Non-invasive estimation of hepatic blood perfusion from H2 15O PET images using tissue-derived arterial and portal input functions

• Published in: European journal of nuclear medicine and molecular imaging Vol. 35; no. 10; pp. 1899 - 1911
• Main Authors: Kudomi, N, Slimani, L, Järvisalo, M J, Kiss, J, Lautamäki, R, Naum, G A, Savunen, T, Knuuti, J, Iida, H, Nuutila, P, Iozzo, P
• Format: Journal Article
• Language: English
• Published: Germany Springer Nature B.V 01-10-2008
• Subjects: Algorithms; Animals; Blood Flow Velocity - physiology; Hepatic Artery - physiology; Image Interpretation, Computer-Assisted - methods; Liver; Liver - blood supply; Liver - physiology; Nuclear medicine; Oxygen Radioisotopes; Positron-Emission Tomography - methods; Reproducibility of Results; Sensitivity and Specificity; Swine; Tomography; Veins & arteries; Water
• ISSN: 1619-7070; 1619-7089
• DOI: 10.1007/s00259-008-0796-z

The liver is perfused through the portal vein and the hepatic artery. When its perfusion is assessed using positron emission tomography (PET) and (15)O-labeled water (H(2) (15)O), calculations require a dual blood input function (DIF), i.e., arterial and portal blood activity curves. The former can be generally obtained invasively, but blood withdrawal from the portal vein is not feasible in humans. The aim of the present study was to develop a new technique to estimate quantitative liver perfusion from H(2) (15)O PET images with a completely non-invasive approach.
We studied normal pigs (n=14) in which arterial and portal blood tracer concentrations and Doppler ultrasonography flow rates were determined invasively to serve as reference measurements. Our technique consisted of using model DIF to create tissue model function and the latter method to simultaneously fit multiple liver time-activity curves from images. The parameters obtained reproduced the DIF. Simulation studies were performed to examine the magnitude of potential biases in the flow values and to optimize the extraction of multiple tissue curves from the image.
The simulation showed that the error associated with assumed parameters was <10%, and the optimal number of tissue curves was between 10 and 20. The estimated DIFs were well reproduced against the measured ones. In addition, the calculated liver perfusion values were not different between the methods and showed a tight correlation (r=0.90).
In conclusion, our results demonstrate that DIF can be estimated directly from tissue curves obtained through H(2) (15)O PET imaging. This suggests the possibility to enable completely non-invasive technique to assess liver perfusion in patho-physiological studies.