Initial publication describing the method:

Denoising of Diffusion MRI Using Random Matrix Theory

Denoising functional MRI for pre-operative planning:

Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising

Review paper on denoising diffusion MRI data using MPPCA methodology:

Denoising Diffusion MRI: Considerations and Implications for Analysis

Denoising neuroimaging diffusion MRI:

Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline

Advanced diffusion metrics in neuroimaging:

Quantifying Brain Microstructure with Diffusion MRI: Theory and Parameter Estimation

Improved Fiber Orientations via MPPCA confirmed on simulation and real data:

Brain Fiber Structure Estimation Based on Principal Component Analysis and RINLM Filter

Gold Standard weights for estimation of the diffusion cumulant expansion:

Weighted Linear Least Squares Estimation of Diffusion MRI Parameters: Strengths, Limitations, and Pitfalls

Bayesian approach to fitting diffusion MRI data:

Disentangling Micro from Mesostructure by Diffusion MRI: A Bayesian Approach

Nature Communications Paper applying MP-PCA onto normalized noise levels for high-resolution functional MRI:

Lowering the Thermal Noise Barrier in Functional Brain Mapping with Magnetic Resonance Imaging

Denoising prostate diffusion MRI on 0.55T:

Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising

Fractional anisotropy is highest in low grade cancers:

Time-Dependent Diffusion in Prostate Cancer

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