Author
Listed:
- Aleksandr Smolin
(Smart Engines Service LLC, Moscow 121205, Russia
Phystech School of Applied Mathematics and Informatics, Moscow Institute of Physics and Technology, Moscow 117303, Russia)
- Andrei Yamaev
(Smart Engines Service LLC, Moscow 121205, Russia
Physics Department, Moscow State University, Moscow 119234, Russia)
- Anastasia Ingacheva
(Smart Engines Service LLC, Moscow 121205, Russia
Institute for Information Transmission Problems (Kharkevich Institute) RAS, Moscow 127051, Russia)
- Tatyana Shevtsova
(University Clinical Hospital No. 3 of the Clinical Center, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow 119991, Russia)
- Dmitriy Polevoy
(Smart Engines Service LLC, Moscow 121205, Russia
Federal Research Center Computer Science and Control RAS, Moscow 119333, Russia
National University of Science and Technology MISIS, Moscow 119049, Russia)
- Marina Chukalina
(Smart Engines Service LLC, Moscow 121205, Russia
FSRC Crystallography and Photonics RAS, Moscow 119333, Russia)
- Dmitry Nikolaev
(Smart Engines Service LLC, Moscow 121205, Russia
Institute for Information Transmission Problems (Kharkevich Institute) RAS, Moscow 127051, Russia)
- Vladimir Arlazarov
(Smart Engines Service LLC, Moscow 121205, Russia
Federal Research Center Computer Science and Control RAS, Moscow 119333, Russia)
Abstract
In computed tomography, state-of-the-art reconstruction is based on neural network (NN) algorithms. However, NN reconstruction algorithms can be not robust to small noise-like perturbations in the input signal. A not robust NN algorithm can produce inaccurate reconstruction with plausible artifacts that cannot be detected. Hence, the robustness of NN algorithms should be investigated and evaluated. There have been several attempts to construct the numerical metrics of the NN reconstruction algorithms’ robustness. However, these metrics estimate only the probability of the easily distinguishable artifacts occurring in the reconstruction. However, these methods measure only the probability of appearance of easily distinguishable artifacts on the reconstruction, which cannot lead to misdiagnosis in clinical applications. In this work, we propose a new method for numerical estimation of the robustness of the NN reconstruction algorithms. This method is based on the probability evaluation for NN to form such selected additional structures during reconstruction which may lead to an incorrect diagnosis. The method outputs a numerical score value from 0 to 1 that can be used when benchmarking the robustness of different reconstruction algorithms. We employed the proposed method to perform a comparative study of seven reconstruction algorithms, including five NN-based and two classical. The ResUNet network had the best robustness score (0.65) among the investigated NN algorithms, but its robustness score is still lower than that of the classical algorithm SIRT (0.989). The investigated NN models demonstrated a wide range of robustness scores (0.38–0.65). Thus, in this work, robustness of 7 reconstruction algorithms was measured using the new proposed score and it was shown that some of the neural algorithms are not robust.
Suggested Citation
Aleksandr Smolin & Andrei Yamaev & Anastasia Ingacheva & Tatyana Shevtsova & Dmitriy Polevoy & Marina Chukalina & Dmitry Nikolaev & Vladimir Arlazarov, 2022.
"Reprojection-Based Numerical Measure of Robustness for CT Reconstruction Neural Network Algorithms,"
Mathematics, MDPI, vol. 10(22), pages 1-17, November.
Handle:
RePEc:gam:jmathe:v:10:y:2022:i:22:p:4210-:d:969599
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