Dynamic frame-by-frame motion correction for ¹⁸F-flurpiridaz PET-MPI using convolution neural network.

ABSTRACT:

Purpose

Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in ¹⁸F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of ¹⁸F-flurpiridaz PET.

Methods

The method employs a 3D-ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32-sites of a Phase-III clinical trial (NCT01347710). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness.

SUBMITTER: Urs M 

PROVIDER: S-EPMC12834570 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature