Recurrent Variational Inference for fast and robust reconstruction of accelerated FLAIR MRI in Multiple Sclerosis
D. Karkalousos1, L. C. Liebrand1, S. Noteboom2, H. E. Hulst2,3, F. M. Vos4, and M. W. A. Caan1
1Department of Biomedical Engineering & Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, 2Department of Anatomy & Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 3Department of Medical, Health and Neuropsychology, Leiden University, Leiden, Netherlands, 4Delft University of Technology, Delft, Netherlands
Robustness when applying Deep Learning methods to clinical data is crucial for accurate high-resolution reconstructions while having fast inference times. We propose the Cascades of Independently Recurrent Variational Inference Machine (CIRVIM), targeting deep unrolled optimization and enforcing data consistency for further robustness. We quantify contrast resolution of seven and half times prospectively undersampled FLAIR MRI without fully-sampled center containing Multiple Sclerosis lesions. The proposed scheme reduces inference times by a factor of 6 compared to Compressed Sensing. Lesion contrast resolution improves by approximately 13% while preserving spatial detail with enhanced sharpness compared to more blurred results of other presented methods.
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