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Abstract #1102

Deep Learning-enabled Diffusion Tensor MR Fingerprinting

Carolin M Pirkl1,2, Ilona Lipp3,4, Guido Buonincontri5, Miguel Molina-Romero1,2, Anjany Sekuboyina1,6, Diana Waldmannstetter1, Jonathan Dannenberg2,7, Valentina Tomassini3,4, Michela Tosetti5, Derek K Jones3, Marion I Menzel2, Bjoern H Menze1, and Pedro A Gómez1,2

1Computer Science, Technical University of Munich, Garching, Germany, 2GE Healthcare, Munich, Germany, 3Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University School of Psychology, Cardiff, United Kingdom, 4Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom, 5Imago7 Foundation, Pisa, Italy, 6Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany, 7Department of Physics, Technical University of Munich, Garching, Germany

MR Fingerprinting enables the quantification of multiple tissue properties from a single, time-efficient scan. Here we present a novel Diffusion Tensor MR Fingerprinting acquisition scheme that is simultaneously sensitive to T1, T2 and the full diffusion tensor. We circumvent the long-standing issue of phase errors in diffusion encoding and expensive dictionary matching by using a neural network architecture capable of learning the non-linear relation between fingerprints and multiparametric maps, robustly mitigating motion, undersampling and phase artifacts. As such, our framework enables the simultaneous quantification of relaxation parameters together with the diffusion tensor from a single, highly accelerated acquisition.

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