Patient motion during PET is inevitable. Its long acquisition time not only
increases the motion and the associated artifacts but also the patient's
discomfort, thus PET acceleration is desirable. However, accelerating PET
acquisition will result in reconstructed images with low SNR, and the image
quality will still be degraded by motion-induced artifacts. Most of the
previous PET motion correction methods are motion type specific that require
motion modeling, thus may fail when multiple types of motion present together.
Also, those methods are customized for standard long acquisition and could not
be directly applied to accelerated PET. To this end, modeling-free universal
motion correction reconstruction for accelerated PET is still highly
under-explored. In this work, we propose a novel deep learning-aided motion
correction and reconstruction framework for accelerated PET, called
Fast-MC-PET. Our framework consists of a universal motion correction (UMC) and
a short-to-long acquisition reconstruction (SL-Reon) module. The UMC enables
modeling-free motion correction by estimating quasi-continuous motion from
ultra-short frame reconstructions and using this information for
motion-compensated reconstruction. Then, the SL-Recon converts the accelerated
UMC image with low counts to a high-quality image with high counts for our
final reconstruction output. Our experimental results on human studies show
that our Fast-MC-PET can enable 7-fold acceleration and use only 2 minutes
acquisition to generate high-quality reconstruction images that
outperform/match previous motion correction reconstruction methods using
standard 15 minutes long acquisition data.Comment: Accepted at Information Processing in Medical Imaging (IPMI 2023