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. Jansen1,2, Gerhard S. Drenthen1,2, Benedikt A. Poser3, Christopher J. Wiggins4, Paul A.M. Hofman2,5, Louis G. Wagner5, Rick H.G.J. van Lanen6, Christianne M. Hoeberigs2,5, Albert J. Colon5, Olaf E.M.G. Schijns1,5,6, and Walter H. Backes1,2
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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