Abstract #0475

Reconstructing Lesions not seen during training using a Recurrent Inference Machine

Bob van Hoek¹, Kai Lønning^2,3, Hanneke Hulst⁴, Frans Vos^1,5, 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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