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成像过程中患者运动不可避免。其长采集时间不仅加剧运动及伪影，还增加患者不适，因此PET加速具有重要价值。然而，加速PET采集会导致重建图像信噪比降低，且图像质量仍会受到运动伪影的影响。既往多数PET运动校正方法具有运动类型特异性，需进行运动建模，当多种运动类型同时存在时可能失效。此外，这些方法专为标准长采集设计，无法直接应用于加速PET。为此，加速PET的无模型通用运动校正重建仍是一个亟待探索的领域。本文提出一种名为Fast-MC-PET的新型深度学习辅助运动校正与重建框架。该框架包含通用运动校正（UMC）模块和短-长采集重建（SL-Recon）模块。UMC通过从超短帧重建中估计准连续运动并利用该信息进行运动补偿重建，实现无模型运动校正。随后，SL-Recon将加速UMC图像的低计数信息转换为高计数高质量图像，作为最终重建输出。基于人体研究的实验结果表明，我们的Fast-MC-PET可实现7倍加速，仅需2分钟采集即可生成高质量重建图像，其性能优于或匹配采用标准15分钟长采集数据的既往运动校正重建方法。