Abstract #2180

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

Ingo Hermann^1,2,3, Alena-Kathrin Golla^1,3, Eloy Martinez-Heras⁴, Ralf Schmidt¹, Elisabeth Solana⁴, Sara Llufriu⁴, Achim Gass⁵, Lothar Schad¹, Sebastian Weingärtner², and Frank Zöllner^1,3

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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