Magnetic Resonance Spectroscopy Spectral Registration with Unsupervised Deep Learning

Abstract

A deep learning-based registration method has been a successful image processing tool adopted in medical image registration but there is a lack of learning-based registration tools for spectral registration protocols. A novel CNN-based unsupervised deep learning spectral registration model was developed and trained on a simulation dataset. The model was then further evaluated on a simulated test set with more extreme conditions and on an in vivo dataset and was compared performances to published frequency-and-phase correction models. An unsupervised deep learning-based spectral registration approach was found to demonstrate state-of-the-art performance in frequency-and-phase correction.