MR-assisted PET respiratory motion correction using deep-learning based short-scan motion fields.

ABSTRACT:

Purpose

We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan.

Methods

The evaluation of PET MoCo was performed in a respiratory motion phantom study with varying lesion sizes and tumor to background ratios (TBRs) using a static scan as the ground truth. MRI-based MVFs were derived from either 2000 spokes (MoCo₂₀₀₀ , 5-6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCo_P2P200 , 30-40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesion

SUBMITTER: Chen S 

PROVIDER: S-EPMC11459372 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature