM) quantification in the liver across field strengths and MR scanners with different gradient hardware.

Cramer-Rao lower bound optimization was performed to determine optimized monopolar and motion-robust 2D (b-value and first-order motion moment [M1]) IVIM-DWI acquisitions. Eleven healthy volunteers underwent diffusion MRI of the liver, where each optimized acquisition was obtained five times across three MRI scanners. For each data set, IVIM estimates (diffusion coefficient (D), pseudo-diffusion coefficients (d 1 * $$ {d}_1^{\ast } $$ andd 2 * $$ {d}_2^{\ast } $$ ), blood velocity SDs (Vb1 and Vb2), and perfusion fractions [f1 and f2]) were obtained in the right and left liver lobes using two signal models (pseudo-diffusion and M1-dependent physical) with and without T2 correction (fc1 and fc2) and three fitting techniques (tri-exponential region of interest-based full and segmented fitting and blood velocity SD distribution fitting). Reproducibility and interlobar agreement were compared across methods using within-subject and pairwise coefficients of variation (CVw and CVp), paired sample t-tests, and Bland-Altman analysis.

Using a combination of motion-robust 2D (b-M1) data acquisition, M1-dependent physical signal modeling with T2 correction, and blood velocity SD distribution fitting, multiscanner reproducibility with median CVw = 5.09%, 11.3%, 9.20%, 14.2%, and 12.6% for D, Vb1, Vb2, fc1, and fc2, respectively, and interlobar agreement with CVp = 8.14%, 11.9%, 8.50%, 49.9%, and 42.0%, respectively, was achieved.

Recently proposed advanced IVIM acquisition, signal modeling, and fitting techniques may facilitate reproducible IVIM quantification in the liver, as needed for establishment of IVIM-based quantitative biomarkers for detection, staging, and treatment monitoring of diseases.

Background The extent of liver steatosis can be assessed using US attenuation coefficient (AC) algorithms currently implemented in several US systems. However, little is known about intersystem and interoperator variability in measurements. Purpose To assess intersystem and interoperator agreement in US AC measurements for fat quantification in individuals with varying degrees of liver steatosis and to assess the correlation of each manufacturer's AC algorithm results with MRI proton density fat fraction (PDFF). Materials and Methods This prospective study was conducted at Southwoods Imaging, Youngstown, Ohio, September 30-October 1, 2023. Two operators independently obtained AC measurements using eight US systems equipped with an AC algorithm from different manufacturers. On the same day, MRI PDFF measurement was performed by a different operator. Correlation between US AC and MRI PDFF was assessed using a mixed-effects model. Agreement between systems and operators was evaluated using the intraclass correlation coefficient (ICC). Results Twenty-six individuals (mean age, 55.4 years ± 10.7 [SD]; 16 female participants) were evaluated. The correlation of US AC with MRI PDFF was high for five AC algorithms (r range, 0.70-0.86), moderate for two (r = 0.62 for both), and poor for one (r = 0.47). In pairwise comparisons, none of the pairs of systems achieved excellent agreement (overall ICC = 0.33 [95% CI: 0.15, 0.52]). One pair showed good agreement (ICC = 0.79 [95% CI: 0.66, 0.87]), eight pairs showed moderate agreement (ICC range, 0.50 [95% CI: 0.22, 0.69] to 0.73 [95% CI: 0.49, 0.85]), and 19 pairs showed poor agreement (ICC range, 0.11 [95% CI: -0.06, 0.37] to 0.48 [95% CI: 0.20, 0.67]). Interoperator agreement on AC value was excellent for the Samsung Medison algorithm (ICC = 0.90 [95% CI: 0.80, 0.96]), good for the Siemens Healthineers (ICC = 0.76 [95% CI: 0.54, 0.89]) and Canon Medical Systems (ICC = 0.76 [95% CI: 0.16, 0.92]) algorithms, and moderate for the remaining algorithms (ICC range, 0.50 [95% CI: 0.16, 0.73] to 0.74 [95% CI: 0.51, 0.88]). The mean AC value obtained by the two operators did not differ for any system except the system from Canon Medical Systems. Conclusion There was substantial variability in AC values obtained with different US systems, precluding interchangeability between systems for liver steatosis diagnosis and follow-up imaging. Interoperator agreement ranged from moderate to excellent. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Han in this issue.

By exploiting different tissues' characteristic T1 relaxation times, T1-weighted images help distinguish normal and abnormal tissues, aiding assessment of diffuse and local pathologies. However, such images do not provide quantitative T1 values. Advances in abdominal MRI techniques have enabled measurement of abdominal organs' T1 relaxation times, which can be used to create color-coded quantitative maps. T1 mapping is sensitive to tissue microenvironments including inflammation and fibrosis and has received substantial interest for noninvasive imaging of abdominal organ pathology. In particular, quantitative mapping provides a powerful tool for evaluation of diffuse disease by making apparent changes in T1 occurring across organs that may otherwise be difficult to identify. Quantitative measurement also facilitates sensitive monitoring of longitudinal T1 changes. Increased T1 in liver helps to predict parenchymal fibro-inflammation, in pancreas is associated with reduced exocrine function from chronic or autoimmune pancreatitis, and in kidney is associated with impaired renal function and aids diagnosis of chronic kidney disease. In this review, we describe the acquisition, postprocessing, and analysis of T1 maps in the abdomen and explore applications in liver, spleen, pancreas, and kidney. We highlight practical aspects of implementation and standardization, technical pitfalls and confounding factors, and areas of likely greatest clinical impact.

To evaluate the efficacy of volumetric CT attenuation-based parameters obtained through automated 3D organ segmentation on virtual non-contrast (VNC) images from dual-energy CT (DECT) for assessing hepatic steatosis.

This retrospective study included living liver donor candidates having liver DECT and MRI-determined proton density fat fraction (PDFF) assessments. Employing a 3D deep learning algorithm, the liver and spleen were automatically segmented from VNC images (derived from contrast-enhanced DECT scans) and true non-contrast (TNC) images, respectively. Mean volumetric CT attenuation values of each segmented liver (L) and spleen (S) were measured, allowing for liver attenuation index (LAI) calculation, defined as L minus S. Agreements of VNC and TNC parameters for hepatic steatosis, i.e., L and LAI, were assessed using intraclass correlation coefficients (ICC). Correlations between VNC parameters and MRI-PDFF values were assessed using the Pearson's correlation coefficient. Their performance to identify MRI-PDFF ≥ 5% and ≥ 10% was evaluated using receiver operating characteristic (ROC) curve analysis.

Of 252 participants, 56 (22.2%) and 16 (6.3%) had hepatic steatosis with MRI-PDFF ≥ 5% and ≥ 10%, respectively. LVNC and LAIVNC showed excellent agreement with LTNC and LAITNC (ICC = 0.957 and 0.968) and significant correlations with MRI-PDFF values (r = - 0.585 and - 0.588, Ps < 0.001). LVNC and LAIVNC exhibited areas under the ROC curve of 0.795 and 0.806 for MRI-PDFF ≥ 5%; and 0.916 and 0.932, for MRI-PDFF ≥ 10%, respectively.

Volumetric CT attenuation-based parameters from VNC images generated by DECT, via automated 3D segmentation of the liver and spleen, have potential for opportunistic hepatic steatosis screening, as an alternative to TNC images.

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.

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.

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.

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.

To assess the utility of fat fraction quantification using quantitative multi-echo Dixon for evaluating tumor proliferation and microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

A total of 66 patients with resection and histopathologic confirmed HCC were enrolled. Preoperative MRI with proton density fat fraction and R2* mapping was analyzed. Intratumoral and peritumoral regions were delineated with manually placed regions of interest at the maximum level of intratumoral fat. Correlation analysis explored the relationship between fat fraction and Ki67. The fat fraction and R2* were compared between high Ki67(>30%) and low Ki67 nodules, and between MVI negative and positive groups. Receiver operating characteristic (ROC) analysis was used for further analysis if statistically different.

The median fat fraction of tumor (tFF) was higher than peritumor liver (5.24% vs 3.51%, P=0.012). The tFF was negatively correlated with Ki67 (r=-0.306, P=0.012), and tFF of high Ki67 nodules was lower than that of low Ki67 nodules (2.10% vs 4.90%, P=0.001). The tFF was a good estimator for low proliferation nodules (AUC 0.747, cut-off 3.39%, sensitivity 0.778, specificity 0.692). There was no significant difference in tFF and R2* between MVI positive and negative nodules (3.00% vs 2.90%, P=0.784; 55.80s-1 vs 49.15s-1, P=0.227).

