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

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images.

BackgroundLiver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to register cross-sectional liver imaging series and compared its performance to manual image registration.MethodsThree hundred fourteen patients, including internal and external datasets, who underwent gadoxetate disodium-enhanced magnetic resonance imaging for clinical care from 2011 to 2018, were retrospectively selected. Automated registration was applied to all 2,663 within-patient series pairs derived from these datasets. Additionally, 100 within-patient series pairs from the internal dataset were independently manually registered by expert readers. Liver overlap, image correlation, and intra-observation distances for manual versus automated registrations were compared using paired t tests. Influence of patient demographics, imaging characteristics, and liver uptake function was evaluated using univariate and multivariate mixed models.ResultsCompared to the manual, automated registration produced significantly lower intra-observation distance (p < 0.001) and higher liver overlap and image correlation (p < 0.001). Intra-exam automated registration achieved 0.88 mean liver overlap and 0.44 mean image correlation for the internal dataset and 0.91 and 0.41, respectively, for the external dataset. For inter-exam registration, mean overlap was 0.81 and image correlation 0.41. Older age, female sex, greater inter-series time interval, differing uptake, and greater voxel size differences independently reduced automated registration performance (p ≤ 0.020).ConclusionA fully automated algorithm accurately registered the liver within and between examinations, yielding better liver and focal observation co-localization compared to manual registration

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

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

A doubly hermaphroditic chiral crown ether

A single-crystal structure determination on the S-protected form of a chiral 18-crown-6 derivative known to be a selective catalyst for thiolysis reactions of amino acid derivatives has shown the molecule to crystallise in an unsolvated form where the macrocyclic ring has a conformation in which the dipoles of substituent amide units are aligned parallel. The resulting polar entities are linked through NH center dot center dot center dot O H-bonds and weaker interactions which can be considered to result in doubly hermaphroditic links, the whole crystal proving to be polar. The possible consequences of the observed secondary interactions, some being intramolecular, are considered in relation to the mechanism of catalysis by the isolated molecule

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