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Abstract #3550

PET Image Denoising Using Structural MRI with a Dilated Convolutional Neural Network

Mario Serrano-Sosa1, Christine DeLorenzo1,2, and Chuan Huang1,2,3
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, 2Psychiatry, Stony Brook University, Stony Brook, NY, United States, 3Radiology, Stony Brook University, Stony Brook, NY, United States

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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