We developed a new PET denoising model by utilizing a dilated CNN (dNet) architecture with PET/MRI inputs (dNetPET/MRI) and compared it to three other deep learning models with objective imaging metrics Structural Similarity index (SSIM), Peak signal-to-noise ratio (PSNR) and mean absolute percent error (MAPE). The dNetPET/MRI performed the best across all metrics and performed significantly better than uNetPET/MRI (pSSIM=0.0218, pPSNR=0.0034, pMAPE=0.0305). Also, dNetPET performed significantly better than uNetPET (p<0.001 for all metrics). Trend-level improvements were found across all objective metrics in networks using PET/MRI compared to PET only inputs within similar networks (dNetPET/MRI vs. dNetPET and uNetPET/MRI vs. uNetPET).
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