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Abstract #3451

MRI Below the Noise Floor

Gregory Lemberskiy1,2, Steven Baete1, Jelle Veraart1, Timothy M. Shepherd1, Els Fieremans1, and Dmitry S Novikov1
1New York University School of Medicine, New York, NY, United States, 2Microstructure Imaging INC, New York, NY, United States

We develop random matrix theory (RMT)-based MRI image reconstruction able to increase SNR by up to 10-fold, and to radically increase resolution for routine clinical acquisitions. RMT offers an objective criterion for separating signal from noise across all coils, voxels and MRI contrasts, by utilizing the redundancy in MRI measurements. We demonstrate RMT on a 0.8x0.8x0.8 mm3 neuro exam that includes a series of multiple T2w, T1w, diffusion, and fMRI images on a 3T clinical scanner. RMT can serve as a paradigm for reconstructing multiple contrasts, enhancing image quality for low-field scanners, increasing MR value, and improving biomarker precision.

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