Super-resolution convolutional neural networks applied to functional lung MRI 
at 1.5T

Pusterla, O; Santini, F; Heule, R; Nguyen, D; Sandkühler, R; Andermatt, S; Cattin, PC; Willers, C; Nyilas, S; Latzin, P; Bieri, O; Bauman, G

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Meeting Abstract

Super-resolution convolutional neural networks applied to functional lung MRI at 1.5T

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Heule, R
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Pusterla, O., Santini, F., Heule, R., Nguyen, D., Sandkühler, R., Andermatt, S., et al. (2019). Super-resolution convolutional neural networks applied to functional lung MRI at 1.5T. In 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019).

Cite as: https://hdl.handle.net/21.11116/0000-000B-0425-7

Abstract

High-resolution images are needed in many MR applications to enhance the diagnostic information at early stages of the disease. Often, the achievable resolution is limited by acquisition time constraints, in particular in moving organs such as the lung, where rapid imaging is a necessity. The low proton density in the lung parenchyma further constrains the resolution as sufficiently high signal-to-noise ratio (SNR) requires large voxel size. In this work, the concept of super-resolution is investigated to increase the spatial resolution and potentially shorten the acquisition time for functional assessment in the lung without SNR penalty.