Abstract
High angular resolution diffusion imaging (HARDI) allows for more detailed fiber structures to be obtained by scanning in more directions than conventional diffusion MRI. However, the scanning time of HARDI increases linearly with the number of directions, which limits its use in clinical practice. And directly reducing the directions of HARDI to shorten the scan time would lead to a non-accurate reconstruction of microstructural tissues, such as complex white matter fibers. In this work, we propose a deep-based super-angular resolution scheme to reduce scanning orientations while maintaining reconstruction accuracy, in which an end-to-end neural network is proposed to map low-angular resolution diffusion imaging (LARDI) data to HARDI data. Specifically, our deep network is designed for a tailored farthest point sampling in q-space, which can maximize the separation angles between nearest gradients. For implementation, we transform the sampled 4-D diffusion data into six 3-D data and feed them respectively into six 3-D sub-networks to extract features in the spatial and angular domains. Experimental results show that our scheme can yield an accurate prediction of HARDI from LARDI on the human connectome program (HCP) dataset.
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Acknowledgments
This research was sponsored in part by the National Natural Science Foundation of China (Grant Nos. 62002327, 61976190), and Natural Science Foundation of Zhejiang Province (Grant No. LQ21F020017).
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Chen, Z., Peng, C., Zhang, H., Zeng, Q., Feng, Y. (2021). Deep-Based Super-Angular Resolution for Diffusion Imaging. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_43
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