Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Frontiers in Neurology

Hélène Lajous¹  Priscille de Dumast¹  Hamza Kebiri¹  Yasser Alemán-Gómez³  Mériam Koob³  András Jakab⁴  Gabriel Girard⁵  Erick J. Canales-Rodríguez⁵  Meritxell Bach Cuadra⁵ 

[1] CIBM Center for Biomedical Imaging, Lausanne, Switzerland;Center for MR Research University Children's Hospital Zurich, Zurich, Switzerland;Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland;Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland;Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;

关键词: unsupervised learning;    autoencoders;    super-resolution;    diffusion-weighted imaging;    magnetic resonance imaging (MRI);    pre-term neonates;   

DOI  :  10.3389/fneur.2022.827816

来源: DOAJ

【 摘 要 】

Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network for single-image through-plane super-resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize fetal data with different levels of motions and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.

【 授权许可】

Unknown