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

Julian P. Merkofer; Dennis M. J. van de Sande; Sina Amirrajab; Gerhard S. Drenthen; Mitko Veta; Jacobus F. A. Jansen; Marcel Breeuwer; Ruud J. G. van Sloun

arXiv · 2306.02984

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

Julian P. Merkofer·Dennis M. J. van de Sande·Sina Amirrajab·Gerhard S. Drenthen·Mitko Veta·Jacobus F. A. Jansen·Marcel Breeuwer·Ruud J. G. van Sloun

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Abstract

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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Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Gerhard S. Drenthen, Mitko Veta, Jacobus F. A. Jansen, Marcel Breeuwer, Ruud J. G. van Sloun. 2023-06-05. A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge. https://arxiv.org/abs/2306.02984

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