Combination of complex-based and magnitude-based multiecho water-fat separation for accurate quantification of fat-fraction

Multipoint water-fat separation techniques rely on different water-fat phase shifts generated at multiple echo times to decompose water and fat. Therefore, these methods require complex source images and allow unambiguous separation of water and fat signals. However, complex-based water-fat separation methods are sensitive to phase errors in the source images, which may lead to clinically important errors. An alternative approach to quantify fat is through magnitude-based methods that acquire multiecho magnitude images. Magnitude-based methods are insensitive to phase errors, but cannot estimate fat-fraction greater than 50%. In this work, we introduce a water-fat separation approach that combines the strengths of both complex and magnitude reconstruction algorithms. A magnitude-based reconstruction is applied after complex-based water-fat separation to removes the effect of phase errors. The results from the two reconstructions are then combined. We demonstrate that using this hybrid method, 0-100% fat-fraction can be estimated with improved accuracy at low fat-fractions. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc. Copyright © 2011 Wiley-Liss, Inc

Liver imaging reporting and data system: An expert consensus statement

The increasing incidence and high morbidity and mortality of hepatocellular carcinoma (HCC) have inspired the creation of the Liver Imaging Reporting and Data System (LI-RADS). LI-RADS aims to reduce variability in exam interpretation, improve communication, facilitate clinical therapeutic decisions, reduce omission of pertinent information, and facilitate the monitoring of outcomes. LI-RADS is a dynamic process, which is updated frequently. In this article, we describe the LI-RADS 2014 version (v2014), which marks the second update since the initial version in 2011

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Consensus report from the 6th International forum for liver MRI using gadoxetic acid

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108276/1/jmri24419.pd

T1 independent, T2* corrected chemical shift based fat-water separation with multi-peak fat spectral modeling is an accurate and precise measure of hepatic steatosis

Purpose: To determine the precision and accuracy of hepatic fat-fraction measured with a chemical shift-based MRI fat-water separation method, using single-voxel MR spectroscopy (MRS) as a reference standard. Materials and Methods: In 42 patients, two repeated measurements were made using a T 1-independent, T2 *-corrected chemical shift-based fat-water separation method with multi-peak spectral modeling of fat, and T 2-corrected single voxel MR spectroscopy. Precision was assessed through calculation of Bland-Altman plots and concordance correlation intervals. Accuracy was assessed through linear regression between MRI and MRS. Sensitivity and specificity of MRI fat-fractions for diagnosis of steatosis using MRS as a reference standard were also calculated. Results: Statistical analysis demonstrated excellent precision of MRI and MRS fat-fractions, indicated by 95% confidence intervals (units of absolute percent) of [-2.66%,2.64%] for single MRI ROI measurements, [-0.81%,0.80%] for averaged MRI ROI, and [-2.70%,2.87%] for single-voxel MRS. Linear regression between MRI and MRS indicated that the MRI method is highly accurate. Sensitivity and specificity for detection of steatosis using averaged MRI ROI were 100% and 94%, respectively. The relationship between hepatic fat-fraction and body mass index was examined. Conclusion: Fat-fraction measured with T1-independent T 2*-corrected MRI and multi-peak spectral modeling of fat is a highly precise and accurate method of quantifying hepatic steatosis. © 2011 Wiley-Liss, Inc

Insulin resistance drives hepatic de novo lipogenesis in nonalcoholic fatty liver disease

BACKGROUNDAn increase in intrahepatic triglyceride (IHTG) is the hallmark feature of nonalcoholic fatty liver disease (NAFLD) and is decreased by weight loss. Hepatic de novo lipogenesis (DNL) contributes to steatosis in individuals with NAFLD. The physiological factors that stimulate hepatic DNL and the effect of weight loss on hepatic DNL are not clear.METHODSHepatic DNL, 24-hour integrated plasma insulin and glucose concentrations, and both liver and whole-body insulin sensitivity were determined in individuals who were lean (n = 14), obese with normal IHTG content (n = 26), or obese with NAFLD (n = 27). Hepatic DNL was assessed using the deuterated water method corrected for the potential confounding contribution of adipose tissue DNL. Liver and whole-body insulin sensitivity was assessed using the hyperinsulinemic-euglycemic clamp procedure in conjunction with glucose tracer infusion. Six subjects in the obese-NAFLD group were also evaluated before and after a diet-induced weight loss of 10%.RESULTSThe contribution of hepatic DNL to IHTG-palmitate was 11%, 19%, and 38% in the lean, obese, and obese-NAFLD groups, respectively. Hepatic DNL was inversely correlated with hepatic and whole-body insulin sensitivity, but directly correlated with 24-hour plasma glucose and insulin concentrations. Weight loss decreased IHTG content, in conjunction with a decrease in hepatic DNL and 24-hour plasma glucose and insulin concentrations.CONCLUSIONSThese data suggest hepatic DNL is an important regulator of IHTG content and that increases in circulating glucose and insulin stimulate hepatic DNL in individuals with NAFLD. Weight loss decreased IHTG content, at least in part, by decreasing hepatic DNL.TRIAL REGISTRATIONClinicalTrials.gov NCT02706262.FUNDINGThis study was supported by NIH grants DK56341 (Nutrition Obesity Research Center), DK20579 (Diabetes Research Center), DK52574 (Digestive Disease Research Center), and RR024992 (Clinical and Translational Science Award), and by grants from the Academy of Nutrition and Dietetics Foundation, the College of Natural Resources of UCB, and the Pershing Square Foundation

