Abstract #5037

Synthesising MRIs from CTs to Improve Stroke Treatment Using Deep Learning

Grace Wen¹, Jake McNaughton¹, Ben Chong¹, Vickie Shim¹, Justin Fernandez¹, Samantha Holdsworth^2,3, and Alan Wang^1,2,3

¹Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, ²Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand, ³Mātai Medical Research Institute, Tairāwhiti-Gisborne, New Zealand

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Stroke, Image Synthesis

MRI holds an important role in diagnosing brain conditions, however, many patients do not receive an MRI before their diagnosis and onset of treatment. We propose to use deep learning to generate an MRI from a patient's CT and have implemented multiple models to compare their results. Using CT/MRI pairs from 181 stroke patients, we use mutiple deep learning models to generate MRI from the CT images. The model produces high quality images and accurately translates lesions onto the target image.

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