Optimization and applications of deep learning algorithms for super-resolution in MRI

The increasing amount of data produced by modern infrastructures requires instruments of analysis more and more precise, quick, and efficient. For these reasons in the last decades, Machine Learning (ML) and Deep Learning (DL) techniques saw exponential growth in publications and research from the scientific community. In this work are proposed two new frameworks for Deep Learning: Byron written in C++, for fast analysis in a parallelized CPU environment, and NumPyNet written in Python, which provides a clear and understandable interface on deep learning tailored around readability. Byron will be tested on the field of Single Image Super-Resolution for NMR imaging of brains (Nuclear Magnetic Resonance) using pre-trained models for x2 and x4 upscaling which exhibit greater performance than most common non-learning-based algorithms. The work will show that the reconstruction ability of DL models surpasses the interpolation of a bicubic algorithm even with images totally different from the dataset in which they were trained, indicating that the generalization abilities of those deep learning models can be sufficient to perform well even on biomedical data, which contain particular shapes and textures. Ulterior studies will focus on how the same algorithms perform with different conditions for the input, showing a large variance between results.