DeepDenoiseNet: a convolutional neural network trained with synthesized images from inversion-recovery maps for SASHA denoising

Motivation: SASHA T1 has high accuracy but low precision due to the low SNR of T1-weighted images. Convolutional neural network has the potential to improve SASHA T1 precision by using spatio-temporal correlations. Goal(s): The aim of this study is to develope a convolutional neural network for improving SASHA T1 precision. Approach: We implemented a convolutional neural network (DeepDenoiseNet) and trained it using synthesized SASHA images from co-registered high-quality T1, T2, and M0 images. Different-level noise was added to simulate low SNR SASHA images. Results: DeepDenoiseNet could reduce the impaction from noise and improve SASHA T1 precision. Impact: The deep convolutional neural network trained with synthesized images and simulated noise could improve SASHA T1 precision.