Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT, 06520-8048, USA.
J Nucl Cardiol. 2022 Oct;29(5):2235-2250. doi: 10.1007/s12350-021-02672-0. Epub 2021 Jun 3.
Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC.
CT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance.
Quantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net.
Our customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net.
使用 CT 透射扫描进行衰减校正（AC）可实现专用心脏 SPECT 的准确定量分析。然而，AC 对仅用于 SPECT 的扫描仪来说具有挑战性。我们开发了一种基于深度学习的方法，可从无 AC 的 SPECT 图像生成合成 AC 图像。
使用我们定制的双挤压-激励残差密集网络（DuRDN）实现无 CT 的 AC。在训练、验证和测试中使用了 172 例匿名临床混合 SPECT/CT 应激/休息心肌灌注研究。还将额外的体重指数（BMI）、性别和散射窗口信息作为通道输入，以进一步提高网络性能。
基于体素和 17 节极地图的定量和定性分析表明，我们的方法有潜力生成一致的 SPECT AC 图像。与传统的网络设计（如 U-Net）相比，我们定制的 DuRDN 表现出更好的性能。DuRDN 预测的 AC 图像与真实 AC 图像之间的体素归一化均方误差（NMSE）平均为 2.01±1.01%，而 U-Net 为 2.23±1.20%。
我们定制的 DuRDN 可在不进行 CT 扫描的情况下实现专用心脏 SPECT AC。DuRDN 可以有效地整合额外的患者信息，并可能比传统的 U-Net 实现更好的性能。
