Diffusion MRI signal augmentation: From single shell to multi shell with deep learning

Koppers S, Haarburger C, Merhof D (2017)

Publication Type: Conference contribution

Publication year: 2017

Publisher: Springer Heidelberg

Pages Range: 61-70

Conference Proceedings Title: Mathematics and Visualization

Event location: Athens, GRC

ISBN: 9783319541297

DOI: 10.1007/978-3-319-54130-3_5

Abstract

High Angular Resolution Diffusion Imaging makes it possible to capture information about the course and location of complex fiber structures in the human brain. Ideally, multi-shell sampling would be applied, which however increases the acquisition time. Therefore, multi-shell acquisitions are considered infeasible for practical use in a clinical setting. In this work, we present a data-driven approach that is able to augment single-shell signals to multi-shell signals based on Deep Neural Networks and Spherical Harmonics. The proposed concept is evaluated on synthetic data to investigate the impact of noise and number of gradients. Moreover, it is evaluated on human brain data from the Human Connectome Project, comprising 100 scans from different subjects. The proposed approach makes it possible to drastically reduce the signal acquisition time and performs equally well on both synthetic as well as real human brain data.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE)

How to cite

APA:

Koppers, S., Haarburger, C., & Merhof, D. (2017). Diffusion MRI signal augmentation: From single shell to multi shell with deep learning. In Andrea Fuster, Yogesh Rathi, Marco Reisert, Enrico Kaden, Aurobrata Ghosh (Eds.), Mathematics and Visualization (pp. 61-70). Athens, GRC: Springer Heidelberg.

MLA:

Koppers, Simon, Christoph Haarburger, and Dorit Merhof. "Diffusion MRI signal augmentation: From single shell to multi shell with deep learning." Proceedings of the MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016, Athens, GRC Ed. Andrea Fuster, Yogesh Rathi, Marco Reisert, Enrico Kaden, Aurobrata Ghosh, Springer Heidelberg, 2017. 61-70.

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