Background and purpose MRI is critical for the detection of subtle cortical pathology in epilepsy surgery assessment. This can be aided by improved MRI quality and resolution using ultra-high field (7T). But poor access and long scan durations limit widespread use, particularly in a paediatric setting. AI-based learning approaches may provide similar information by enhancing data obtained with conventional MRI (3T). We used a convolutional neural network trained on matched 3T and 7T images to enhance quantitative R1-maps (longitudinal relaxation rate) obtained at 3T in paediatric epilepsy patients and to determine their potential clinical value for lesion identification.

Materials and Methods A 3D U-Net was trained using paired patches from 3T and 7T R1-maps from n=10 healthy volunteers. The trained network was applied to enhance paediatric focal epilepsy 3T R1 images from a different scanner/site (n=17 MRI lesion positive / n=14 MR-negative). Radiological review assessed image quality, as well as lesion identification and visualization of enhanced maps in comparison to the 3T R1-maps without clinical information. Lesion appearance was then compared to 3D-FLAIR.

Results AI enhanced R1 maps were superior in terms of image quality in comparison to the original 3T R1 maps, while preserving and enhancing the visibility of lesions. After exclusion of 5/31 patients (due to movement artefact or incomplete data), lesions were detected in AI Enhanced R1 maps for 14/15 (93%) MR-positive and 4/11 (36%) MR-negative patients.

Conclusion AI enhanced R1 maps improved the visibility of lesions in MR positive patients, as well as providing higher sensitivity in the MR-negative group compared to either the original 3T R1-maps or 3D-FLAIR. This provides promising initial evidence that 3T quantitative maps can outperform conventional 3T imaging via enhancement by an AI model trained on 7T MRI data, without the need for pathology-specific information.

The Max Planck Institute for Human Cognitive and Brain Sciences , KCL and UCL have an institutional research agreement with Siemens Healthcare. Siemens Healthcare provide research support to KCL including onsite scientists. Prof Nikolaus Weiskopf holds a patent on acquisition of MRI data during spoiler gradients (US 10,401,453 B2). NW was a speaker at an event organized by Siemens Healthcare and was reimbursed for the travel expenses.

This work was supported by the Henry Smith Charity and Action Medical Research (GN2214) (D.W.C. and S.L.) and supported by the National Institute of Health Research (NIHR) Great Ormond Street Hospital Biomedical Research Centre. A BBSRC NPIF studentship (G.D.), and the Wellcome Trust core funding from the Wellcome EPSRC Centre for Medical Engineering at Kings College London [WT203148/Z/16/Z]. J.O.M was funded by a Sir Henry Dale Fellowship jointly by the Wellcome Trust and the Royal Society (206675/Z/17/Z). R.J.P was funded by a Surgeon-Scientist grant by GOSCHCC (VS0221). N.W. received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project no. 347592254 (WE 5046/4-2) and the Federal Ministry of Education and Research (BMBF) under support code 01ED2210.

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IRAS 148540, CPMS ID 17548 Using MRI tissue parameter maps to detect and delineate FCD. For 7T data - Ethics Committee at the Medical Faculty of the University of Leipzig (Reg.-No. 273-14-25082014).

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