Reprojection-Based Numerical Measure of Robustness for CT Reconstruction Neural Network Algorithms

Aleksandr Smolin, Andrei Yamaev (), Anastasia Ingacheva, Tatyana Shevtsova, Dmitriy Polevoy, Marina Chukalina, Dmitry Nikolaev and Vladimir Arlazarov ()
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Aleksandr Smolin: Smart Engines Service LLC, Moscow 121205, Russia
Andrei Yamaev: Smart Engines Service LLC, Moscow 121205, Russia
Anastasia Ingacheva: Smart Engines Service LLC, Moscow 121205, 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
Marina Chukalina: Smart Engines Service LLC, Moscow 121205, Russia
Dmitry Nikolaev: Smart Engines Service LLC, Moscow 121205, Russia
Vladimir Arlazarov: Smart Engines Service LLC, Moscow 121205, Russia

Mathematics, 2022, vol. 10, issue 22, 1-17

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.

Keywords: robustness; neural network; computed tomography (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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