  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.
