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Open AccessArticle

On-the-Fly Machine Learning for Improving Image Resolution in Tomography

Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2445;
Received: 10 May 2019 / Revised: 7 June 2019 / Accepted: 11 June 2019 / Published: 14 June 2019
(This article belongs to the Special Issue Machine Learning and Compressed Sensing in Image Reconstruction)
In tomography, the resolution of the reconstructed 3D volume is inherently limited by the pixel resolution of the detector and optical phenomena. Machine learning has demonstrated powerful capabilities for super-resolution in several imaging applications. Such methods typically rely on the availability of high-quality training data for a series of similar objects. In many applications of tomography, existing machine learning methods cannot be used because scanning such a series of similar objects is either impossible or infeasible. In this paper, we propose a novel technique for improving the resolution of tomographic volumes that is based on the assumption that the local structure is similar throughout the object. Therefore, our approach does not require a training set of similar objects. The technique combines a specially designed scanning procedure with a machine learning method for super-resolution imaging. We demonstrate the effectiveness of our approach using both simulated and experimental data. The results show that the proposed method is able to significantly improve resolution of tomographic reconstructions. View Full-Text
Keywords: computed tomography; super-resolution; region-of-interest tomography; machine learning; deep learning computed tomography; super-resolution; region-of-interest tomography; machine learning; deep learning
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Hendriksen, A.A.; Pelt, D.M.; Palenstijn, W.J.; Coban, S.B.; Batenburg, K.J. On-the-Fly Machine Learning for Improving Image Resolution in Tomography. Appl. Sci. 2019, 9, 2445.

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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.2657644
    Description: Oatmeal Data: Experimental cone-beam tomographic data for techniques to improve image resolution
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