We infer that intratumor fat can be identified in HCC and fat fraction quantification using quantitative multi-echo Dixon can distinguish low proliferative HCCs.

Obesity is highly associated with Non-alcoholic fatty liver disease (NAFLD) and increased risk of liver cirrhosis and liver cancer-related death. We determined the diagnostic performance of the complex-based chemical shift technique MRI-PDFF for quantifying liver fat and its correlation with histopathologic findings in an obese population within 24 h before bariatric surgery. This was a prospective, cross-sectional, Institutional Review Board-approved study of PDFF-MRI of the liver and MRI-DIXON image volume before bariatric surgery. Liver tissues were obtained during bariatric surgery. The prevalence of NAFLD in the investigated cohort was as high as 94%. Histologic hepatic steatosis grades 0, 1, 2, and 3 were observed in 3 (6%), 25 (50%), 14 (28%), and 8 (16%) of 50 obese patients, respectively. The mean percentages of MRI-PDFF from the anterior and posterior right hepatic lobe and left lobe vs. isolate left hepatic lobe were 15.6% (standard deviation [SD], 9.28%) vs. 16.29% (SD, 9.25%). There was a strong correlation between the percentage of steatotic hepatocytes and MRI-PDFF in the left hepatic lobe (r = 0.82, p < 0.001) and the mean value (r = 0.78, p < 0.001). There was a strong correlation between MRI-derived subcutaneous adipose tissue volume and total body fat mass by dual-energy X-ray absorptiometry, especially at the L2-3 and L4 level (r = 0.85, p < 0.001). MRI-PDFF showed good performance in assessing hepatic steatosis and was an excellent noninvasive technique for monitoring hepatic steatosis in an obese population.

Weight loss is the most effective treatment for nonalcoholic fatty liver disease (NAFLD). There is evidence that the Mediterranean diets rich in unsaturated fatty acids and fiber have beneficial effects on weight homeostasis and metabolic risk factors in individuals with NAFLD. Studies have also shown that higher circulating concentrations of pentadecanoic acid (C15:0) are associated with a lower risk for NAFLD.

To examine the effects of a Mediterranean-like, culturally contextualized Asian diet rich in fiber and unsaturated fatty acids, with or without C15:0 supplementation, in Chinese females with NAFLD.

In a double-blinded, parallel-design, randomized controlled trial, 88 Chinese females with NAFLD were randomly assigned to 1 of the 3 groups for 12 wk: diet with C15:0 supplementation (n = 31), diet without C15:0 supplementation (n = 28), or control (habitual diet and no C15:0 supplementation, n = 29). At baseline and after the intervention, body fat percentage, intrahepatic lipid content, muscle and abdominal fat, liver enzymes, cardiometabolic risk factors, and gut microbiome were assessed.

In the intention-to-treat analysis, weight reductions of 4.0 ± 0.5 kg (5.3%), 3.4 ± 0.5 kg (4.5%), and 1.5 ± 0.5 kg (2.1%) were achieved in the diet-with-C15:0, diet without-C15:0, and the control groups, respectively. The proton density fat fraction (PDFF) of the liver decreased by 33%, 30%, and 10%, respectively. Both diet groups achieved significantly greater reductions in body weight, liver PDFF, total cholesterol, gamma-glutamyl transferase, and triglyceride concentrations compared with the control group. C15:0 supplementation reduced LDL-cholesterol further, and increased the abundance of Bifidobacterium adolescentis. Fat mass, visceral adipose tissue, subcutaneous abdominal adipose tissue (deep and superficial), insulin, glycated hemoglobin, and blood pressure decreased significantly in all groups, in parallel with weight loss.

Mild weight loss induced by a Mediterranean-like diet adapted for Asians has multiple beneficial health effects in females with NAFLD. C15:0 supplementation lowers LDL-cholesterol and may cause beneficial shifts in the gut microbiome.

This trial was registered at the clinicaltrials.gov as NCT05259475.

