背景:心脏运动经常降低PET图像的可解释性。本研究利用原型数据驱动运动校正(DDMC)算法生成校正图像,并将DDMC图像与非校正图像(NMC)进行比较,以评估图像质量以及灌注缺陷大小和严重程度的变化。
方法:由2名盲人研究者在4点视觉序数量表(0:最小运动;1:轻度运动;2:中度运动;3:重度运动/无法解释)上对来自40例连续运动患者的NMC和DDMC的休息和压力图像进行评分。还使用Dwell分数对运动进行了量化,这是运动矢量显示心脏在校正位置的6mm内的时间分数,并且是从NMC图像的列表模式数据导出的。
结果:在15%的患者中看到最小的运动,而40%,30%,15%的患者有轻度、中度和重度运动,分别。所有校正后的图像均显示质量改善,并且在处理后可解释。运动量化的机器测量和医师解释之间的显著相关性(Spearman相关系数0.626,P<.001)证实了这一点。
结论:新的DDMC算法提高了运动心脏PET图像的质量。运动量化的机器测量与医生解释之间的相关性显着。
Cardiac motion frequently reduces the interpretability of PET images. This study utilized a prototype data-driven motion correction (DDMC) algorithm to generate corrected images and compare DDMC images with non-corrected images (NMC) to evaluate image quality and change of perfusion defect size and severity.
Rest and stress images with NMC and DDMC from 40 consecutive patients with motion were rated by 2 blinded investigators on a 4-point visual ordinal scale (0: minimal motion; 1: mild motion; 2: moderate motion; 3: severe motion/uninterpretable). Motion was also quantified using Dwell Fraction, which is the fraction of time the motion vector shows the heart to be within 6 mm of the corrected position and was derived from listmode data of NMC images.
Minimal motion was seen in 15% of patients, while 40%, 30%, and 15% of patients had mild moderate and severe motion, respectively. All corrected images showed an improvement in quality and were interpretable after processing. This was confirmed by a significant correlation (Spearman\'s correlation coefficient 0.626, P < .001) between machine measurement of motion quantification and physician interpretation.
The novel DDMC algorithm improved quality of cardiac PET images with motion. Correlation between machine measurement of motion quantification and physician interpretation was significant.