Rapid 2D 23Na MRI of the calf using a denoising convolutional neural network

Abstract

23Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the low 23Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as CS have been proposed to mitigate low SNR; although, these can result in unnatural images, suboptimal denoising and long processing times. Recently, ML has been used to denoise 1H MRI acquisitions; however, this approach typically requires large volumes of high-quality training data, which is not readily available for 23Na MRI. Here, we train a denoising CNN using 1H data, which we subsequently demonstrate on prospective 23Na images of the calf. 1893 1H transverse slices of the knee were used to train denoising CNNs for different levels of noise. Low SNR images were generated by adding gaussian noise to the high-quality 1H kspace data before reconstruction to create paired training data. For prospective testing, 23Na images of the calf were acquired in 10 volunteers with 150 averages, which were used as a reference throughout the study. From this data, lower-average images were reconstructed using a NUFFT as well as CS, with the NUFFT images subsequently denoised using the trained CNN. CNNs were successfully applied to 23Na images reconstructed with 50, 40 and 30 averages. SNR was significantly higher in CNN images compared to NUFFT, CS and reference images. Edge sharpness was equivalent for all images. For image quality ranking, CNN images ranked equally best with reference images and significantly better than NUFFT and CS images. Muscle and skin TSC quantification from CNN images were equivalent to those from CS images, with <0.9 mM bias compared to reference values. Denoising CNNs trained on 1H data can be successfully applied to 23Na images of the calf; thus, allowing scan time to be reduced from 10 minutes to 2 minutes with little impact on image quality or TSC quantification accuracy.