## 2 Materials and Methods  
### Training and Testing Dataset  
In silico LQ-to-HQ UNet upscaling studies require the networks to be trained and tested with spatially/anatomically matched pairs of LQ and HQ images (i.e., of the same structures). As such matched datasets are not publicly available and are difficult to collect, we synthesized them from publicly accessible images, a commonly used practice in DL [44; 45; 46; 47; 48], to increase the size and variability in training and testing data. As illustrated in Figure 1a,b, evaluation of the UNets for the two scanning scenarios require matching sets from the same object consisting of: (i) an LQ (input) and an HQ (ground truth) for the WithOut-Prior, and (ii) an LQ and an HQCP image (the two inputs) and an HQ (ground truth) for the With-Prior. The three UNets were trained and tested for the two acquisition scenarios using synthetic imaging data as has been the practice in numerous prior works. The synthetic data were composed of 2616 image sets; of these, 2076 were used for training and 540 for testing; the split in training/testing was performed at random. This data set was synthesized from 436 actual MRI images obtained from the OASIS project [49], which were augmented to a total number of 2616 using a customized image generator based on the one reported by [PERSON] et al. [21] and modified to include structural, noise, and signal intensity variations. As illustrated in Figure 1c, we started with 436 data sets from the OASIS project [49] with each set containing a 176 \(\times\) 208 T1-weighted brain image (used as HQCP) together with the corresponding segmented binary maps of cerebrospinal fluid (CSF), white (WM), and gray matter (GM). Our data augmentation processes first included subjecting each data set, i.e., the T1-weighted image (HQCP Image) and the GW, WM, and CSF maps, to six random rotations, resulting in 2616 sets of 176 \(\times\) 208 rotMRI, rotGM, rotWM, and rotCSF, respectively. Then, hyperintense lesions of circular shape with random size and position followed by an elastic deformation [33; 34] were added to the WM and GM regions. Various techniques have been introduced that can enhance the lesions in the brain as described by [PERSON] et al. [50] and [PERSON] et al. [51]. This image enhancement can also be performed at the acquisition level by using gadolinium-based contrast agents [51]. Since elastic deformation was used and lesions were restricted to the WM and GM the lesions had random shapes and sizes. Each rotMRI was further modified by adding 5% Gaussian noise and this image was designated as the augmMRI serving as the HQCP to the training of the With-Prior acquisition scenario. rotWM, rotGM, and rotCSF were combined using random weighting factor to produce synthetic MRI (synMRI) images with a digital resolution of 176 \(\times\) 208. The 176 \(\times\) 208 synMRI images were designated as the denoised high-quality synthetic (synHQ) images that served as the Ground Truth, as well as being used to generate all low-quality synthetic (synLQ) images by first combining  
Figure 1: **(a,b)** Illustrations of the two paradigms With-Prior (**a**) and WithOut-Prior (**b**) (i.e., same-subject complementary high resolution images collected at the same location), showing the type of data collected on the scanner and generated in silico. (**c**) Flowchart of the processes employed in the generation of 2616 data sets that included high quality synthetic (synHQ), low quality synthetic (synLQ), and augmented physically collected T1-weighted (augmMRI) images. (**d**) Flowchart of the UNet training to upscale the synLQ to synHQ, for the two paradigms of With- and WithOut-Prior; in the first case, the augmMRI served as the high quality complementary prior.