Liver imaging : it is time to adopt standardized terminology

Liver imaging plays a vital role in the management of patients at risk for hepatocellular carcinoma (HCC); however, progress in the field is challenged by nonuniform and inconsistent terminology in the published literature. The Steering Committee of the American College of Radiology (ACR)’s Liver Imaging Reporting And Data System (LI-RADS), in conjunction with the LI-RADS Lexicon Writing Group and the LI-RADS International Working Group, present this consensus document to establish a single universal liver imaging lexicon. The lexicon is intended for use in research, education, and clinical care of patients at risk for HCC (i.e., the LI-RADS population) and in the general population (i.e., even when LI-RADS algorithms are not applicable). We anticipate that the universal adoption of this lexicon will provide research, educational, and clinical benefits

Agreement Between Magnetic Resonance Imaging Proton Density Fat Fraction Measurements and Pathologist-assigned Steatosis Grades of Liver Biopsies from Adults with Nonalcoholic Steatohepatitis

Background & Aims We assessed the diagnostic performance of magnetic resonance imaging (MRI) proton density fat fraction (PDFF) in grading hepatic steatosis and change in hepatic steatosis in adults with nonalcoholic steatohepatitis (NASH) in a multi-center study, using central histology as reference. Methods We collected data from 113 adults with NASH participating in a multi-center, randomized, double-masked, placebo-controlled, phase 2b trial to compare the efficacy cross-sectionally and longitudinally of obeticholic acid vs placebo. Hepatic steatosis was assessed at baseline and after 72 weeks of obeticholic acid or placebo by liver biopsy and MRI (scanners from different manufacturers, at 1.5T or 3T). We compared steatosis estimates by PDFF vs histology. Histologic steatosis grade was scored in consensus by a pathology committee. Cross-validated receiver operating characteristic (ROC) analyses were performed. Results At baseline, 34% of subjects had steatosis grade 0 or 1, 39% had steatosis grade 2, and 27% had steatosis grade 3; corresponding mean PDFF values were 9.8%±3.7%, 18.1%±4.3%, and 30.1%±8.1%. PDFF classified steatosis grade 0–1 vs 2–3 with an area under the ROC curve (AUROC) of 0.95 (95% CI, 0.91–0.98), and grade 0–2 vs grade 3 steatosis with an AUROC of 0.96 (95% CI, 0.93–0.99). PDFF cut-off values at 90% specificity were 16.3% for grades 2–3 and 21.7% for grade 3, with corresponding sensitivities of 83% and 84%. After 72 weeks' of obeticholic vs placebo, 42% of subjects had a reduced steatosis grade (mean reduction in PDFF from baseline of 7.4%±8.7%), 49% had no change in steatosis grade (mean increase in PDFF from baseline of 0.3%±6.3%), and 9% had an increased steatosis grade (mean increase in PDFF from baseline of 7.7%±6.0%). PDFF change identified subjects with reduced steatosis grade with an AUROC of 0.81 (95% CI, 0.71–0.91) and increased steatosis grade with an AUROC of 0.81 (95% CI, 0.63–0.99). A PDFF reduction of 5.15% identified subjects with reduced steatosis grade with 90% specificity and 58% sensitivity, whereas a PDFF increase of 5.6% identified those with increased steatosis grade with 90% specificity and 57% sensitivity. Conclusions Based on data from a phase 2 randomized controlled trial of adults with NASH, PDFF estimated by MRI scanners of different field strength and at different sites, accurately classifies grades and changes in hepatic steatosis when histologic analysis of biopsies is used as a reference

Consensus report from the 9th International Forum for Liver Magnetic Resonance Imaging: applications of gadoxetic acid-enhanced imaging

Objectives The 9th International Forum for Liver Magnetic Resonance Imaging (MRI) was held in Singapore in September 2019, bringing together radiologists and allied specialists to discuss the latest developments in and formulate consensus statements for liver MRI, including the applications of gadoxetic acid-enhanced imaging. Methods As at previous Liver Forums, the meeting was held over 2 days. Presentations by the faculty on days 1 and 2 and breakout group discussions on day 1 were followed by delegate voting on consensus statements presented on day 2. Presentations and discussions centered on two main meeting themes relating to the use of gadoxetic acid-enhanced MRI in primary liver cancer and metastatic liver disease. Results and conclusions Gadoxetic acid-enhanced MRI offers the ability to monitor response to systemic therapy and to assist in pre-surgical/pre-interventional planning in liver metastases. In hepatocellular carcinoma, gadoxetic acid-enhanced MRI provides precise staging information for accurate treatment decision-making and follow-up post therapy. Gadoxetic acid-enhanced MRI also has potential, currently investigational, indications for the functional assessment of the liver and the biliary system. Additional voting sessions at the Liver Forum debated the role of multidisciplinary care in the management of patients with liver disease, evidence to support the use of abbreviated imaging protocols, and the importance of standardizing nomenclature in international guidelines in order to increase the sharing of scientific data and improve the communication between centers