Abstract #1874
Image Quality Transfer improves the potential clinical value of low-field MRI
Matteo Figini1,2, Hongxiang Lin1,2,3, Felice D'Arco4, Godwin Inalegwu Ogbole5, Maria Camilla Rossi Espagnet6, Olalekan Ibukun Oyinloye7, Joseph O Yaria8, Donald Amasike Nzeh7, Mojisola Omolola Atalabi9, Lisa Ronan1,2, David W Carmichael10,11, Judith Helen Cross4,11, Ikeoluwa A Lagunju12, Delmiro Fernandez-Reyes2,12, and Daniel C Alexander1,2
1Centre for Medical Image Computing, University College London, London, United Kingdom, 2Computer Science, University College London, London, United Kingdom, 3Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China, 4Great Ormond Street Hospital for Children, London, United Kingdom, 5Radiology, College of Medicine, University of Ibadan, Ibadan, Nigeria, 6Neuroradiology, Sapienza University, Rome, Italy, 7Radiology, University of Ilorin Teaching Hospital, Ilorin, Nigeria, 8Neurology, University College Hospital Ibadan, Ibadan, Nigeria, 9Radiology, University College Hospital Ibadan, Ibadan, Nigeria, 10School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, 11UCL Great Ormond Street Institute of Child Health, London, United Kingdom, 12Paediatrics, College of Medicine, University of Ibadan, Ibadan, Nigeria
Synopsis
We applied Image Quality Transfer to enhance the contrast and resolution in the slice direction of low-field structural MRI, using a deep learning model trained on simulated images. Six radiologists blindly reviewed the enhanced images compared to low- and high-field images from 12 paediatric patients with epilepsy. Results demonstrated significant improvement of the visualisation of brain structures in sagittal and coronal orientations, and marginal improvement of the contrast between grey and white matter. If these promising results are confirmed in a larger study and in lesions, IQT could be an important tool to enhance the diagnostic power of low-field MRI.
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