Abstract #4189

Enhanced-Deep-Super-Resolution Neural Network on Multiple MR Brain Images

Cristiana Fiscone¹, Nico Curti², Matti Ceccarelli³, David Neil Manners¹, Gastone Castellani², Caterina Tonon^1,4, Daniel Remondini^5,6, Raffaele Lodi^1,4, and Claudia Testa^4,5

¹Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy, ²Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy, ³Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy, ⁴Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy, ⁵Department of Physics and Astronomy, University of Bologna, Bologna, Italy, ⁶INFN Bologna, Bologna, Italy

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Visualization, Super Resolution, GeneralizationEnhanced Deep Super Resolution (EDSR) is a machine learning model aimed to improve image spatial resolution. It was previously trained with general purpose figures and, in this work, directly tested on different MR images: T₁w, T₂w and Quantitative Susceptibility Mapping (QSM), a quantitative imaging technique. The studied cohort included 28 healthy subjects. Without needing fine-tuning, EDSR shows excellent ability of generalization over new kind of data, improving imaging visualization and outperforming the traditional bicubic upsampling. In future applications, images of patients will be considered to test EDSR reconstruction when there is pathological tissue.

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