Within this context, our contribution involves comparing these three UNets in upscaling LQ MRI for different acquisition scenarios, for the same training and performance-testing datasets as well as the same training conditions. Specifically, to replicate practical acquisition scenarios, the three UNets were tested on (a) two image acquisition scenarios and (b) for three different acquisition matrices. In one case, herein referred to as With-Priors, we replicated an imaging protocol that collects LQ as well as a same-subject high quality complementary prior (HQCP) image that is of different contrast, finer spatial resolution, and higher SNR. Examples of this acquisition scenario include LQ 23 Na MRI [41] or CSI [9] images collected together with the HQ 1H anatomical scans. In the other acquisition scenario, herein referred to as WithOut-Priors, only the LQ data set is collected during an imaging session: this would be the case, for example, for low magnetic field MRI [10; 42]. The three UNets for the two acquisition scenarios (i.e., six cases of scenarios/Unet pairs) were trained and tested for upscaling LQ images collected with different acquisition matrices (i.e., spatial resolutions) of 1/8, 1/4, and 1/2 of the matrix size of the targeted upscaled reconstructed image. To analyze the reconstructed images of the three Unets we used Mean Squared Error (MSE) and Structural Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Intensity Error (MIE). Yet another contribution of this work is the analysis of the significance of the three Unet architectures, the three acquisition matrices, and the two acquisition scenarios, not just by descriptive statistics (i.e., means and standard deviations), but with mixed effects (aka repeated measures) modeling (MEM) [43]. MEManalysis was selected, as it takes into account that in this study we performed repeated measurements (i.e., multiple evaluation measurements) on a set of distinct images. For the MEM analysis, the fixed effects were determined by the type of UNet (with three levels), and the matrix size (also with three levels) along with their interaction, while the images constitute the random effects. Our main contributions stem from the thorough comparison of the three state-of-the-art UNet architectures that entail:  
* Two different acquisition scenarios: (a) the With-Priors that mimic studies that entail the collection of both an LQ image and its complementary high quality prior to upscale the image to a corresponding HQ target, (b) the WithOut-Priors that mimic studies that entail the collection of only a single LQ Image to upscale the image to a corresponding HQ target. * Creation of synthetic training and testing data so we have the same set of LQ images, its complementary HQ prior, and an HQ image (ground truth). In addition, hyperintense lesions of random intensity, size, and position were added to increase the variability in the images. * The collected LQ Images were truncated to three smaller matrix sizes to mimic studies where the acquisition matrix sizes are small. We train the networks to upscale these images in two acquisition scenarios. * An extensive analysis of the quality of the upscaled images obtained from different UNet was performed using various indices and statistical tests using a mixed effects model.