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

Autor:	Merkofer, Julian P., van de Sande, Dennis M. J., Amirrajab, Sina, Drenthen, Gerhard S., Veta, Mitko, Jansen, Jacobus F. A., Breeuwer, Marcel, van Sloun, Ruud J. G.
Rok vydání:	2023
Předmět:	Physics - Medical Physics Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.02984 Zobrazit plný text záznamu View this record from Arxiv