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Single‐image Tomography: 3D Volumes from 2D Cranial X‐Rays

Henzler, P; Rasche, V; Ropinski, T; Ritschel, T; (2018) Single‐image Tomography: 3D Volumes from 2D Cranial X‐Rays. Computer Graphics Forum , 37 (2) pp. 377-388. 10.1111/cgf.13369. Green open access

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Abstract

As many different 3D volumes could produce the same 2D x‐ray image, inverting this process is challenging. We show that recent deep learning‐based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed‐resolution volume which is then fused in a second step with the input x‐ray into a high‐resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer‐simulated 2D x‐ray images of 3D volumes scanned from 175 mammalian species. Future applications of our approach include stereoscopic rendering of legacy x‐ray images, re‐rendering of x‐rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x‐rays.

Type: Article
Title: Single‐image Tomography: 3D Volumes from 2D Cranial X‐Rays
Location: United Kingdom
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/cgf.13369
Publisher version: https://doi.org/10.1111/cgf.13369
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep learning, Volume rendering, Inverse rendering, Convolutional neural networks, Tomography
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10055179
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