Clinical trials enroll patients with active fibrotic nonalcoholic steatohepatitis (NASH) (nonalcoholic fatty liver disease [NAFLD] activity score ≥ 4) and significant fibrosis (F ≥ 2); however, screening failure rates are high following biopsy. We developed new scores to identify active fibrotic NASH using FibroScan and magnetic resonance imaging (MRI).

We undertook prospective primary (n = 176), retrospective validation (n = 169), and University of California San Diego (UCSD; n = 234) studies of liver biopsy-proven NAFLD. Liver stiffness measurement (LSM) using FibroScan or magnetic resonance elastography (MRE), controlled attenuation parameter (CAP), or proton density fat fraction (PDFF), and aspartate aminotransferase (AST) were combined to develop a two-step strategy-FibroScan-based LSM followed by CAP with AST (F-CAST) and MRE-based LSM followed by PDFF with AST (M-PAST)-and compared with FibroScan-AST (FAST) and MRI-AST (MAST) for diagnosing active fibrotic NASH. Each model was categorized using rule-in and rule-out criteria.

Areas under receiver operating characteristic curves (AUROCs) of F-CAST (0.826) and M-PAST (0.832) were significantly higher than those of FAST (0.744, p = 0.004) and MAST (0.710, p < 0.001). Following the rule-in criteria, positive predictive values of F-CAST (81.8%) and M-PAST (81.8%) were higher than those of FAST (73.5%) and MAST (70.0%). Following the rule-out criteria, negative predictive values of F-CAST (90.5%) and M-PAST (90.9%) were higher than those of FAST (84.0%) and MAST (73.9%). In the validation and UCSD cohorts, AUROCs did not differ significantly between F-CAST and FAST, but M-PAST had a higher diagnostic performance than MAST.

The two-step strategy, especially M-PAST, showed reliability of rule-in/-out for active fibrotic NASH, with better predictive performance compared with MAST. This study is registered with ClinicalTrials.gov (number, UMIN000012757).

Non-alcoholic fatty liver disease (NAFLD) is currently diagnosed by liver biopsy or MRI proton density fat fraction (MRI-PDFF) from left hepatic lobe (LTHL) and/or right hepatic lobe (RTHL). The objective of this study was to compare the diagnostic value of ultrasound attenuation coefficients (ACs) from RTHL and LTHL in detecting hepatic steatosis using biopsy or MRI-PDFF as a reference standard.

Sixty-six patients with suspected NAFLD were imaged with an Aplio i800 ultrasound scanner (Canon Medical Systems, Tustin, CA). Five AC measurements from RTHL and LTHL were averaged separately and together to be compared with the reference standard.

Forty-seven patients (71%) were diagnosed with NAFLD. Mean ACs were significantly higher in fatty livers than non-fatty livers (RTHL: 0.73 ± 0.10 vs. 0.63 ± 0.07 dB/cm/MHZ; p < 0.0001, LTHL: 0.78 ± 0.11 vs. 0.63 ± 0.06 dB/cm/MHz; p < 0.0001, RTHL & LTHL: 0.76 ± 0.09 vs. 0.63 ± 0.05 dB/cm/MHz; p < 0.0001). Biopsy steatosis grades (n =31) were better correlated with the mean ACs of RTHL & LTHL (r = 0.72) compared to LTHL (r = 0.67) or RTHL (r = 0.61). Correlation between MRI-PDFF (n = 35) and mean ACs was better for LTHL (r = 0.69) compared to the RTHL & LTHL (r = 0.66) or RTHL (r = 0.45). Higher diagnostic accuracy was shown for the mean ACs of RTHL & LTHL (AUC 0.89, specificity 94%, sensitivity 78%) compared to LTHL (AUC 0.89, specificity 88%, sensitivity 82%) or RTHL (AUC 0.81, specificity 89%, sensitivity 68%).

Ultrasound ACs from RTHL and LTHL showed comparable diagnostic values in detection of hepatic steatosis with the highest diagnostic accuracy when they were averaged together.

This randomized controlled trial aimed to study the effects of Yijinjing plus Elastic Band Resistance exercise on intrahepatic lipid (IHL), body fat distribution, glucolipid metabolism and biomarkers of inflammation in middle-aged and older people with pre-diabetes mellitus (PDM).

