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

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 synthet...

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Published in:arXiv.org 2023-06
Main Authors: Merkofer, Julian P, Dennis M J van de Sande, Amirrajab, Sina, Drenthen, Gerhard S, Mitko Veta, Jansen, Jacobus F A, Breeuwer, Marcel, Ruud J G van Sloun
Format: Article
Language:English
Subjects:
Covariance matrix
Deep learning
Machine learning
Magnetic resonance spectroscopy
Matrix methods
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container_title arXiv.org
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creator Merkofer, Julian P
Dennis M J van de Sande
Amirrajab, Sina
Drenthen, Gerhard S
Mitko Veta
Jansen, Jacobus F A
Breeuwer, Marcel
Ruud J G van Sloun
description 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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subjects Covariance matrix
Deep learning
Machine learning
Magnetic resonance spectroscopy
Matrix methods
title A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge
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