Department of Nuclear Medicine, University Hospital Zurich / University of Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.
University of Zurich, Zurich, Switzerland.
J Nucl Cardiol. 2023 Feb;30(1):313-320. doi: 10.1007/s12350-022-02940-7. Epub 2022 Mar 17.
To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed tomography myocardial perfusion imaging (SPECT-MPI).
Patients were enrolled in this study as part of a larger prospective study (NCT03637231). In this study, 56 Patients who underwent cardiac SPECT-MPI due to suspected coronary artery disease (CAD) were prospectively enrolled. All patients underwent non-contrast CT for AC of SPECT-MPI twice. CACS was manually assessed (serving as standard of reference) on both CT datasets (n = 112) and by a cloud-based DL tool. The agreement in CAC scores and CAC score risk categories was quantified. For the 112 scans included in the analysis, interscore agreement between the CAC scores of the standard of reference and the DL tool was 0.986. The agreement in risk categories was 0.977 with a reclassification rate of 3.6%. Heart rate, image noise, body mass index (BMI), and scan did not significantly impact (p=0.09 - p=0.76) absolute percentage difference in CAC scores.
A DL tool enables a fully automated and accurate estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI.
评估完全自动化的深度学习（DL）基于非对比 CT（NCCT）在衰减校正（AC）心脏单光子发射计算机断层心肌灌注成像（SPECT-MPI）中的冠状动脉钙评分（CACS）的准确性。
本研究纳入了疑似冠心病（CAD）的 56 名患者。所有患者均进行了两次非对比 CT 以进行 SPECT-MPI 的 AC。手动评估（作为标准参考）两个 CT 数据集（n=112）和基于云的 DL 工具上的 CACS（n=112）。对 CAC 评分和 CAC 评分风险类别进行了量化。对于纳入分析的 112 个扫描，标准参考和 DL 工具的 CAC 评分之间的评分一致性为 0.986。风险类别的一致性为 0.977，再分类率为 3.6%。心率、图像噪声、体重指数（BMI）和扫描次数并未显著影响（p=0.09-p=0.76）CAC 评分的绝对百分比差异。
在进行 SPECT-MPI 的 AC 的 NCCT 的患者中，DL 工具可实现 CAC 评分的全自动和准确估计。
