Abstract
In computed tomography, several well-known techniques exist that can reconstruct a cross section of an object from a finite set of it’s projections, the sinogram. This task – the numerical inversion of the Radon transform – is well understood, with state of the art algorithms mostly relying on back-projection. Even though back-projection has a significant computational burden compared to the family of direct Fourier reconstruction based methods, the latter class of algorithms is less popular due to the complications related to frequency space resampling. Moreover, interpolation errors in resampling in frequency domain can lead to artifacts in the reconstructed image. Here, we present a novel neural-network assisted reconstruction method, that intends to reconstruct the object in frequency space, while taking the well-understood Fourier slice theorem into account as well. In our case, the details of approximated resampling is learned by the network for peak performance. We show that with this method it is possible to achieve comparable, and in some cases better reconstruction quality than with another state of the art algorithm also working in frequency domain.
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Acknowledgments
The authors would like to thank Rajmund Mokso for supplying the real test dataset for the studies.
This research was supported by Project no. TKP2021-NVA-09. Project no. TKP2021-NVA-09 has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme.
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Domokos, Z., Varga, L.G. (2022). Fourier Domain CT Reconstruction with Complex Valued Neural Networks. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_32
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DOI: https://doi.org/10.1007/978-3-031-09037-0_32
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