34 PDM participants (mean age, 62.62 ± 4.71 years; body mass index [BMI], 25.98 ± 2.44 kg/m2) were randomly assigned to the exercise group (n = 17) or control group (n = 17). The exercise group performed moderate-intensity Yijinjing and Elastic Band Resistance training 5 times per week for 6 months. The control group maintained their previous lifestyle. We measured body composition (body weight and body fat distribution), IHL, plasma glucose, lipid and the homeostatic model assessment of insulin resistance (HOMA-IR), inflammatory cytokines at baseline and 6 months.

Compared with baseline, exercise significantly reduced IHL (reduction of 1.91 % ± 2.61 % vs an increase of 0.38 % ± 1.85 % for controls; P = 0.007), BMI (reduction of 1.38 ± 0.88 kg/m2 vs an increase of 0.24 ± 1.02 kg/m2 for controls; P = 0.001), upper limb fat mass, thigh fat mass and whole body fat mass. Fasting glucose, HOMA-IR, plasma total cholesterol (TC), and triglyceride (TG) were decreased in the exercise group (P < 0.05). There were no effects of exercise on liver enzyme levels and inflammatory cytokines. The decrease in IHL was positively correlated with the decreases in BMI, body fat mass and HOMA-IR.

Six months of Yijinjing and resistance exercise significantly reduced hepatic lipids and body fat mass in middle-aged and older people with PDM. These effects were accompanied by weight loss, improved glycolipid metabolism and insulin resistance.

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Accumulation of excess iron in the body, or systemic iron overload, results from a variety of causes. The concentration of iron in the liver is linearly related to the total body iron stores and, for this reason, quantification of liver iron concentration (LIC) is widely regarded as the best surrogate to assess total body iron. Historically assessed using biopsy, there is a clear need for noninvasive quantitative imaging biomarkers of LIC. MRI is highly sensitive to the presence of tissue iron and has been increasingly adopted as a noninvasive alternative to biopsy for detection, severity grading, and treatment monitoring in patients with known or suspected iron overload. Multiple MRI strategies have been developed in the past 2 decades, based on both gradient-echo and spin-echo imaging, including signal intensity ratio and relaxometry strategies. However, there is a general lack of consensus regarding the appropriate use of these methods. The overall goal of this article is to summarize the current state of the art in the clinical use of MRI to quantify liver iron content and to assess the overall level of evidence of these various methods. Based on this summary, expert consensus panel recommendations on best practices for MRI-based quantification of liver iron are provided.

Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD.

One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study. Subjects were categorized as NAFLD and non-NAFLD according to MR proton density fat fraction (PDFF) findings. Ultrasound images from 10 different locations in the right and left hepatic lobes were collected following a standard protocol. MRI-based liver fat quantification was used as the reference standard with >6.4% indicative of NAFLD. A supervised machine learning model was developed for assessment of NAFLD. To validate model performance, a balanced testing dataset of 24 subjects was used. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy with 95% confidence interval were calculated.

A total of 1119 images from 106 participants was used for model development. The internal evaluation achieved an average precision of 0.941, recall of 88.2%, and precision of 89.0%. In the testing set AutoML achieved a sensitivity of 72.2% (63.1%-80.1%), specificity of 94.6% (88.7%-98.0%), positive predictive value (PPV) of 93.1% (86.0%-96.7%), negative predictive value of 77.3% (71.6%-82.1%), and accuracy of 83.4% (77.9%-88.0%). The average agreement for an individual subject was 92%.

An ultrasound-based machine learning model for identification of NAFLD showed high specificity and PPV in this prospective trial. This approach may in the future be used as an inexpensive and noninvasive screening tool for identifying NAFLD in high-risk patients.

To assess diagnostic performance of quantitative ultrasound (QUS) biomarkers in assessing hepatic steatosis.

We prospectively recruited 125 participants (mean age 54 years) who underwent liver QUS, magnetic resonance imaging (MRI), and laboratory tests within 30 days in this IRB approved study. Based on MRI-proton density fat fraction (MRI-PDFF) and MRE, we divided 125 participants into normal liver, nonalcoholic fatty liver (NAFL) and liver fibrosis (≥F1) groups. We examined diagnostic performance of ultrasound attenuation coefficient (AC), normalized local variance (NLV), superb microvascular imaging-based vascularity index (SMI-VI), and shear wave velocity (SWV) for determining hepatic steatosis and fibrosis using area under receiver operating characteristic curve (AUC). We also analyzed correlations of QUS biomarkers to MRI using Spearman correlation coefficient.

