Cerebral blood flow measurements with ¹⁵O-water PET using a non-invasive machine-learning-derived arterial input function.

ABSTRACT: Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of ¹⁵O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.Twenty-five subjects underwent two 10 min dynamic ¹⁵O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-ba

SUBMITTER: Kuttner S 

PROVIDER: S-EPMC8392760 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature