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Abstract #0475

Reconstructing Lesions not seen during training using a Recurrent Inference Machine

Bob van Hoek1, Kai L√łnning2,3, Hanneke Hulst4, Frans Vos1,5, and Matthan Caan6

1Delft University of Technology, Delft, Netherlands, 2Radiotherapy, Netherlands Cancer Institute, Amsterdam, Netherlands, 3Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, 4Anatomy and Neurosciences, VU University Medical Center, Amsterdam, Netherlands, 5Radiology, Academic Medical Center, Amsterdam, Netherlands, 6Biomedical Engineering and Physics, Academic Medical Center, Amsterdam, Netherlands

Deep learning can accelerate MRI beyond what is currently possible. Broad clinical application requires generalizability to multiple contrasts, acceleration levels and pathologies. Here we explore how a Recurrent Inference Machine trained on healthy volunteer T1-weighted brain images performs in such a situation, by reconstructing FLAIR images with white matter lesions, in simulation and prospectively undersampled patient data. Lesion contrast is maintained up to 6x acceleration and higher than in compressed sensing (CS) reconstruction, and all lesions are retained compared to CS.

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