Sodium Image Denoising Based on a Convolutional Denoising Autoencoder

Sodium Magnetic Resonance Imaging (sodium MRI) is an imaging modality that has gained momentum over the past decade, because of its potential ability to become a biomarker for several diseases, ranging from cancer to neurodegenerative pathologies, along with monitoring of tissues metabolism. One of the most important limitation to the exploitation of this imaging modality is its characteristic low resolution and signal-to-noise-ratio as compared to the classical proton MRI, which is due to the notably lower concentration of sodium than water in the human body. Therefore, denoising is a central aspect with respect to the clinical use of sodium MRI. In this work, we introduce a Convolutional Denoising Autoencoder that is trained on a training database of thirteen training subjects with three sodium MRI images each. The results illustrate that the denoised images show a strong improvement after application in comparison to the state-of-the-art Non Local Means denoising algorithm. This effect is demonstrated based on different noise metrics and a qualitative evaluation.