Quantitative magnetic resonance imaging in evaluating haemodynamic changes in portal hypertension

The majority of complications in patients with cirrhosis result from the development and progression of portal hypertension characterised by increased intrahepatic resistance and progressive splanchnic vasodilation. Hepatic venous pressure gradient (HVPG) measurement is the only validated technique to accurately evaluate changes in portal pressure. However, HVPG measurements are invasive and available only in specialised hepatology units, precluding its use in routine clinical practice. In the first study, we evaluated the use of non-contrast quantitative magnetic resonance imaging (MRI) as a surrogate measure of portal pressure. 30 patients undergoing HVPG measurement were prospectively recruited. MR parameters of longitudinal relaxation time (T1), perfusion of the liver and spleen (by arterial spin labelling), and blood flow in the portal, splanchnic and collateral circulation (by phase-contrast MRI) were assessed. We estimated the liver stiffness measurement (LSM) and Enhanced Liver Fibrosis (ELF) score. The correlation of all non-invasive parameters with HVPG was evaluated. The mean (range) HVPG of the patients was 9.8 (1-22) mmHg, and 14 patients (48%) had clinically significant portal hypertension (CSPH, HVPG ≥10 mmHg). Liver T1 relaxation time, splenic artery and superior mesenteric artery velocity correlated significantly with HVPG. Using multiple linear regression, liver T1 and splenic artery velocity remained as the two parameters in the multivariate model significantly associated with HVPG (R=0.90, p<0.001). This correlation was maintained in patients with CSPH (R=0.85, p<0.001). A validation cohort (n=10) showed this linear model provided a good prediction of HVPG. LSM and ELF score correlated significantly with HVPG in the whole population but the correlation was absent in CSPH. In conclusion, MR parameters related to both hepatic architecture and splanchnic haemodynamics correlate significantly with HVPG. This proposed model, confirmed in a validation