Deep learning-based noise reduction in low dose SPECT Myocardial Perfusion Imaging: Quantitative assessment and clinical performance

Purpose: This work was set out to investigate the feasibility of SPECT-MPI dose reduction without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize normal-dose images from the corresponding low-dose data at different reduced dose levels in the projection space. Methods: Clinical SPECT-MPI images of 345 patients acquired from a dedicated cardiac SPECT in list-mode format were retrospectively employed to predict normal-dose images from low-dose data at the 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 by applying a binomial subsampling. A generative adversarial network was implemented to predict non-gated normal-dose images in the projection space at the different reduced dose levels. Established metrics including the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), and structural similarity index metrics (SSIM) in addition to Pearson correlation coefficient analysis and derived parameters from Cedars-Sinai software were used to quantitatively assess the quality of the predicted normal-dose images. For clinical evaluation, the quality of the predicted normal-dose images was evaluated by a nuclear medicine specialist using a seven-point (-3 to +3) grading scheme. Results: By considering PSNR, SSIM, and RMSE quantitative parameters among the different reduced dose levels, the highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01), and the lowest RMSE (1.99 ± 0.63) were obtained at the half-dose level in the reconstructed images. Pearson correlation coefficients were measured 0.997 ± 0.001, 0.994 ± 0.003, and 0.987 ± 0.004 for the predicted normal-dose images at the half, quarter, and one-eighth-dose levels, respectively. Regarding the normal-dose images as the reference, the Bland-Altman plots sketched for the Cedars-Sinai selected parameters exhibited remarkably less bias and variance in the predicted normal-dose images compared with the low-dose data at the entire reduced dose levels. Overall, considering the clinical assessment performed by a nuclear medicine specialist, 100%, 80%, and 11% of the predicted normal-dose images were clinically acceptable at the half, quarter, and one-eighth-dose levels, respectively. Conclusion: Considering the quantitative metrics as well as the clinical assessment, the noise was effectively suppressed by the proposed network and the predicted normal-dose images were comparable to the reference normal-dose images at the half and quarter-dose levels. However, recovery of the underlying signals/information in low dose images beyond a quarter of the normal dose would not be feasible (due to very poor signal-to-noise-ratio) which will adversely affect the clinical interpretation of the resulting images.