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

Liver Imaging Reporting and Data System: Review of Ancillary Imaging Features.

The American College of Radiology supported Liver Imaging Reporting And Data System (LI-RADS) is a comprehensive system for standardized interpretation and reporting of imaging examinations performed in patients at risk for hepatocellular carcinoma (HCC). As reviewed in the first article of a two-part series, LI-RADS uses 5 major imaging features to categorize LR-3, LR-4, and LR-5 observations. The major features are arterial phase enhancement, washout appearance, capsule appearance, diameter, and threshold growth. In addition to the major imaging features, LI-RADS uses ancillary imaging features to adjust the LI-RADS category to increase or decrease the suspicion for HCC. In this second article of a two-part series, we would discuss and illustrate a selection of LI-RADS ancillary imaging features

Liver Imaging Reporting and Data System: Review of Major Imaging Features.

The purpose of this article is to review and illustrate the Liver Imaging Reporting and Data System (LI-RADS) major features for interpretation and reporting of liver imaging examinations performed in patients at risk for hepatocellular carcinoma (HCC)

Spontaneous complete remission of type 1 diabetes mellitus in an adult – review and case report

Type 1 diabetes mellitus (T1DM) is an autoimmune condition that results in low plasma insulin levels by destruction of beta cells of the pancreas. As part of the natural progression of this disease, some patients regain beta cell activity transiently. This period is often referred to as the ‘honeymoon period’ or remission of T1DM. During this period, patients manifest improved glycemic control with reduced or no use of insulin or anti-diabetic medications. The incidence rates of remission and duration of remission is extremely variable. Various factors seem to influence the remission rates and duration. These include but are not limited to C-peptide level, serum bicarbonate level at the time of diagnosis, duration of T1DM symptoms, haemoglobin A1C (HbA1C) levels at the time of diagnosis, sex, and age of the patient. Mechanism of remission is not clearly understood. Extensive research is ongoing in regard to the possible prevention and reversal of T1DM. However, most of the studies that showed positive results were small and uncontrolled. We present a 32-year-old newly diagnosed T1DM patient who presented with diabetic ketoacidosis (DKA) and HbA1C of 12.7%. She was on basal bolus insulin regimen for the first 4 months after diagnosis. Later, she stopped taking insulin and other anti-diabetic medications due to compliance and logistical issues. Eleven months after diagnosis, her HbA1C spontaneously improved to 5.6%. Currently (14 months after T1DM diagnosis), she is still in complete remission, not requiring insulin therapy

Pilot study on longitudinal change in pancreatic proton density fat fraction during a weight‐loss surgery program in adults with obesity

BACKGROUND:Quantitative-chemical-shift-encoded (CSE)-MRI methods have been applied to the liver. The feasibility and potential utility CSE-MRI in monitoring changes in pancreatic proton density fat fraction (PDFF) have not yet been demonstrated. PURPOSE:To use quantitative CSE-MRI to estimate pancreatic fat changes during a weight-loss program in adults with severe obesity and nonalcoholic fatty liver disease (NAFLD). To explore the relationship of reduction in pancreatic PDFF with reductions in anthropometric indices. STUDY TYPE:Prospective/longitudinal. POPULATION:Nine adults with severe obesity and NAFLD enrolled in a weight-loss program. FIELD STRENGTH/SEQUENCE:CSE-MRI fat quantification techniques and multistation-volumetric fat/water separation techniques were performed at 3 T. ASSESSMENT:PDFF values were recorded from parametric maps colocalized across timepoints. STATISTICAL TESTS:Rates of change of log-transformed variables across time were determined (linear-regression), and their significance assessed compared with no change (Wilcoxon test). Rates of change were correlated pairwise (Spearman's correlation). RESULTS:Mean pancreatic PDFF decreased by 5.7% (range 0.7-17.7%) from 14.3 to 8.6%, hepatic PDFF by 11.4% (2.6-22.0%) from 14.8 to 3.4%, weight by 30.9 kg (17.3-64.2 kg) from 119.0 to 88.1 kg, body mass index by 11.0 kg/m2 (6.3-19.1 kg/m2 ) from 44.1 to 32.9 kg/m2 , waist circumference (WC) by 25.2 cm (4.0-41.0 cm) from 133.1 to 107.9 cm, HC by 23.5 cm (4.5-47.0 cm) from 135.8 to 112.3 cm, visceral adipose tissue (VAT) by 2.9 L (1.7-5.7 L) from 7.1 to 4.2 L, subcutaneous adipose tissue (SCAT) by 4.0 L (2.9-7.4 L) from 15.0 to 11.0 L. Log-transformed rate of change for pancreatic PDFF was moderately correlated with log-transformed rates for hepatic PDFF, VAT, SCAT, and WC (ρ = 0.5, 0.47, 0.45, and 0.48, respectively), although not statistically significant. DATA CONCLUSION:Changes in pancreatic PDFF can be estimated by quantitative CSE-MRI in adults undergoing a weight-loss surgery program. Pancreatic and hepatic PDFF and anthropometric indices decreased significantly. LEVEL OF EVIDENCE:2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1092-1102

Pilot study on longitudinal change in pancreatic proton density fat fraction during a weight-loss surgery program in adults with obesity.

