Quantitative imaging of the liver with 18F-FDG PET/CT in patients with hepatic steatosis

  • Georgia Keramida

Student thesis: Doctoral Thesis


Background and Aims: Hepatic steatosis (≥10% fat within the liver) affects ~75% of obese and ~15% of non-obese adults. It leads to steatohepatitis (SH) in ~10% of patients. SH may then lead to cirrhosis. The primary aim of the thesis was to image the liver with FDG PET/CT to determine if glucose utilisation rate (MRglu) is increased in steatosis. Texture analysis of FDG PET/CT was also explored as a means of detecting steatosis and SH. Previous PET studies of steatosis quantified hepatic FDG uptake as the standardised uptake value (SUV). Methods: All patients were referred for routine PET/CT. ROI were placed over the liver on whole body scans for measurement of CT density, which is inversely proportional to hepatic fat content, and SUV. SUV may be expressed as the ‘hottest’ pixel (SUVmax) or as the average of all pixels in the ROI (SUVave). Dynamic imaging from 0-30 min post-injection was also done in 60 patients and hepatic FDG clearance and MRglu were measured using Patlak-Rutland analysis. Texture analysis of PET and CT scans was explored as possible means of identifying SH at an early stage. Results: SUV was overestimated in obese individuals when calculated using weight but not when using estimated lean body mass. SUVmax, but not SUVave, was increased in steatosis because SUVave is reduced in fatty liver as a result of ‘signal dilution’ by the fat. SUVmax/SUVave is therefore a marker of steatosis. Hepatic FDG clearance and MRglu were increased in steatosis, irrespective of BMI, but not in obese patients who did not have steatosis. Results of texture analysis were promising but inconclusive. Conclusions and future work: SUV has major shortcomings as a surrogate for FDG clearance but in general indicates increased MRglu in steatosis. Dynamic studies confirm that MRglu is increased in steatosis. SUVmax/SUVave is a marker of steatosis. Future work should aim to a) use dynamic FDG imaging to diagnose hepatic inflammation, and b) explore texture analysis to detect transition from steatosis to SH.
Date of AwardJun 2016
Original languageEnglish
Awarding Institution
  • University of Brighton

Cite this