Achieving sub-mm clinical diffusion MRI resolution by removing noise during reconstruction using random matrix theory
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 0.8 mm isotropic voxels for
b= 1000 and 2000 protocol (3T), showing an increase of SNR and decrease of the Rician …
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 0.8 mm isotropic voxels for
b= 1000 and 2000 protocol (3T), showing an increase of SNR and decrease of the Rician …
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