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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
ISSN:2331-8422