Lightweight denoising filtering neural network for FBP algorithm

Yamaev, Andrei; Chukalina, Marina; Nikolaev, Dmitry P.; Sheshkus, Alexander; Chulichkov, A. I.

doi:10.1117/12.2587185

Cited by 9 publications

(11 citation statements)

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“…9(c), without the post-CNN architecture, the reconstructed results would be contaminated seriously by severe streak artifacts and fake structures. As described in [51], good reconstruction results can be achieved by the 'pre-CNN+BP' method with a large receptive field and large training datasets. In this work, to decrease the complexity of the pre-CNN, the proposed method reduces the depth of the pre-CNN, which results in a reduction of the receptive field.…”

Section: Discussionmentioning

confidence: 99%

A Dual-Domain CNN-Based Network for CT Reconstruction

Jiao

Gui

et al. 2021

IEEE Access

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Convolutional neural network (CNN)-based deep learning techniques have enjoyed many successful applications in the field of medical imaging. However, the complicated between-manifold projection from the projection domain to the spatial domain hinders the direct application of CNN techniques in computed tomography (CT) reconstruction. In this work, we proposed a novel CT reconstruction framework based on a CNN, i.e., an intelligent back-projection network (iBP-Net). The proposed iBP-Net method fused a pre-CNN, a back-projection layer, and a post-CNN into an end-to-end network. The pre-CNN adopted CNN techniques to model a filtering operation in the projection domain. In the back-projection layer, a back-projection operation was employed to perform between-manifold projection. Based on CNN techniques, the post-CNN worked together with the pre-CNN to recover reconstructed images from the outputs of the back-projection layer in the spatial domain while maintaining high visual sensitivity. The experimental results demonstrate the feasibility of the proposed iBP-Net framework for CT reconstruction.

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Section: Discussionmentioning

confidence: 99%

A Dual-Domain CNN-Based Network for CT Reconstruction

Jiao

Gui

et al. 2021

IEEE Access

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“…We also hope that the published data will be useful for the development of new robust CT-reconstruction methods [58][59][60][61][62][63][64] optimized for speed [65] and memory requirements.…”

Section: Table 3 Package Data Descriptionmentioning

confidence: 99%

From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence

Polevoy,

Kulagin,

Ingacheva

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Virtual unrolling or unfolding, digital unwrapping, flattening or unfurling - all these terms are used to describe the process of surface straightening of a tomographically reconstructed digital object. For many objects of historical heritage, tomography is the only way to obtain a hidden image of the original object without its destruction. Digital flattening is no longer considered a unique met hodology. It being applied by many research group, but AI-based methods are used insignificantly in such projects, despite the amazing success of AI in computer vision, in particular optical text recognition. It can be explained by the fact that the success of AI depends on large, broad and high quality datasets, but there are very few published CT-based datasets relevant to the task of digital flattening. Accumulation of a sufficient amount of data necessary for training models is a key point for the next technological breakthrough. In this paper, we present open and cumulative dataset CT-OCR-2022. Dataset includes 6 packages data for different model objects that help to enrich tomographic solutions and to train machine learning models. Each package contains optically scanned image of model objects, 400 measured X-ray projections, 2687 CT- reconstructed cross-sections of 3D reconstructed image, segmentation markups. We believe that CT-OCR-2022 dataset will serve as a benchmark for reconstructed object digital flattening and recognition systems, and that it will prove invaluable for advancement of the field of CT-reconstruction, symbols analysis and recognition. The data presented are openly available in Zenodo at doi:10.5281/zenodo.7123495 and linked repositories.

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“…The result of the 3D reconstruction from the fully collected set of projections is presented in Figure 2. A Filtered Back Projection algorithm was used for reconstruction [13].…”

Section: Nano-xct (Laboratory Tool)mentioning

confidence: 99%

Monitored Tomographic Reconstruction—An Advanced Tool to Study the 3D Morphology of Nanomaterials

et al. 2021

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Detailed and accurate three-dimensional (3D) information about the morphology of hierarchically structured materials is derived from multi-scale X-ray computed tomography (XCT) and subsequent 3D data reconstruction. High-resolution X-ray microscopy and nano-XCT are suitable techniques to nondestructively study nanomaterials, including porous or skeleton materials. However, laboratory nano-XCT studies are very time-consuming. To reduce the time-to-data by more than an order of magnitude, we propose taking advantage of a monitored tomographic reconstruction. The benefit of this new protocol for 3D imaging is that the data acquisition for each projection is interspersed by image reconstruction. We demonstrate this new approach for nano-XCT data of a novel transition-metal-based materials system: MoNi4 electrocatalysts anchored on MoO2 cuboids aligned on Ni foam (MoNi4/MoO2@Ni). Quantitative data that describe the 3D morphology of this hierarchically structured system with an advanced electrocatalytically active nanomaterial are needed to tailor performance and durability of the electrocatalyst system. We present the framework for monitored tomographic reconstruction, construct three stopping rules for various reconstruction quality metrics and provide their experimental evaluation.

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Lightweight denoising filtering neural network for FBP algorithm

Cited by 9 publications

References 0 publications

A Dual-Domain CNN-Based Network for CT Reconstruction

A Dual-Domain CNN-Based Network for CT Reconstruction

From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence

Monitored Tomographic Reconstruction—An Advanced Tool to Study the 3D Morphology of Nanomaterials

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