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.

翻译：PET 期间的病人运动是不可避免的。 其长期的购买时间不仅会增加运动和相关的人工制品,而且会增加病人的不适状态, 因此PET的加速是可取的。 但是, 加速PET的获取将导致以低SNR重建图像, 图像质量仍然会因运动引发的人工制品而退化。 以前的PET运动修正方法大多是运动型的, 需要运动模型, 因此当多种类型的运动同时出现时可能会失败。 此外, 这些方法为标准的长期购买定制, 并且不能直接应用于加速的 PET 。 为此, 为加速的PET 重建而建模的无限制通用运动重建, 仍然极慢。 在这项工作中, 我们提出了一个新的全新的学习辅助运动的调整和重建框架, 称为快速MC- PET 。 我们的框架是通用运动校正(UMC) 和短到长到长的购置模块(SL- Recon) 。 UMC 能够通过估计超短时间框架重建的准持续动作, 并且使用这一信息, 快速重建的快速重建(S- Reach) 15 将我们的快速重建结果转换为我们快速的快速的图像, 。