Kebiri, HamzaCanales-Rodriguez, Erick J.Lajous, Helenede Dumast, PriscilleGirard, GabrielAleman-Gomez, YasserKoob, MeriamJakab, AndrasBach Cuadra, Meritxell2022-06-062022-06-062022-06-062022-05-0210.3389/fneur.2022.827816https://infoscience.epfl.ch/handle/20.500.14299/188361WOS:000796436600001Fetal 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.Clinical NeurologyNeurosciencesNeurosciences & Neurologyunsupervised learningautoencoderssuper-resolutiondiffusion-weighted imagingmagnetic resonance imaging (mri)pre-term neonatesfetusesbrainfetal-brainvolume reconstructionmriregistrationefficienttractographyconnectometext::journal::journal article::research article