A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge
This addresses the challenge of faster MRS acquisition for medical imaging applications, but appears incremental as it applies existing deep learning techniques to a specific domain.
The paper tackled the problem of accelerating high-quality edited magnetic resonance spectroscopy (MRS) scans by proposing a machine learning method using covariance matrix analysis, achieving robustness to noise and invariance to transients in synthetic and in-vivo scenarios.
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.