Empirical analysis of the optimality of RSRE-based stopping rules for monitored reconstruction

Konstantin Bulatov; Arseniy Mukovozov; Vladimir V. Arlazarov

doi:10.1117/12.2587184

4 January 2021 Empirical analysis of the optimality of RSRE-based stopping rules for monitored reconstruction

Konstantin Bulatov, Arseniy Mukovozov, Vladimir V. Arlazarov

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051Y (2021) https://doi.org/10.1117/12.2587184
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

One of the challenges present in the field of tomographic imaging is the reduction of the radiation dose imparted to the object. Monitored reconstruction is one of the approaches to reduce the dose by means of dynamic stopping of the scanning process. In this paper, analysis was performed for the RSRE-based stopping rules for monitored reconstruction, using the previously analyzed RSRE-based reconstruction quality metrics, a widely-used PSNR metric, as well as quality metrics designed to mimic human perception, such as SSIM and ISSIM. It was shown that the unnormalized RSRE-based stopping rule performs better than the baseline for SSIM, ISSIM, and PSNR, and that for the latter the best result was achieved using the stopping rule designed for the reconstruction quality metric normalized to the Radon invariant. The stopping rules were compared with a synthetic a-posteriori “perfect” stopping rule and it was shown that the RSRE-based stopping rules closely approach the perfect stopping rule for the RSRE metric normalized on the Radon invariant, as well as for the ISSIM metric.

Citation Download Citation

Konstantin Bulatov, Arseniy Mukovozov, and Vladimir V. Arlazarov "Empirical analysis of the optimality of RSRE-based stopping rules for monitored reconstruction", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051Y (4 January 2021); https://doi.org/10.1117/12.2587184

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