Abstract #3550

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

Mario Serrano-Sosa¹, Christine DeLorenzo^1,2, and Chuan Huang^1,2,3

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 (dNet_PET/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 dNet_PET/MRI performed the best across all metrics and performed significantly better than uNet_PET/MRI (p_SSIM=0.0218, p_PSNR=0.0034, p_MAPE=0.0305). Also, dNet_PET performed significantly better than uNet_PET (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 (dNet_PET/MRI vs. dNet_PET and uNet_PET/MRI vs. uNet_PET).

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