We observed significant differences in AC, NLV, and SMI-VI among the three groups (22 participants with normal liver, 78 with NAFL, and 25 with liver fibrosis). AUC of AC, NLV, and SMI-VI for determining ≥ mild steatotic livers (MRI-PDFF ≥5%) was 0.95, 0.90, and 0.92, respectively. AUC of SWV for determining ≥ F1 liver fibrosis was 0.93. The correlation of MRI-PDFF was positive to AC (r = 0.91) and negative to NLV (r = -0.74), SMI-VI (r = -0.8) in NAFL group. There was a significant difference in regression slope of AC to MRI-PDFF in livers with and without ≥F1 (0.84 vs 0.91, P = .02).

QUS biomarkers have high sensitivity and specificity to determine and grade hepatic steatosis and detect liver fibrosis. The effect of liver fibrosis on the performance of QUS biomarkers in quantifying liver fat content warrants further investigation.

This research aims to determine the best liver segments representing the whole-liver fat fraction (FF) in magnetic resonance imaging (MRI)-based measurement of the proton density fat fraction (PDFF).

This retrospective study included 989 adult subjects who underwent MRI-PDFF from March 2018 to January 2021. Three regions of interest (ROI) were measured and averaged for each hepatic segment and the volume-weighted hepatic FF was calculated. Intrahepatic fat variability was assessed by standard deviation between all ROIs. Univariate and multivariate linear regression analyses were done for the factors associated with intrahepatic fat variability among clinical characteristics, blood parameters and the volume-weighted FF. The arithmetic means of specific hepatic segments that were the closest to the volume-weighted FF were identified in all subjects and those with moderate or severe fatty liver.

The volume-weighted FF was 8.18% and variability was 1.33%. Volume-weighted FF was the only associated factor with intrahepatic variability. The arithmetic mean of segments V, VI, and IV was closest to the volume-weighted FF in all subjects and in subjects with moderate or severe fatty liver.

There was considerable heterogeneity in hepatic steatosis between each segment of the liver, and the variability was significantly affected by the volume-weighted FF. The mean hepatic FF from segments V, VI, and IV could be used to estimate the volume-weighted FF of the whole liver, not only in the general population but also in patients with moderate or severe fatty liver.

The recently developed ultrasound-based hepatic fat quantification tools have the potential to be implemented in daily practice with wide acceptance due to inherited advantages of ultrasound technology. Researchers intensively focused on this topic and the accumulated evidences that support clinical usefulness of these tools. However, differences in the researcher-dependent factors of the utilized MRI-PDFF technique, the recommended reference standard, may hinder the better understanding of the diagnostic performances of these tools. Therefore, a standardized approach for MRI-PDFF technique, which is established with international consensus may be considered as important.

The purpose of this work was to obtain precise tri-exponential intravoxel incoherent motion (IVIM) quantification in the liver using 2D (b-value and first-order motion moment [M1 ]) IVIM-DWI acquisitions and region of interest (ROI)-based fitting techniques.

Diffusion MRI of the liver was performed in 10 healthy volunteers using three IVIM-DWI acquisitions: conventional monopolar, optimized monopolar, and optimized 2D (b-M1 ). For each acquisition, bi-exponential and tri-exponential full, segmented, and over-segmented ROI-based fitting and a newly proposed blood velocity SDdistribution (BVD) fitting technique were performed to obtain IVIM estimates in the right and left liver lobes. Fitting quality was evaluated using corrected Akaike information criterion. Precision metrics (test-retest repeatability, inter-reader reproducibility, and inter-lobar agreement) were evaluated using Bland-Altman analysis, repeatability/reproducibility coefficients (RPCs), and paired sample t-tests. Precision was compared across acquisitions and fitting methods.