BACKGROUND:Quantitative-chemical-shift-encoded (CSE)-MRI methods have been applied to the liver. The feasibility and potential utility CSE-MRI in monitoring changes in pancreatic proton density fat fraction (PDFF) have not yet been demonstrated. PURPOSE:To use quantitative CSE-MRI to estimate pancreatic fat changes during a weight-loss program in adults with severe obesity and nonalcoholic fatty liver disease (NAFLD). To explore the relationship of reduction in pancreatic PDFF with reductions in anthropometric indices. STUDY TYPE:Prospective/longitudinal. POPULATION:Nine adults with severe obesity and NAFLD enrolled in a weight-loss program. FIELD STRENGTH/SEQUENCE:CSE-MRI fat quantification techniques and multistation-volumetric fat/water separation techniques were performed at 3 T. ASSESSMENT:PDFF values were recorded from parametric maps colocalized across timepoints. STATISTICAL TESTS:Rates of change of log-transformed variables across time were determined (linear-regression), and their significance assessed compared with no change (Wilcoxon test). Rates of change were correlated pairwise (Spearman's correlation). RESULTS:Mean pancreatic PDFF decreased by 5.7% (range 0.7-17.7%) from 14.3 to 8.6%, hepatic PDFF by 11.4% (2.6-22.0%) from 14.8 to 3.4%, weight by 30.9 kg (17.3-64.2 kg) from 119.0 to 88.1 kg, body mass index by 11.0 kg/m2 (6.3-19.1 kg/m2 ) from 44.1 to 32.9 kg/m2 , waist circumference (WC) by 25.2 cm (4.0-41.0 cm) from 133.1 to 107.9 cm, HC by 23.5 cm (4.5-47.0 cm) from 135.8 to 112.3 cm, visceral adipose tissue (VAT) by 2.9 L (1.7-5.7 L) from 7.1 to 4.2 L, subcutaneous adipose tissue (SCAT) by 4.0 L (2.9-7.4 L) from 15.0 to 11.0 L. Log-transformed rate of change for pancreatic PDFF was moderately correlated with log-transformed rates for hepatic PDFF, VAT, SCAT, and WC (ρ = 0.5, 0.47, 0.45, and 0.48, respectively), although not statistically significant. DATA CONCLUSION:Changes in pancreatic PDFF can be estimated by quantitative CSE-MRI in adults undergoing a weight-loss surgery program. Pancreatic and hepatic PDFF and anthropometric indices decreased significantly. LEVEL OF EVIDENCE:2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1092-1102

MRI proton density fat fraction is robust across the biologically plausible range of triglyceride spectra in adults with nonalcoholic steatohepatitis.

BackgroundProton density fat fraction (PDFF) estimation requires spectral modeling of the hepatic triglyceride (TG) signal. Deviations in the TG spectrum may occur, leading to bias in PDFF quantification.PurposeTo investigate the effects of varying six-peak TG spectral models on PDFF estimation bias.Study typeRetrospective secondary analysis of prospectively acquired clinical research data.PopulationForty-four adults with biopsy-confirmed nonalcoholic steatohepatitis.Field strength/sequenceConfounder-corrected chemical-shift-encoded 3T MRI (using a 2D multiecho gradient-recalled echo technique with magnitude reconstruction) and MR spectroscopy.AssessmentIn each patient, 61 pairs of colocalized MRI-PDFF and MRS-PDFF values were estimated: one pair used the standard six-peak spectral model, the other 60 were six-peak variants calculated by adjusting spectral model parameters over their biologically plausible ranges. MRI-PDFF values calculated using each variant model and the standard model were compared, and the agreement between MRI-PDFF and MRS-PDFF was assessed.Statistical testsMRS-PDFF and MRI-PDFF were summarized descriptively. Bland-Altman (BA) analyses were performed between PDFF values calculated using each variant model and the standard model. Linear regressions were performed between BA biases and mean PDFF values for each variant model, and between MRI-PDFF and MRS-PDFF.ResultsUsing the standard model, mean MRS-PDFF of the study population was 17.9 ± 8.0% (range: 4.1-34.3%). The difference between the highest and lowest mean variant MRI-PDFF values was 1.5%. Relative to the standard model, the model with the greatest absolute BA bias overestimated PDFF by 1.2%. Bias increased with increasing PDFF (P < 0.0001 for 59 of the 60 variant models). MRI-PDFF and MRS-PDFF agreed closely for all variant models (R2  = 0.980, P < 0.0001).Data conclusionOver a wide range of hepatic fat content, PDFF estimation is robust across the biologically plausible range of TG spectra. Although absolute estimation bias increased with higher PDFF, its magnitude was small and unlikely to be clinically meaningful.Level of evidence3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:995-1002