4/1/2023 0 Comments Level method map image![]() ![]() Its average AUC with half time data was 0.94 and 0.91 with quarter time data. cGAN did not achieve the defect detection performance of the other DL methods. However, with quarter time data CNN, RES and UNET had an average AUC of 0.93, which was lower than full time OSEM’s AUC, but equal to quarter acquisition time OSEM. Half time OSEM, CNN, RES and UNET provided equal or nearly equal AUC. The defect detection performance of full time OSEM measured as area under the ROC curve (AUC) was on average 0.97. SSIM of the reduced acquisition time OSEM was overall higher than with the DL methods. Image quality and polar map uniformity of DL-denoised images were also better than reduced acquisition time OSEM’s. cGAN had the lowest CoV of the DL methods at all noise levels. All DL methods also outperformed full time OSEM without DL-based denoising in terms of noise level with both half and quarter acquisition time, but this difference was not statistically significant. CoV of the myocardium counts with the different DL noising methods was on average 7% (CNN), 8% (RES), 7% (UNET) and 14% (cGAN) lower than with OSEM. ResultsĪll the DL denoising methods tested provided statistically significantly lower noise level than OSEM without DL-based denoising with the same acquisition time. Total perfusion deficit scores were used as observer rating for the presence of a perfusion defect. The methods were evaluated in terms of noise level (coefficient of variation of counts, CoV), structural similarity index measure (SSIM) in the myocardium of normal patients and receiver operating characteristic (ROC) analysis of realistic artificial perfusion defects inserted into normal MPS scans. Comparisons were made using half and quarter time acquisition data. All DL methods were compared against each other and also against images without DL-based denoising. MethodsĬonvolution neural network (CNN), residual neural network (RES), UNET and conditional generative adversarial neural network (cGAN) were generated and trained using ordered subsets expectation maximization (OSEM) reconstructed MPS studies acquired with full, half, three-eighths and quarter acquisition time. The aim of this study was to investigate the differences among several DL denoising models. Deep learning (DL)-based methods have been proposed to overcome the noise artefacts. Poor-quality images can lead to misinterpretations of perfusion defects. Myocardial perfusion SPECT (MPS) images often suffer from artefacts caused by low-count statistics. ![]()
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