Abstract
This paper is concerned with the originally formulated 3D statistical model-based iterative reconstruction algorithm for spiral cone-beam x-ray tomography. The conception proposed here is based on a continuous-continuous data model, and a reconstruction problem is formulated as a shift invariant system. This algorithm significantly improves the quality of the subsequently reconstructed images, so allowing a decrease in the x-ray dose absorbed by a patient. The analytical roots of the algorithm proposed here permit a decrease in the complexity of the reconstruction problem in comparison with other model-based iterative approaches. In this paper, we proved that this statistical approach, originally formulated for parallel beam geometry, can be adapted for helical cone-beam geometry of scanner, with the direct use of projections. Computer simulations have shown that the reconstruction algorithm presented here outperforms conventional analytical methods with regard to the image quality obtained.
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Acknowledgment
The authors thank Dr. Cynthia McCoullough and the American Association of Physicists in Medicine for providing the Low-Dose CT Grand Challenge dataset.
This work was partly supported by The National Centre for Research and Development in Poland (Research Project POIR.01.01.01-00-0463/17).
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Cierniak, R., Pluta, P. (2019). Iterative Statistical Reconstruction Algorithm Based on C-C Data Model with the Direct Use of Projections Performed in Spiral Cone-Beam CT Scanners. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_6
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