PROJECT SUMMARY
MRI is a widely-used imaging modality which offers unique soft-tissue contrast and provides a wealth of
anatomical and functional information. However, MRI is inherently slow and signal-to-noise ratio (SNR)-limited,
resulting in variable diagnostic image quality and limiting statistical power for research studies. Particularly
clinically relevant SNR-starved applications are diffusion MRI (dMRI) and functional (fMRI) for surgical planning
(e.g., in functional neurosurgery and in brain tumors). dMRI suffers from long scan times, low resolution and
subject motion; BOLD fMRI response signal changes are only about 3% using 3T MRI. State-of-the-art
denoising methods, based on image models or smoothing, result in partial-volume effects and loss of fine
anatomical detail.
We have identified an untapped reserve for significant noise reduction in clinically feasible MRI protocols
resulting in SNR increase and Rician MRI noise floor decrease by factors of up to 5-fold, using a model-free
noise reduction (denoising) and image reconstruction technique, based on random matrix theory. It does not
rely on user-specific input, and outperforms state-of-the-art denoising methods. Our method allows us to
identify and remove a pure thermal noise contribution in the principal component analysis (PCA) representation
of an MRI data matrix. Remarkably, while noise enters randomly in each voxel's signal, its contribution to the
principal components becomes deterministic, when signals from large number of voxels and inequivalent
acquisitions (e.g., q-space, time-domain, coils) are combined, which allows us to identify and remove pure-
noise components. The key to our MP-PCA method is acquisition redundancy, such that the bulk of the PCA
spectrum is dominated by the noise, whose contribution can then be identified and removed. While we initially
exploited redundancy in the dMRI q-space, our preliminary findings show it is also present in multi-coil arrays,
and in the temporal domain of fMRI.
The main goals of this study are: To develop and optimize the MP-PCA denoising framework at the level of
multi-coil image reconstruction and to evaluate its accuracy and precision in dMRI (Aim 1); to evaluate its
clinical utility for increasing dMRI resolution in functional neurosurgery, based on the ground-truth derived from
MR-guided ultrasound intra-operative feedback (Aim 2); and to evaluate its clinical utility for decreasing fMRI
scan time in preoperative planning of brain tumor resections (Aim 3).
Fundamentally, this project will establish an objective framework to quantify the information content of different
MRI modalities, by separating between the signal and the noise. Its applications to dMRI and fMRI, together
with using multi-coil redundancy, will lead to maximal possible SNR, thereby reducing scan time, and
improving resolution, precision, sensitivity and diagnostic utility of clinically relevant MRI protocols.