applied sciences  
A Deep Learning Approach to Upscaling \"Low-Quality\" MR Images: An In Silico Comparison Study Based on the UNet Framework  
[PERSON]\({}^{\copyright}\), [PERSON]\({}^{\copyright}\), [PERSON]. [PERSON]\({}^{\copyright}\), [PERSON], [PERSON]\({}^{\copyright}\), [PERSON] and [PERSON],*  
Medical Robotics and Imaging Lab, Department of Computer Science, University of Houston, Houston, TX 77004, USA Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy Center Center for High Field MRI, Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2333 ZA Leiden, The Netherlands Laboratory of Medical Physics, National and Kapodistrian University of Athens, 11527 Athens, Greece Department of Computer Science, University of Houston, Houston, TX 77004, USA  
Footnote 1(LQ) from now on, various methods have been introduced, such as multiple- and single-image super-resolution reconstruction [12; 13; 14; 15; 16], density-weighted k-space scanning [17], and denoising techniques applied to both the spatial and k-space domains [18; 19; 20]. To enhance the range of options in managing the aforementioned tradeoffs and improving LQ MRI, investigators have pursued deep learning (DL) methods including U-Net convolutional networks [21; 22; 23; 24; 25; 26; 27; 28], MRI-specific DL architectures [29], and generative adversarial networks (GANs) [13; 30; 31; 32]. The inputs to these trained networks are the physically collected LQ MR data and the output is a higher resolution and/or higher SNR image; a high quality (HQ) MRI. Motivated by the potential benefit of using DL to upscale low main magnetic field [21; 22; 23; 24; 25; 26; 27] and X-nuclei MRI, we first evaluated UNets, a robust performer in medical image segmentation [33; 34; 35; 36; 37; 38], as well as having shown its utility in upscaling MR data, such as CSI [21], diffusion weighted imaging [26] and real time imaging [39]. The versatile and widely used state-of-the-art UNet offers a simple and intuitive neural network framework, originally designed for image segmentation, which can be modified to include and link substantially different operations and functions. Examples of UNet architectural modifications include work that uses double convolutions, dense connections, residual addition, and pixel shuffle, to improve the quality of diffusion weighted MRI [26], or substitute the max-pool with an average pooling layer [25]. Other works have further explored the UNet framework, such as [PERSON] et al., who used global residual to enhance the quality of images in [27], while [PERSON] et al. used a residual architecture in UNet to enhance resolution and denoise medical images in [28]. This diversity enables high versatility in capturing context or features [33] in the input LQ images and then combines spatial and contextual information to generate the output HQ image. Secondary to the established performance of UNets, there is an ever-growing number of U-Net architectural modifications that exhibit good performances for the tested datasets and employed metrics [21; 22; 23; 24; 25; 26; 27; 28; 33; 34]. With such a great number of UNet variants, it becomes increasingly challenging to identify the most useful architecture and/or training pipeline. Interestingly, a recent study has also pointed to the importance of loss functions as compared to architectural modifications [40]. These observations led us to the present study, which focuses on aspects of the performance of three UNet architectures on upscaling LQ MRI: a dense (DUNet) [21], a robust (RUNet) [22], and an anisotropic (AUNet) [23]. These were selected since, firstly, they have previously been explored for improving LQ MRI, and secondly, they have quite different architectural layers, which is a starting point in assessing the differential benefit of such architectural modifications.