Uncertainty-Aware High-Fidelity Anatomical MRI Synthesis using Deep Convolutional Network with Monte Carlo Dropout

Multi-modality high-resolution MRI is beneficial for studying the brain structure and function in research and clinical settings. However, its acquisition is time-consuming, which reduces its feasibility for wider adoption especially for certain populations who cannot tolerate long scans. In this study, we propose a convolution neural network to obtain high-resolution T1-weighted MRI from lower-resolution T2-weighted input that can be acquired within a shorter scan time. By leveraging Monte Carlo dropout, our model not only produces high-fidelity anatomical T1-weighted image with higher accuracy compared to baseline model, but also generates uncertainty estimation similar to the actual error map. Our method is validated on the Human Connectome Project, and the experiments indicate our method has the potential to improve the robustness and reliability of deep learning image synthesis and accurately accelerate multi-modality MRI to benefit research and clinical practice.