Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning.

ABSTRACT: Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change across different dynamic frames over time, which can cause difficulties for image registration-based motion correction, particularly for early dynamic frames. In this paper, we developed an automatic deep learning-based motion correction (DeepMC) method for dynamic cardiac PET. In this study we focused on the detection and correction of inter-frame rigid translational motion caused by voluntary body movement and pattern change of respiratory motion. A bidirectional-3D LSTM network was developed to fully utilize both local and nonlocal temporal

SUBMITTER: Shi L 

PROVIDER: S-EPMC8670362 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature