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Abstract #0300

Through-plane diffusion MRI super-resolution with autoencoders: validation on outlier replacement scheme for pre-term baby brains

Hamza Kebiri1,2, Erick J. Canales-Rodríguez3, Priscille de Dumast1,2, Athena Taymourtash4, Hélène Lajous1,2, Yasser Alemán-Gómez2, Georg Langs4, and Meritxell Bach Cuadra1,2,3
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria


Although super-resolution diffusion MRI for isotropic volumes has been explored, to date no unsupervised SR techniques have been investigated for anisotropic dMRI. We propose an autoencoder based framework to enhance the through-plane spatial resolution and to replace slice outliers by leveraging existing high-quality datasets. Quantitative evaluation on 31 pre-term subjects show that the proposed framework significantly outperforms conventionally used interpolation methods at the raw data and estimated diffusion tensor maps. This can hence contribute to the depiction of more accurate white matter properties of the developing brain.

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