Validation of a data-driven motion-compensated PET brain image reconstruction algorithm in clinical patients using four radiotracers.

ABSTRACT:

Purpose

Patients with dementia symptoms often struggle to limit movements during PET examinations, necessitating motion compensation in brain PET imaging to ensure the high image quality needed for diagnostic accuracy. This study validates a data-driven motion-compensated (MoCo) PET brain image reconstruction algorithm that corrects head motion by integrating the detected motion frames and their associated rigid body transformations into the iterative image reconstruction. Validation was conducted using phantom scans, healthy volunteers, and clinical patients using four radiotracers with distinct tracer activity distributions.

Methods

We conducted technical validation experiments of the algorithm using Hoffman brain phantom scans during a series of controlled movements, foll

SUBMITTER: Munk OL 

PROVIDER: S-EPMC11790531 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature