Lightweight denoising filtering neural network for FBP algorithm

Andrei Yamaev; Marina Chukalina; Dmitry Nikolaev; Alexander Sheshkus; Alexey Chulichkov

doi:10.1117/12.2587185

4 January 2021 Lightweight denoising filtering neural network for FBP algorithm

Andrei Yamaev, Marina Chukalina, Dmitry Nikolaev, Alexander Sheshkus, Alexey Chulichkov

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116050L (2021) https://doi.org/10.1117/12.2587185
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

In that paper, we a suggest lightweight filtering neural network, which implements the filtering stage in the Filtered Back-Projection algorithm (FBP), but good reconstruction results are achieved not only in ideal data but also in noisy data, which a usual FBP algorithm cannot achieve. Thus, our neural network is not an only variation of Ramp filter, which is usually used then FBP algorithm, but also a denoising filter. The neural network architecture was inspired with the idea of the possibility of the Ramp filtering operation’s approximation with sufficient accuracy. The efficiency of our network was shown on the synthetic data, which imitate tomographic projections collected with low exposition. In the generation of synthetic data, we have taken into account the quantum nature of X-ray radiation, exposition time of one frame, and non-linear detector response. The FBP reconstruction time with our neural network was 13 times faster than the time of reconstruction neural network from Learned Primal-Dual Reconstruction, and our reconstruction quality 0.906 by SSIM metric, which is enough to identify most significant objects.

Citation Download Citation

Andrei Yamaev, Marina Chukalina, Dmitry Nikolaev, Alexander Sheshkus, and Alexey Chulichkov "Lightweight denoising filtering neural network for FBP algorithm", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050L (4 January 2021); https://doi.org/10.1117/12.2587185

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