Motion correction for PET using subspace-based real-time MR imaging in simultaneous PET/MR

TL;DR

This paper introduces a real-time MR imaging-based motion correction method for PET that effectively captures complex irregular motions, significantly improving PET image quality during motion artifacts.

Contribution

It presents a novel subspace-based MR imaging approach that estimates nonrigid motion fields in real-time, enhancing PET motion correction over existing methods.

Findings

Accurately captures irregular respiratory and bulk motion.

Outperforms state-of-the-art dynamic MR reconstruction techniques.

Reduces motion artifacts in PET images.

Abstract

Image quality of PET reconstructions is degraded by subject motion occurring during the acquisition. MR-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of the low-rank characteristics of dynamic MR images to reconstruct high-resolution MR images at high frame rates from highly undersampled k-space data. Reconstructed dynamic MR images are used to determine motion phases for PET reconstruction and estimate phase-to-phase nonrigid motion fields able to capture complex motion patterns such as irregular respiratory and bulk motion. MR-derived binning and motion fields are used for PET reconstruction to generate motion-corrected PET images. The proposed method was evaluated on in vivo data with irregular motion patterns. MR reconstructions accurately captured motion, outperforming state-of-the-art dynamic MR reconstruction techniques. Evaluation of PET reconstructions demonstrated the benefits of the proposed method over standard methods in terms of motion artifact reduction. The proposed method can improve the image quality of motion-corrected PET reconstructions in clinical applications.