Purpose

This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.

Methods

Clinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels. To simulate realistic low-dose projections, 50%, 25%, and 12.5% of the events were randomly selected from the list-mode data through applying binomial subsampling. A generative adversarial network was implemented to predict non-gated standard-dose SPECT images in the projection space at the different dose reduction levels. Well-established metrics, including peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index metrics (SSIM) in addition to Pearson correlation coefficient analysis and clinical parameters derived from Cedars-Sinai software were used to quantitatively assess the predicted standard-dose images. For clinical evaluation, the quality of the predicted standard-dose images was evaluated by a nuclear medicine specialist using a seven-point (− 3 to + 3) grading scheme.

Results

The highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01) and the lowest RMSE (1.99 ± 0.63) were achieved at a half-dose level. Pearson correlation coefficients were 0.997 ± 0.001, 0.994 ± 0.003, and 0.987 ± 0.004 for the predicted standard-dose images at half-, quarter-, and one-eighth-dose levels, respectively. Using the standard-dose images as reference, the Bland–Altman plots sketched for the Cedars-Sinai selected parameters exhibited remarkably less bias and variance in the predicted standard-dose images compared with the low-dose images at all reduced dose levels. Overall, considering the clinical assessment performed by a nuclear medicine specialist, 100%, 80%, and 11% of the predicted standard-dose images were clinically acceptable at half-, quarter-, and one-eighth-dose levels, respectively.

Conclusion

The noise was effectively suppressed by the proposed network, and the predicted standard-dose images were comparable to reference standard-dose images at half- and quarter-dose levels. However, recovery of the underlying signals/information in low-dose images beyond a quarter of the standard dose would not be feasible (due to very poor signal-to-noise ratio) which will adversely affect the clinical interpretation of the resulting images.

中文翻译：

---

基于深度学习的低剂量 SPECT 心肌灌注图像去噪：定量评估和临床表现

目的

这项工作旨在研究在不牺牲诊断准确性的情况下减少 SPECT 心肌灌注成像 (MPI) 剂量的可行性。提出了一种深度学习方法，以在投影空间中以不同的剂量降低水平从相应的低剂量图像合成全剂量图像。

方法

回顾性地采用列表模式格式在专用心脏 SPECT 相机上采集的 345 名患者的临床 SPECT-MPI 图像从低剂量图像中预测半、四分之一和八分之一剂量水平的标准剂量。为了模拟真实的低剂量预测，通过应用二项式二次抽样从列表模式数据中随机选择 50%、25% 和 12.5% 的事件。实施生成对抗网络以预测不同剂量降低水平下投影空间中的非门控标准剂量 SPECT 图像。完善的指标，包括峰值信噪比 (PSNR)、均方根误差 (RMSE)、除了 Pearson 相关系数分析和来自 Cedars-Sinai 软件的临床参数之外，还使用结构相似性指数度量 (SSIM) 和结构相似性指数度量 (SSIM) 来定量评估预测的标准剂量图像。对于临床评估，预测的标准剂量图像的质量由核医学专家使用七点（- 3 到 + 3）分级方案进行评估。

结果

在半剂量水平下达到最高 PSNR (42.49 ± 2.37) 和 SSIM (0.99 ± 0.01) 和最低 RMSE (1.99 ± 0.63)。对于半、四分之一和八分之一剂量水平的预测标准剂量图像，皮尔逊相关系数分别为 0.997 ± 0.001、0.994 ± 0.003 和 0.987 ± 0.004。使用标准剂量图像作为参考，与所有降低剂量水平的低剂量图像相比，为 Cedars-Sinai 选定参数绘制的 Bland-Altman 图在预测的标准剂量图像中显示出明显更少的偏差和方差。总体而言，考虑到核医学专家进行的临床评估，100%、80% 和 11% 的预测标准剂量图像在一半、四分之一和八分之一剂量水平下分别在临床上是可接受的。

结论

所提出的网络有效地抑制了噪声，并且预测的标准剂量图像与半剂量和四分之一剂量水平的参考标准剂量图像相当。然而，在超过标准剂量四分之一的低剂量图像中恢复基本信号/信息是不可行的（由于非常差的信噪比），这将对结果图像的临床解释产生不利影响。