Development of a Super-Resolution Scheme for Pediatric Magnetic Resonance Brain Imaging Through Convolutional Neural Networks

Abstract

Pediatric medical imaging represents a real challenge for physicians, as children who are patients often move during the examination, and it causes the appearance of different artifacts in the images. Thus, it is not possible to obtain good quality images for this target population limiting the possibility of evaluation and diagnosis in certain pathological conditions. Specifically, magnetic resonance imaging (MRI) is a technique that requires long acquisition times and, therefore, demands the use of sedation or general anesthesia to avoid the movement of the patient, which is really damaging in this specific population. Because ALARA (as low as reasonably achievable) principles should be considered for all imaging studies, one of the most important reasons for establishing novel MRI imaging protocols is to avoid the harmful effects of anesthesia/sedation. In this context, ground-breaking concepts and novel technologies, such as artificial intelligence, can help to find a solution to these challenges while helping in the search for underlying disease mechanisms. The use of new MRI protocols and new image acquisition and/or pre-processing techniques can aid in the development of neuroimaging studies for children evaluation, and their translation to pediatric populations. In this paper, a novel super-resolution method based on a convolutional neural network (CNN) in two and three dimensions to automatically increase the resolution of pediatric brain MRI acquired in a reduced time scheme is proposed. Low resolution images have been generated from an original high resolution dataset and used as the input of the CNN, while several scaling factors have been assessed separately. Apart from a healthy dataset, we also tested our model with pathological pediatric MRI, and it successfully recovers the original image quality in both visual and quantitative ways, even for available examples of dysplasia lesions. We hope then to establish the basis for developing an innovative free-sedation protocol in pediatric anatomical MRI acquisition.

Keywords: convolutional neural networks (CNN); deep learning (DL); magnetic resonance imaging (MRI); pediatric imaging; sedation; super-resolution (SR).