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Abstract #0846

Deep generative MRI reconstruction for unsupervised Gibbs ringing correction

Lucilio Cordero-Grande1,2, Enrique Ortuño-Fisac1, Jonathan O'Muircheartaigh2,3, Jo Hajnal2, and María Jesús Ledesma-Carbayo1
1Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain, 2Centre for the Developing Brain & Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience & MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom


MRI reconstruction is formulated as the retrieval of the parameters of a deep decoder network fitted to the observations by an image formation model including the truncation of high-frequency information. Solutions without Gibbs ringing at any prescribed image grid can be obtained naturally by the model without training or ad-hoc post-corrections. We present quantitative and visual results for spectral extrapolation of magnitude images at different scales in an in-silico experiment and a high resolution ex-vivo brain MRI scan. After minor modifications to deal with complex data, the architecture is applied to 2D parallel imaging showing promising visual results.

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