Anne Adlung1, Nadia Karina Paschke1, Alena-Kathrin Golla1,2, Dominik Bauer1,2, Sherif Mohamed3, Melina Samartzi4, Marc Fatar4, Eva Neumaier Probst3, Frank Gerrit Zöllner1,2, and Lothar Rudi Schad1
1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 2Mannheim Institute for Intelligent System in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 3Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 4Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 2Mannheim Institute for Intelligent System in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 3Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 4Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
This
study investigates factors of k-space undersampling for which CNN
postprocessing is able to improve 23Na MRI data and results indicate
that it could enable the significant reduction of 23Na MRI data
acquisition time.
Figure 2: One representative image slice of one test case
Top: Quantified FI, RI (S = 5) and the CNN output image with L2 and LGDL. Bottom: Corresponding TSC error maps within the whole brain.
Figure 1:
Representative image slice of one patient in its different versions. Full image
(FI) and reduced images (RI) with varying factors S (2, 4, 5 10), and the
corresponding CNN out images for L2 and LGDL.