Tomographic Reconstruction: General Approach to Fast Back-Projection Algorithms

Polevoy, Dmitry; Gilmanov, Marat; Kazimirov, Danil; Chukalina, Marina; Ingacheva, Anastasia; Kulagin, Petr; Nikolaev, Dmitry

doi:10.3390/math11234759

Cited by 3 publications

(1 citation statement)

References 84 publications

(140 reference statements)

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“…We used inhause software using a method that works well under these conditions [21]. The back-projection stage is optimized using an algorithm from [22,23]. After reconstruction, image segmentation is the first step in computer-aided image analysis systems.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of human olfactory bulb glomeruli on its phase-contrast tomographic images with neural networks

Smolin,

Chukalina,

Bukreeva

et al. 2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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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.

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Section: Discussionmentioning

confidence: 99%

Segmentation of human olfactory bulb glomeruli on its phase-contrast tomographic images with neural networks

Smolin,

Chukalina,

Bukreeva

et al. 2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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Adaptive automated sinogram normalization for ring artifacts suppression in CT

Kazimirov,

Polevoy,

Ingacheva

et al. 2024

Opt. Express

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Ring artifacts pose a major barrier to obtaining precise reconstruction in computed tomography (CT). The presence of ring artifacts complicates the use of automatic means of processing CT reconstruction results, such as segmentation, correction of geometric shapes, alignment of reconstructed volumes. Although there are numerous efficient methods for suppressing ring artifacts, many of them appear to be manual. Along with this, a large proportion of the automatic methods cope unsatisfactorily with the target task while requiring computational capacity. The current work introduces a projection data preprocessing method for suppressing ring artifacts that constitutes a compromise among the outlined aspects – automaticity, high efficiency and computational speed. Derived as the automation of the classical sinogram normalization method, the proposed method specific advantages consist in adaptability in relation to the filtered sinograms and the edge-preservation property proven within the experiments on both synthetic and real CT data. Concerning the challenging open-access data, the method has performed superior quality comparable to that of the advanced methods: it has demonstrated 70.4% ring artifacts suppression percentage (RASP) quality metric. In application to our real laboratory CT data, the proposed method allowed us to gain significant refinement of the reconstruction quality which has not been surpassed by a range of compared manual ring artifacts suppression methods.

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Applicability and potential of monitored reconstruction in computed tomography

Gilmanov,

Bulatov,

Bugai

et al. 2024

PLoS ONE

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Monitored tomographic reconstruction (MTR) is a potentially powerful tool for dose and time reduction in computed tomography scanning. We are the first to study the issue of practical implementation of MTR protocols in current-generation real-life instruments. We propose an empirical quantitative model for calculating acquisition and reconstruction times. It is demonstrated that projection acquisition order has a significant impact on the time and dose of tomographic experiments. The new alternative acquisition most suitable for MTR protocols is proposed. To estimate the restrictions and scope of applicability for MTR four typical commercial setups are studied within a proposed model. We construct an experimental stand for achieving a real-time reconstruction, together with validation of the proposed acquisition time model. We demonstrate that real-time reconstruction may be implemented without slowing down an acquisition process. An optimization of reconstruction from partial data is proposed, which allowed the production of 385 and 440 reconstructions for standard and proposed acquisition orders correspondingly during a single acquisition of 512 projections. The results of the study demonstrate that with proposed optimizations MTR can be effectively utilized for practical applications using the current generation of existing setups in industrial and nano tomography fields.

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Tomographic Reconstruction: General Approach to Fast Back-Projection Algorithms

Cited by 3 publications

References 84 publications

Segmentation of human olfactory bulb glomeruli on its phase-contrast tomographic images with neural networks

Segmentation of human olfactory bulb glomeruli on its phase-contrast tomographic images with neural networks

Adaptive automated sinogram normalization for ring artifacts suppression in CT

Applicability and potential of monitored reconstruction in computed tomography

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