Abstract
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 transmembrane 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 downsampling, 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.
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Zheng, T., Ba, R., Wang, X., Ye, C., Wu, D. (2023). AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_14
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DOI: https://doi.org/10.1007/978-3-031-43993-3_14
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