Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Sciences, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California; and.
Department of Cardiology, Nihon University, Tokyo, Japan.
J Nucl Med. 2024 Jan 2;65(1):139-146. doi: 10.2967/jnumed.123.266208.
Motion correction (MC) affects myocardial blood flow (MBF) measurements in Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). In total, 565 patients who underwent PET-MPI were considered. Patients without angiographic findings were split into training ( = 112) and validation ( = 112) groups. The automated MC algorithm used simplex iterative optimization of a count-based cost function and was developed using the training group. MBF measurements with automated MC were compared with those with manual MC in the validation group. In a separate cohort, 341 patients who underwent PET-MPI and invasive coronary angiography were enrolled in the angiographic group. The predictive performance in patients with significant CAD (≥70% stenosis) was compared between MBF measurements with and without automated MC. In the validation group ( = 112), MBF measurements with automated and manual MC showed strong correlations ( = 0.98 for stress MBF and = 0.99 for rest MBF). The automatic MC took less time than the manual MC (<12 s vs. 10 min per case). In the angiographic group ( = 341), MBF measurements with automated MC decreased significantly compared with those without (stress MBF, 2.16 vs. 2.26 mL/g/min; rest MBF, 1.12 vs. 1.14 mL/g/min; MFR, 2.02 vs. 2.10; all < 0.05). The area under the curve (AUC) for the detection of significant CAD by stress MBF with automated MC was higher than that without (AUC, 95% CI, 0.76 [0.71-0.80] vs. 0.73 [0.68-0.78]; < 0.05). The addition of stress MBF with automated MC to the model with ischemic total perfusion deficit showed higher diagnostic performance for detection of significant CAD (AUC, 95% CI, 0.82 [0.77-0.86] vs. 0.78 [0.74-0.83]; = 0.022), but the addition of stress MBF without MC to the model with ischemic total perfusion deficit did not reach significance (AUC, 95% CI, 0.81 [0.76-0.85] vs. 0.78 [0.74-0.83]; = 0.067). Automated MC on Rb PET-MPI can be performed rapidly with excellent agreement with experienced operators. Stress MBF with automated MC showed significantly higher diagnostic performance than without MC.
运动校正（MC）会影响放射性核素心肌灌注成像（MPI）中铷-82 的心肌血流（MBF）测量；然而，动态帧的逐帧手动 MC 非常耗时。本研究旨在开发一种用于房室模型的时间活动曲线的自动 MC 算法，并比较有和没有自动 MC 的 MBF 对有意义的冠状动脉疾病（CAD）的预测价值。 共纳入 565 名接受 PET-MPI 的患者。无血管造影发现的患者分为训练组（=112）和验证组（=112）。自动 MC 算法使用基于计数的成本函数的单纯形迭代优化，并使用训练组开发。在验证组中比较了自动 MC 和手动 MC 的 MBF 测量值。在另一个队列中，341 名接受 PET-MPI 和冠状动脉造影的患者被纳入血管造影组。比较了有意义的 CAD（≥70% 狭窄）患者的 MBF 测量值与自动 MC 和无自动 MC 的预测性能。 在验证组（=112）中，自动 MC 和手动 MC 的 MBF 测量值具有很强的相关性（应激 MBF 的 =0.98，静息 MBF 的 =0.99）。自动 MC 比手动 MC 花费的时间更少（<12s 与每例 10 分钟）。在血管造影组（=341）中，与无自动 MC 相比，MBF 测量值明显降低（应激 MBF，2.16 与 2.26 mL/g/min；静息 MBF，1.12 与 1.14 mL/g/min；MFR，2.02 与 2.10；均<0.05）。具有自动 MC 的应激 MBF 检测有意义 CAD 的曲线下面积（AUC）高于无自动 MC（AUC，95%CI，0.76[0.71-0.80]与 0.73[0.68-0.78]；<0.05）。将具有自动 MC 的应激 MBF 添加到缺血性总灌注缺陷模型中，可提高检测有意义 CAD 的诊断性能（AUC，95%CI，0.82[0.77-0.86]与 0.78[0.74-0.83]；=0.022），但将没有自动 MC 的应激 MBF 添加到缺血性总灌注缺陷模型中并没有达到统计学意义（AUC，95%CI，0.81[0.76-0.85]与 0.78[0.74-0.83]；=0.067）。Rb PET-MPI 上的自动 MC 可以快速进行，与有经验的操作人员具有极好的一致性。具有自动 MC 的应激 MBF 显示出比没有 MC 更高的诊断性能。
