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

Autor: Daniël M. Pelt, Oriol Roche i Morgó, Charlotte Maughan Jones, Alessandro Olivo, Charlotte K. Hagen
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-022-04910-y
Popis: 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.
Databáze: Directory of Open Access Journals
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