Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau SAR, China.
J Nucl Cardiol. 2023 Jun;30(3):1022-1037. doi: 10.1007/s12350-022-03092-4. Epub 2022 Sep 12.
Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and compare the DL-based direct and indirect AC methods, with and without SPECT and CT mismatch.
One hundred patients with different Tc-sestamibi activity distributions and anatomical variations were simulated by a population of XCAT phantoms. Additionally, 34 patients Tc-sestamibi stress/rest SPECT/CT scans were retrospectively recruited. Projections were reconstructed by OS-EM method with or without AC. Mismatch between SPECT and CT images was modeled. A 3D conditional generative adversarial network (cGAN) was optimized for two DL-based AC methods: (i) indirect approach, i.e., non-attenuation corrected (NAC) SPECT paired with the corresponding attenuation map for training. The projections were reconstructed with the DL-generated attenuation map for AC; (ii) direct approach, i.e., NAC SPECT paired with the corresponding AC SPECT for training to perform direct AC.
Mismatch between SPECT and CT degraded DL-based AC performance. The indirect approach is superior to direct approach for various physical and clinical indices, even with mismatch modeled.
DL-based estimation of attenuation map for AC is superior and more robust to direct generation of AC SPECT.
基于深度学习（DL）的衰减校正（AC）有望改善心肌灌注（MP）SPECT。我们旨在优化和比较基于 DL 的直接和间接 AC 方法，包括有无 SPECT 和 CT 不匹配的情况。
通过 XCAT 体模的人群模拟了 100 名具有不同 Tc- sestamibi 活性分布和解剖变异的患者。此外，回顾性招募了 34 名 Tc- sestamibi 应激/静息 SPECT/CT 扫描患者。通过 OS-EM 方法重建投影，无论是否有 AC。对 SPECT 和 CT 图像之间的不匹配进行建模。优化了一个 3D 条件生成对抗网络（cGAN）用于两种基于 DL 的 AC 方法：（i）间接方法，即未校正衰减（NAC）SPECT 与相应的衰减图配对进行训练。使用 DL 生成的衰减图进行 AC 重建投影；（ii）直接方法，即 NAC SPECT 与相应的 AC SPECT 配对进行训练以进行直接 AC。
SPECT 和 CT 之间的不匹配降低了基于 DL 的 AC 性能。即使考虑了不匹配的情况，间接方法在各种物理和临床指标上均优于直接方法。
用于 AC 的基于 DL 的衰减图估计比直接生成 AC SPECT 更优越且更稳健。
