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Abstract #2180

Deep Learning Reconstruction of MR Fingerprinting for simultaneous T1, T2* mapping and generation of WM, GM and WM lesion probability maps

Ingo Hermann1,2,3, Alena-Kathrin Golla1,3, Eloy Martinez-Heras4, Ralf Schmidt1, Elisabeth Solana4, Sara Llufriu4, Achim Gass5, Lothar Schad1, Sebastian Weingärtner2, and Frank Zöllner1,3
1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany, 2Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, Netherlands, 3Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany, 4Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Universitat de Barcelona, Barcelona, Spain, 5Department of Neurology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany

A single deep learning regression network was trained for unified reconstruction, distortion correction and denoising of T1, T2*, WM, GM and lesion probability maps from MRF-EPI acquisition. The network was trained with binary lesion masks and the WM, GM probability maps generated with SPM. The training T1 and T2* maps were reconstructed using dictionary matching. The relative deviation was 7.6% for the 5 output mask in the whole brain between the proposed deep learning network and the conventional processing. Dice coefficients were 0.85 for WM and GM and 0.67 for the lesions with a lesion detection rate of 0.83.

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