Automated extraction of the arterial input function from brain images for parametric PET studies.

ABSTRACT:

Background

Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images.

Results

Total body ¹⁸F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish a

SUBMITTER: Moradi H 

PROVIDER: S-EPMC11372015 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature