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Abstract #2700

Performance Analysis of Undersampling Strategies for Multi-Shell Diffusion MRI

Elif Aygun1,2 and Emine Ulku Saritas1,2,3
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Neuroscience Graduate Program, Bilkent University, Ankara, Turkey


Multi-shell dMRI metrics quantify information on micro-properties of neural tissue and can be used as markers for neurological diseases. The protocols used to acquire dMRI data often have prolonged acquisition times. In this work, we propose different undersampling strategies that reduce the acquisition time to half, and evaluate how the performances of multi-shell dMRI metrics change under these strategies. The results show that, while the best performing strategy changes for each metric, 3-shell gradient schemes with small variance of b-vector density on consecutive shells demonstrate improved performance. Additionally, more complex dMRI metrics exhibit relatively increased sensitivity to undersampling.

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