Quantitative MRI of Diffuse Liver Disease: Current Applications and Future Directions.

As radiologic technology advances, quantitative imaging is becoming more prevalent in clinical practice. This article reviews quantitative hepatic MRI, specifically involving fat and iron deposition, by demonstrating how they were iteratively improved. These iterative improvements involved incorporating more knowledge about the physiology of liver disease and MRI physics to reduce the adverse effects caused by confounding factors. The relevant foundations of MRI physics and liver pathophysiology are briefly reviewed, followed by the various improvements made by expanding on this foundational knowledge. Results from the literature are then discussed within this context, validating the improvement of these resultant methods into clinically robust and useful techniques. Fibrosis quantification, which has been more difficult to robustly perform in clinical practice, is similarly reviewed in an online appendix, with proposals for future multiparametric directions to improve performance on the basis of the insights gained from fat and iron quantification in the liver.

Epidemiology of Hepatic Steatosis at a Tertiary Care Center: An MRI-based Analysis.

RATIONALE AND OBJECTIVES: Little is known about the frequency and risk factors of hepatic steatosis in the tertiary care setting. Such knowledge is essential to clinicians making decisions about testing for this condition. Thus, our aim was to describe the epidemiology of hepatic steatosis, as captured by magnetic resonance imaging (MRI), at a tertiary care center. MATERIALS AND METHODS: A near-consecutive cohort of 1006 adult patients underwent standard-of-care liver MRIs. Images were retrospectively processed to derive proton density fat fraction (PDFF) maps. Data from three spatially distinct regions of interest (ROIs) were aggregated to derive overall hepatic PDFF values. Demographic, anthropometric, clinical, and laboratory variables were included in a multivariate analysis to determine predictors of hepatic steatosis grades (based on established PDFF cutoffs). Hepatic steatosis grades derived from single vs aggregated ROIs were compared. RESULTS: Hepatic steatosis was observed in 25% of patients (19% grade 1; 3% grade 2; 3% grade 3). Controlling for all other variables, the odds of hepatic steatosis increased by 7%-9% (P <.001) for each whole point increase in body mass index (BMI), whereas elevated serum bilirubin was associated with lower odds of hepatic steatosis (P = .002). Race, diabetes mellitus, dyslipidemia, and metabolic syndrome were not independently predictive of hepatic steatosis when controlling for other variables (eg, BMI). Employing single ROIs (rather than three aggregated ROIs) resulted in incorrect steatosis grading in up to 8.0% of patients. CONCLUSION: Many adult patients undergoing liver MRI at a tertiary care center have hepatic steatosis, with larger BMIs as the only independent predictor of higher grades. This information can be used by clinicians at such centers to make evidence-based decisions about when to test for hepatic steatosis in their patients.

Neurofeedback of two motor functions using supervised learning-based real-time functional magnetic resonance imaging.

This study examines the effects of neurofeedback provided by support vector machine (SVM) classification-based real-time functional magnetic resonance imaging (rt-fMRI) during two types of motor tasks. This approach also enables the examination of the neural regions associated with predicting mental states in different domains of motor control, which is critical to further our understanding of normal and impaired function. Healthy volunteers (n = 13) performed both a simple button tapping task, and a covert rate-of-speech counting task. The average prediction accuracy was approximately 95% for the button tapping task and 86% for the speech task. However, subsequent offline analysis revealed that classification of the initial runs was significantly lower - 75% (p<0.001) for button and 72% (p<0.005) for speech. To explore this effect, a group analysis was performed using the spatial maps derived from the SVM models, which showed significant differences between the two fMRI runs. One possible explanation for the difference in spatial patterns and the asymmetry in the prediction accuracies is that when subjects are actively engaged in the task (i.e. when they are trying to control a computer interface), they are generating stronger BOLD responses in terms of both intensity and spatial extent.