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Prostate (Research Only)
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Denoising
Brain
Prostate
(Research Only)
Publications
Company
Contact
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Publications
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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