Neural networks reduce the data requirement for deep learning-based quantitative MRI, nonetheless their uncertainty/confidence has rarely been characterized. We implemented Monte Carlo dropout, a Bayesian approximation of Gaussian process, using U-Net that include dropout layers (active during training and inference) to address this. The uncertainty was calculated as the variance of predictions from 100 different dropout configurations. The estimates were calculated as the average of predictions. The proposed method also achieved higher accuracy in estimating FA and MD from only 3 diffusion-weighted images compared to standard U-Net, which was readily usable for other MRI applications (reconstruction, super-resolution, denoising, segmentation, classification).
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