Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
Magn Reson Med. 2022 Aug;88(2):676-690. doi: 10.1002/mrm.29233. Epub 2022 Mar 28.
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.
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 (MoCo , 5-6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCo , 30-40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesions, the performance of PET MoCo was evaluated using quantitative metrics (SUV , SUV , SUV , lesion volume) and a blinded radiological review on lesion conspicuity.
MRI-assisted PET MoCo methods provided similar results to static scans across most lesions with varying TBRs in the phantom. Both MoCo and MoCo PET images had significantly higher SUV , SUV , SUV and significantly lower lesion volume than non-motion-corrected (non-MoCo) PET images. There was no statistical difference between MoCo and MoCo PET images for SUV , SUV , SUV or lesion volume. Both radiological reviewers found that MoCo and MoCo PET significantly improved lesion conspicuity.
An MRI-assisted PET MoCo method was evaluated using the ground truth in a phantom study. In patients with hepatic lesions, PET MoCo images improved quantitative and qualitative metrics based on only 30-40 s of MRI motion modeling data.
我们评估了 PET 呼吸运动校正（MoCo）在体模和患者中的影响。此外，我们提出并检验了一种使用深度学习重建的短 MRI 扫描的运动矢量场（MVF）的 PET MoCo 方法。
在具有不同病变大小和肿瘤与背景比（TBR）的呼吸运动体模研究中，使用静态扫描作为基准来评估 PET MoCo。基于 MRI 的 MVF 源自使用傅里叶变换重建的 2000 个辐条（MoCo，5-6 分钟采集时间）或使用深度学习 Phase2Phase（P2P）重建的 200 个辐条（MoCo，30-40 秒采集时间），然后将其纳入 PET MoCo 重建中。对于六名肝部病变患者，使用定量指标（SUV、SUV、SUV、病变体积）和病变显著性的盲法放射学评价评估 PET MoCo 的性能。
在体模中具有不同 TBR 的大多数病变中，MRI 辅助的 PET MoCo 方法与静态扫描提供了相似的结果。MoCo 和 MoCo PET 图像的 SUV、SUV、SUV 均显著高于非运动校正（非-MoCo）PET 图像，病变体积显著低于非-MoCo PET 图像。MoCo 和 MoCo PET 图像在 SUV、SUV、SUV 或病变体积方面没有统计学差异。两位放射学评论员均发现 MoCo 和 MoCo PET 显著提高了病变的显著性。
使用体模研究中的基准评估了一种 MRI 辅助的 PET MoCo 方法。在患有肝部病变的患者中，仅使用 30-40 秒的 MRI 运动建模数据即可改善 PET MoCo 图像的定量和定性指标。
