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Abstract #0770

Achieving sub-mm clinical diffusion MRI resolution by removing noise during reconstruction using random matrix theory

Gregory Lemberskiy1, Steven Baete1, Jelle Veraart1, Timothy M Shepherd1, Els Fieremans1, and Dmitry S Novikov1

1Radiology, NYU School of Medicine, New York, NY, United States

We show how to achieve the benefits of inline-scan averaging for reducing thermal noise and lowering the Rician noise floor prior to image reconstruction, albeit in inequivalent diffusion MRI (dMRI) acquisitions. For that, we identify and remove the pure-noise principal components in joint coils x q-space x voxels dMRI data, as they follow the universal Marchenko-Pastur distribution. The method is demonstrated on 0.8mm isotropic voxels for b=1000 and 2000 protocol (3T), showing an increase of SNR and decrease of the Rician noise floor by 5-fold. We discuss applications in dMRI parameter estimation, tractography and functional neurosurgery.

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