A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge

06/05/2023

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This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covariance matrix as input. The method is invariant to the number of transients and robust to noisy input data for both synthetic as well as in-vivo scenarios.

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A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge

On the variability of the sample covariance matrix under complex elliptical distributions

Sequential Inverse Approximation of a Regularized Sample Covariance Matrix

On characterizations of the covariance matrix

Covariance-Free Sparse Bayesian Learning

Random Matrix Improved Covariance Estimation for a Large Class of Metrics

Generalized Information Ratio

Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithms

A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge

Related Research

On the variability of the sample covariance matrix under complex elliptical distributions

Sequential Inverse Approximation of a Regularized Sample Covariance Matrix

On characterizations of the covariance matrix

Covariance-Free Sparse Bayesian Learning

Random Matrix Improved Covariance Estimation for a Large Class of Metrics

Generalized Information Ratio

Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithms