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  Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Pirkl, C. M., Gómez, P. A., Lipp, I., Buonincontri, G., Molina-Romero, M., Sekuboyina, A., et al. (2020). Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting. In Proceedings of Machine Learning Research (pp. 1-17).

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 Creators:
Pirkl, Carolin M.1, 2, Author
Gómez, Pedro A.1, Author
Lipp, Ilona3, 4, 5, Author              
Buonincontri, Guido6, 7, Author
Molina-Romero, Miguel1, Author
Sekuboyina, Anjany1, 8, Author
Waldmannstetter, Diana1, Author
Dannenberg, Jonathan9, Author
Endt, Sebastian1, 2, Author
Merola, Alberto3, 5, Author              
Whittaker, Joseph R.3, 10, Author
Tomassini, Valentina3, 4, 11, Author
Tosetti, Michela6, 7, Author
Jones, Derek K.3, 12, Author
Menze, Bjoern H.13, 14, Author
Affiliations:
1Department of Informatics, Technical University of Munich, Garching, Germany, ou_persistent22              
2GE Healthcare, Munich, Germany, ou_persistent22              
3Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University School of Psychology, Cardiff, United Kingdom, ou_persistent22              
4Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom, ou_persistent22              
5Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
6Fondazione Imago7, Pisa, Italy, ou_persistent22              
7IRCCS Fondazione Stella Maris, Pisa, Italy, ou_persistent22              
8Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany, ou_persistent22              
9Department of Physics, Technical University of Munich, Garching, Germany, ou_persistent22              
10Cardiff University School of Physics and Astronomy, Cardiff, United Kingdom, ou_persistent22              
11Institute for Advanced Biomedical Technologies (ITAB), Department of Neurosciences, Imaging and Clinical Sciences, School of Medicine, University \G. d'Annunzio" of Chieti-Pescara, Chieti, Italy, ou_persistent22              
12Mary McKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia, ou_persistent22              
13Center for Translational Cancer Research, Munich, Germany, ou_persistent22              
14Munich School of BioEngineering, Garching, Germany, ou_persistent22              

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Free keywords: Magnetic Resonance Fingerprinting, Convolutional Neural Network, Image Reconstruction, Diffusion Tensor, Multiple Sclerosis
 Abstract: Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to eciently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions.

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 Dates: 2020-06-26
 Publication Status: Published online
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Title: Proceedings of Machine Learning Research
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 17 Identifier: -