In myocardial perfusion PET, images are acquired during vasodilator stress, increasing the likelihood of intra-frame motion blurring of the heart in reconstructed static images to assess relative perfusion. This work evaluated a prototype data-driven motion correction (DDMC) algorithm designed specifically for cardiac PET. A cardiac torso phantom, with a solid defect, was scanned stationary and being manually pulled to-and-fro in the axial direction with a random motion. Non-motion-corrected (NMC) and DDMC images were reconstructed. Total perfusion deficit was measured in the defect and profiles through the cardiac insert were defined. In addition, 46 static perfusion images from 36 rubidium-82 MPI patients were selected based upon a perception of motion blurring in the images. NMC and DDMC images were reconstructed, blinded, and scored on image quality and perceived motion. Phantom data demonstrated near-perfect recovery of myocardial wall visualization and defect quantification with DDMC compared with the stationary phantom. Quality of clinical images was NMC: 10 non-diagnostic, 31 adequate, and 5 good; DDMC images: 0 non-diagnostic, 6 adequate, and 40 good. The DDMC algorithm shows great promise in rubidium MPI PET with substantial improvements in image quality and the potential to salvage images considered non-diagnostic due to significant motion blurring.

中文翻译：

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SiPM PET-CT 系统上铷 82 高时间分辨率数据驱动运动校正算法的初步评估

在心肌灌注 PET 中，图像是在血管舒张应激期间采集的，这增加了重建静态图像中心脏帧内运动模糊的可能性，以评估相对灌注。这项工作评估了专为心脏 PET 设计的原型数据驱动运动校正 (DDMC) 算法。对具有实体缺损的心脏躯干模型进行静止扫描，并以随机运动手动沿轴向来回拉动。重建非运动校正 (NMC) 和 DDMC 图像。测量缺损处的总灌注不足，并定义通过心脏插入物的轮廓。此外，根据图像中运动模糊的感知，选择了来自 36 名铷-82 MPI 患者的 46 张静态灌注图像。NMC 和 DDMC 图像经过重建、盲化并根据图像质量和感知运动进行评分。体模数据表明，与静止体模相比，DDMC 的心肌壁可视化和缺陷量化恢复近乎完美。临床图像质量为 NMC：10 幅无法诊断，31 幅合格，5 幅良好；DDMC 图像：0 个非诊断图像，6 个足够图像，40 个良好图像。DDMC 算法在铷 MPI PET 中显示出巨大的前景，可显着提高图像质量，并有可能挽救因严重运动模糊而被视为非诊断性的图像。