Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data

Scientific Reports, vol. 12, pp. 893

Abstract

In x-ray computed tomography (CT), the achievable image resolution is typically limited by several pre-fixed characteristics of the x-ray source and detector. Structuring the x-ray beam using a mask with alternating opaque and transmitting septa can overcome this limit. However, the use of a mask imposes an undersampling problem: to obtain complete datasets, significant lateral sample stepping is needed in addition to the sample rotation, resulting in high x-ray doses and long acquisition times. Cycloidal CT, an alternative scanning scheme by which the sample is rotated and translated simultaneously, can provide high aperture-driven resolution without sample stepping, resulting in a lower radiation dose and faster scans. However, cycloidal sinograms are incomplete and must be restored before tomographic images can be computed. In this work, we demonstrate that high-quality images can be reconstructed by applying the recently proposed Mixed Scale Dense (MS-D) convolutional neural network (CNN) to this task. We also propose a novel training approach by which training data are acquired as part of each scan, thus removing the need for large sets of pre-existing reference data, the acquisition of which is often not practicable or possible. We present results for both simulated datasets and real-world data, showing that the combination of cycloidal CT and machine learning-based data recovery can lead to accurate high-resolution images at a limited dose.

Authors 5

1. Daniël M. Pelt corresponding Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333, CA, Leiden, The Netherlands. d.m.pelt@liacs.leidenuniv.nl

   Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333, CA, Leiden, The Netherlands

2. Oriol Roche i Morgó

   University College London

   Affiliation as printed

   Department of Medical Physics and Biomedical Engineering, University College London, Malet Place, London, WC1E 6BT, UK

3. Charlotte Maughan Jones

   University College London

   Affiliation as printed

   Department of Medical Physics and Biomedical Engineering, University College London, Malet Place, London, WC1E 6BT, UK

4. Alessandro Olivo

   University College London

   Affiliation as printed

   Department of Medical Physics and Biomedical Engineering, University College London, Malet Place, London, WC1E 6BT, UK

5. Charlotte Klara Hagen

   University College London

   Affiliation as printed

   Department of Medical Physics and Biomedical Engineering, University College London, Malet Place, London, WC1E 6BT, UK