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Meeting Abstract

Sythetic 9T-like structural MRI using Generative Neural Network

MPS-Authors
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Wang,  Q
Research Group Translational Neuroimaging and Neural Control, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons252833

Steiglechner,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133483

Lohmann,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Wang, Q., Steiglechner, J., & Lohmann, G. (2021). Sythetic 9T-like structural MRI using Generative Neural Network. In NeNa Conference 2021: Neurowissenschaftliche Nachwuchskonferenz (Conference of Junior Neuroscientists) (pp. 14).


Cite as: http://hdl.handle.net/21.11116/0000-0009-5721-0
Abstract
Aiming to tackle data deficiency in 9-Tesla Magnetic Resonance Image(MRI) anatomic images of human brain, which fits an adequate amount for deep neural network training, we applied generative neural networks to produce super-resolution 3D images based on extensive amount of 3T data. Such synthetic data own two main attributes to provide training model with essential features included in 9-Tesla images, ultra-high spatial resolution and the distinguishable contrast, thus a supervised neural network would gain better prediction accuracy benefiting from such realistic data augmentation. Additionally, such augmentation scheme avoids offending privacy from real patients as well as expensive scanning, especially when it comes to such data-driven neural network jobs. Moreover, high quality MR images better resolved contours of tissues and are helpful for follow-up data analysis, e.g. image registration, segmentation, etc., which employed advantage of the prevailing convolutional neural networks.