Order-Aware Uncertainty Minimization Network for Fast High Angular Resolution Diffusion Imaging with Unpaired Data

Abstract

Diffusion magnetic resonance imaging (dMRI) is an indispensable technique in today’s neurological research, but its signal acquisition time is extremely long due to the need to acquire signals in multiple diffusion gradient directions. Supervised deep learning methods often require large amounts of complete data to support training, whereas dMRI data are difficult to obtain. We propose a deep learning model for the fast reconstruction of high angular resolution diffusion imaging in data-unpaired scenarios. Firstly, two convolutional neural networks were designed for the recovery of k-space and q-space signals, while training with unpaired data was achieved by reducing the uncertainty of the prediction results of different reconstruction orders. Then, we enabled the model to handle noisy data by using graph framelet transform. To evaluate the performance of our model, we conducted detailed comparative experiments using the public dataset from human connectome projects and compared it with various state-of-the-art methods. To demonstrate the effectiveness of each module of our model, we also conducted reasonable ablation experiments. The final results showed that our model has high efficiency and superior reconstruction performance.