Abstract #2354

DeepFLAIR: a neural network approach to mitigate signal loss in temporal lobe regions of 7 Tesla FLAIR images

Daniel Uher¹, Jacobus F.A. Jansen^1,2, Gerhard S. Drenthen^1,2, Benedikt A. Poser³, Christopher J. Wiggins⁴, Paul A.M. Hofman^2,5, Louis G. Wagner⁵, Rick H.G.J. van Lanen⁶, Christianne M. Hoeberigs^2,5, Albert J. Colon⁵, Olaf E.M.G. Schijns^1,5,6, and Walter H. Backes^1,2

¹School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands, ²Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, Maastricht, Netherlands, ³Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands, ⁴Scannexus, Maastricht, Netherlands, ⁵Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Centre, Heeze/Maastricht, Netherlands, ⁶Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, Netherlands

Synopsis

In this study we aimed to improve the 7T FLAIR image quality, especially within the temporal lobe regions which are often attenuated due to field inhomogeneities. A neural network using MP2RAGE and T₂-weighted images as inputs was set up to generate a new FLAIR-like image. The training was performed on the extratemporal-lobe voxels of the acquired 7T FLAIR image. The deepFLAIR showed a significant improvement in the signal-to-noise ratio and contrast-to-noise ratio in the temporal lobe regions in a number of cases. This study showed the potential to generate FLAIR-like images with reduced inhomogeneity artifacts and improved image quality.

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