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Abstract #3505

Rapid Estimation of Multiple Diffusion Maps fromĀ  Undersampled Q-Space Data: A Comparison of Three Deep Learning Approaches

SeyyedKazem HashemizadehKolowri1,2, Rong-Rong Chen2, Ganesh Adluru1,3, and Edward V. R. DiBella1,2,3
1Radiology and Imaging Science, University of Utah, SALT LAKE CITY, UT, United States, 2Electrical and Computer Engineering, University of Utah, SALT LAKE CITY, UT, United States, 3Biomedical Engineering, University of Utah, SALT LAKE CITY, UT, United States

Advanced diffusion models enable characterization of tissue microstructure with higher specificity than conventional DTI. While beyond DTI diffusion imaging have been found valuable in many studies, their clinical availability have been hampered mainly due to their long scan times. Furthermore, each diffusion model can only extract a few relevant microstructural features. Therefore, using multiple models helps to better understand the brain microstructure, which requires multiple expensive model-fitting. In this study, we use different deep learning approaches to jointly estimate multiple advanced diffusion maps from highly undersampled q-space data, which can reduce both the scan and processing times significantly.

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