## 4 Discussion
What is well known from experimental and clinical imaging also appears to be reflected in the outcome of this study: if contrast and resolution are insufficient, a structure of interest will not be captured and \"resolved/revealed\" in the upscaling. This is the case when considering the upscaling of 1/8 downsized images in the acquisition scenario without complementary information: lesions with low contrast relative to their surrounding tissue are not recoverable. While this is an expected finding as a result of the basic imaging physics, this can also be considered to be a manifestation of the natural limit of UNets. However, as also seen in Figure 10c, the same lesion that could not be recovered in the single image acquisition scenario could be clearly recognized when the complementary image was included. Obviously, this occurs because the complementary image carries the needed structural information: when combined with perfect spatial matching, the UNet training ensures that information is used in the upscaling of the LQ images. However, there are acquisition scenarios in which a HQCP image cannot be collected, as in the cases of different main magnetic field systems, or motion regimes that do not allow matching of an LQ and an HQCP image set. These findings warrant additional systematic studies of these effects on a wider range of data to better establish the practical role of deep learning-based upscaling with clinical utility; we plan to explore these in future research. We may then envision that the design and use of future imaging protocols to entail processes that integrate the selection of acquisition parameters (e.g., acquisition matrix and number of repetitions) based on how these affect the performance of a deep learning technique in upscaling the images (e.g., improve resolution, SNR or CNR). The statistical analysis using the mixed-effect model showed that the interaction of UNet architecture and the size of the acquisition matrix were highly statistically significant as seen in Table 4, and Figure 9 and illustrated that bottleneck layers (AUNet) performed suboptimally, and in cases of small lesions, they were not recovered. Upscaling in the WithOut-Prior scenario is inferior to that in the With-Prior scenario, a behavior that was exemplified in the more undersampled LQ. Lesions with sizes smaller than eight pixels and with low contrast were not recovered in the WithOut-Prior scenario, specifically in the case of 1/8 undersampling. This study has certain limitations. First, a rather small sample of original images was used (436 images from the OASIS dataset with each image corresponding to a different subject), and we added synthetic hyperintense and homogeneous lesions. We augmented the training and testing dataset by additional image synthesis to increase variability, based on accepted practices [21, 33, 35]. Second, the current work is focused on comparing on three stat-of-the-art UNet models. While UNets appear robust and effective for the investigated datasets, in the light of rapid evolution of DL methodology, more networks should be investigated. Future works will systematically assess the effect of the size and variability of the datasets in the accuracy of training. This may be done by combining multiple available data sets, incorporating additional alterations of morphology and anatomy secondary to pathologies, including same-subject multislice or 3D data sets, the presence of image artifacts, and even multi-contrast complementary priors. In future studies, we also plan to compare and investigate the effect of using bicubic [15] or nearest neighbor interpolations for preprocessing LQ images to the ground truth before being passed to the network and will also compare using an upscaling block as part of the network [58] which takes the LQ image as inputs in its low matrix sizes, without being preprocessed with any interpolation method. The main focus of the current study was on comparing three state-of-the-art UNet architectures for upscaling LQ MRI.