AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation

Diffusion MRI (dMRI) is a well-established tool for probing tissue microstructure properties. However, advanced dMRI models commonly have multiple compartments that are highly nonlinear and complex, and also require dense sampling in q-space. These problems have been investigated using deep learning based techniques. In existing approaches, the labels were calculated from the fully sampled q-space as the ground truth. However, for some of the dMRI models, dense sampling is hard to achieve due to the long scan time, and the low signal-to-noise ratio could lead to noisy labels that make it hard for the network to learn the relationship between the signals and labels. A good example is the time-dependent dMRI (TD-dMRI), which captures the microstructural size and trans-membrane exchange by measuring the signal at varying diffusion times but requires dense sampling in both q-space and t-space. To overcome the noisy label problem and accelerate the acquisition, in this work, we proposed an adaptive uncertainty guided attention for diffusion MRI models estimation (AUA-dE) to estimate the microstructural parameters in the TD-dMRI model. We evaluated our proposed method with three different downsampling strategies, including q-space downsampling, t-space down-sampling, and q-t space downsampling, on two different datasets: a simulation dataset and an experimental dataset from normal and injured rat brains. Our proposed method achieved the best performance compared to the previous q-space learning methods and the conventional optimization methods in terms of accuracy and robustness.