Video frame interpolation neural network for 3D tomography across different length scales

Laura Gambini (), Cian Gabbett, Luke Doolan, Lewys Jones, Jonathan N. Coleman, Paddy Gilligan and Stefano Sanvito
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Laura Gambini: Trinity College Dublin
Cian Gabbett: Trinity College Dublin
Luke Doolan: Trinity College Dublin
Lewys Jones: Trinity College Dublin
Jonathan N. Coleman: Trinity College Dublin
Paddy Gilligan: Mater Misericordiae University Hospital
Stefano Sanvito: Trinity College Dublin

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content.

Date: 2024
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DOI: 10.1038/s41467-024-52260-2

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