High repeatability and reproducibility was observed in the estimations of the diffusion coefficient (Dtri  = [1.03 ± 0.11] × 10-3  mm2 /s; RPCs ≤ 1.34 × 10-4  mm2 /s), perfusion fractions (F1  = 3.19 ± 1.89% and F2  = 16.4 ± 2.07%; RPCs ≤ 2.51%), and blood velocity SDs (Vb,1  = 1.44 ± 0.14 mm/s and Vb,2  = 3.62 ± 0.13 mm/s; RPCs ≤ 0.41 mm/s) in the right liver lobe using the 2D (b-M1 ) acquisition in conjunction with BVD fitting. Using these methods, significantly larger (p < 0.01) estimates of Dtri and F1 were observed in the left lobe in comparison to the right lobe, while estimates of Vb,1 and Vb,2 demonstrated high interlobar agreement (RPCs ≤ 0.45 mm/s).

The 2D (b-M1 ) IVIM-DWI data acquisition in conjunction with BVD fitting enables highly precise tri-exponential IVIM quantification in the right liver lobe.

Multiple region-of-interest (ROI) sampling strategies have been described for liver fat quantification by MRI PDFF. While adult studies have shown that sampling strategies including as few as four ROIs provide a reasonable tradeoff between laboriousness and quantitative performance, there is a paucity of similar data for pediatric patients.

To assess agreement between different ROI sampling strategies for liver MRI PDFF analysis in children and adolescents.

This retrospective, internal review board-approved study included clinical MRI PDFF acquisitions for 50 children and adolescents. Four different ROI sampling paradigms reported in the literature were reproduced to measure mean liver PDFF. An 18-ROI (2 in each Couinaud segment) paradigm was considered the reference standard. Spearman correlation, intraclass correlation coefficients (ICCs), and Bland-Altman analyses were used to quantify agreement.

Mean age for the 50 participants was 14 ± 2.5 years (range 8-17 years). Based on the 18-ROI paradigm, mean PDFF was significantly higher for the right lobe (24.0 ± 13.7% right, 22.0 ± 13.1% left; p = 0.001). PDFF values for each individual Couinaud segment were highly correlated with the reference standard (ρ = 0.977 to 0.993, p < 0.0001). PDFF values derived from all sampling paradigms, including strategies using large free-hand ROIs, were strongly correlated with the reference standard (ρ = 0.995 to 0.998, p < 0.0001) with excellent agreement (ICC range 0.995 to 0.998).

Liver PDFF sampling paradigms using large ROIs showed strong correlation, excellent agreement, and nonsignificant mean differences from a reference standard paradigm sampling every Couinaud segment in children. Paradigms that exclusively sample the right lobe may overestimate liver PDFF.

The use of quantitative MRI methods for assessment of the abdomen in children has become commonplace over the past decade. Increasingly employed methods include MR elastography, chemical shift encoded (CSE) MR imaging for determination of proton density fat fraction, diffusion-weighted imaging, and a variety of relaxometry techniques, such as T1 and T2* mapping. These techniques can be used in a variety of settings to distinguish normal from abnormal tissue as well as determine the severity of disease. The performance of quantitative MRI methods in the pediatric population presents unique challenges as compared to adult populations. These challenges relate to multiple factors, including patient size, pediatric physiology, inability to breath hold, and greater physical motion during the examination. The purpose of this review article is to review quantitative MRI methods that may be used in clinical practice to assess the pediatric abdomen and to discuss special considerations when performing these techniques in children.

To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features.

One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models.

The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05).

It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.

Liver Iron content is best correlated to total body iron stores and is thus the organ of choice for evaluation in iron overload diseases. Liver biopsy was the historic standard for iron evaluation, but the evaluation is localized, comes with increased risks due to its invasiveness, and is costly. MRI is now widely used for liver iron evaluation. The superparamagnetic properties of iron cause a disturbance in magnetic resonance imaging, which can be evaluated with various techniques. These include signal intensity ratio (SIR), T2 relaxometry, T2* relaxometry, and Dixon-based solutions. Each of the methods has its own advantages and disadvantages, and factors such as availability, ease of use, accuracy, reproducibility, and cost can all play a role in the ultimate technique used for liver iron quantification. Quantitative susceptibility mapping, and ultrashort TE sequences are promising supplemental methods, but are primarily used as research sequences. These may become more clinically accepted in the near future. Dual energy CT is also being explored as an alternative but is still in the nascent stages. Overall, accurate liver iron concentration is feasible with the current tools available at most MR imaging centers and is highly valuable for evaluation of iron overload diseases.

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.