Monitored reconstruction improved by post-processing neural network , A.V. Yamaev, and Computer Optics, 2024 Computed tomography (CT) is widely utilized for analyzing internal structures, but the limitations of traditional reconstruction algorithms, which often require a large number of projections, restrict their effectiveness in time-critical tasks or for biological objects studying. Recently Monitored reconstruction approach was proposed for reducing the requirement of dose load. In this paper, there were investigated the advantages of using post-processing neural networks within a monitored reconstruction approach. Three algorithms, namely FBP, FBPConvNet, and LRFR, are evaluated based on their mean count of projections required for the achievement of target reconstruction accuracy. A novel training method specifically designed for neural network algorithms within the Monitored reconstruction framework is proposed. It is shown that the use of the LRFR approach allows one to achieve both a reduction in the number of measured projections and an improvement in the reconstruction accuracy over a certain range of stopping rules. These findings highlight the significant potential of neural networks to be used in the Monitored reconstruction approach.
Segmentation of Human Olfactory Bulb Glomeruli on Its Phase-Contrast Tomographic Images with Neural Networks Aleksandr Smolin, Marina Chukalina, Inna Bukreeva, Olga Junemann, Alessia Cedola, Michela Fratini, Sergei Saveliev, Andrey Yamaev Proceedings of SPIE the International Society for Optical Engineering, 2024 The human olfactory bulb (OB), an important part of the brain responsible for the sense of smell, is a complex structure composed of multiple layers and cell types. Studying the OB morphological structure is essential for understanding the decline in olfactory function related to aging, neurodegenerative disorders, and other pathologies. Traditional microscopy methods in which slices are stained with solutions to contrast individual elements of the morphological structure are destructive. Non-destructive high-resolution technique is the X-ray phase-contrast tomography. However, manual segmentation of the reconstructed images are time-consuming due to large amount of data and prone to errors. U-Net-based model to optimize the segmentation of OB morphological structures, focusing specifically on glomeruli, in tomographic images of the human OB is proposed. The strategy to address overfitting and enhance the model's accuracy is described. This method addresses the challenges posed by complex limited data containing abundant details, similar grayscale levels between soft tissues, and blurry image details. Additionally, it successfully overcomes the limitations of a small dataset containing images with extremely dense point clouds, preventing the models from overfitting.
Computer Tomography as an Artificial Intelligence Instrument—the Survey of Approach and Results of V.L. Arlazarov’s Scientific School A. S. Ingacheva, M. I. Gilmanov, A. V. Yamaev, A. V. Buzmakov, D. D. Kazimirov, I. A. Kunina, Zh. V. Soldatova, M. V. Chukalina, V. V. Arlazarov Pattern Recognition and Image Analysis, 2023 Abstract The article presents the results of research in the field of computational X-ray tomography, obtained within the framework of the scientific school, by Doctor of Engineering, Corresponding Member of the Russian Academy of Sciences V.L. Arlazarov, on artificial intelligence. The field of computed tomography, which is relatively young for the school, arose as a result of a combination of combinatorial optimization approaches, training neural network models, image processing, and solving the problem of stopping information acquisition. Thanks to the accumulated experience, four areas of research can be distinguished: fast algorithms for tomographic reconstruction, scanning protocols for monitored tomographic reconstruction, neural network approaches to reconstruction, as well as methods for suppressing artifacts and distortions that occur in tomographic reconstructions. This article reflects the main successes and achievements obtained in these areas, which are demonstrated using the example of specific applied solutions.
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, Vladimir Arlazarov Mathematics, 2022 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.
Deep Learning-Based Segmentation of Post-Mortem Human’s Olfactory Bulb Structures in X-ray Phase-Contrast Tomography Alexandr Meshkov, Anvar Khafizov, Alexey Buzmakov, Inna Bukreeva, Olga Junemann, Michela Fratini, Alessia Cedola, Marina Chukalina, Andrei Yamaev, Giuseppe Gigli, Fabian Wilde, Elena Longo, Victor Asadchikov, Sergey Saveliev, Dmitry Nikolaev Tomography, 2022 The human olfactory bulb (OB) has a laminar structure. The segregation of cell populations in the OB image poses a significant challenge because of indistinct boundaries of the layers. Standard 3D visualization tools usually have a low resolution and cannot provide the high accuracy required for morphometric analysis. X-ray phase contrast tomography (XPCT) offers sufficient resolution and contrast to identify single cells in large volumes of the brain. The numerous microanatomical structures detectable in XPCT image of the OB, however, greatly complicate the manual delineation of OB neuronal cell layers. To address the challenging problem of fully automated segmentation of XPCT images of human OB morphological layers, we propose a new pipeline for tomographic data processing. Convolutional neural networks (CNN) were used to segment XPCT image of native unstained human OB. Virtual segmentation of the whole OB and an accurate delineation of each layer in a healthy non-demented OB is mandatory as the first step for assessing OB morphological changes in smell impairment research. In this framework, we proposed an effective tool that could help to shed light on OB layer-specific degeneration in patients with olfactory disorder.
Neural network regularization in the problem of few-view computed tomography A.V. Yamaev, , M.V. Chukalina, D.P. Nikolaev, L.G. Kochiev, A.I. Chulichkov, , , , , , , , and Computer Optics, 2022 The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.
Neural Network for Data Preprocessing in Computed Tomography A. V. Yamaev, M. V. Chukalina, D. P. Nikolaev, A. V. Sheshkus, A. I. Chulichkov Automation and Remote Control, 2021 We propose a lightweight noise-canceling filtering neural network that implements the filtering stage in the algorithm for tomographic reconstruction of convolution and backprojection (Filtered BackProjection—FBP). We substantiate the neural network architecture, selected on the basis of the possibility of approximating the ramp filtering operation with sufficient accuracy. The network performance has been demonstrated using synthetic data that mimics low-exposure tomographic projections. The quantum nature of X-ray radiation, the exposure time of one frame, and the nonlinear response of the ionizing radiation detector are taken into account when generating the synthetic data. The reconstruction time using the proposed network is 11 times shorter than that of the heavy networks selected for comparison, with the reconstruction quality in the $$SSIM$$ metric above 0.9.
Lightweight denoising filtering neural network for FBP algorithm Andrei Yamaev, Marina Chukalina, Dmitry Nikolaev, Alexander Sheshkus, Alexey Chulichkov Proceedings of SPIE the International Society for Optical Engineering, 2020 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.
