Applied mathematics in science and engineering最新文献_第3页

Phase-insensitive versus phase-sensitive ultrasound absorption tomography in the frequency domain 频域相不敏感与相敏感超声吸收断层扫描

Applied mathematics in science and engineering Pub Date : 2023-09-14 DOI: 10.1080/27690911.2023.2252571

Santeri Kaupinmäki, Ben Cox, Simon Arridge

引用次数: 0

Structural Gaussian priors for Bayesian CT reconstruction of subsea pipes 海底管道贝叶斯CT重构的结构高斯先验

Applied mathematics in science and engineering Pub Date : 2023-06-26 DOI: 10.1080/27690911.2023.2224918

Silja L. Christensen, Nicolai A. B. Riis, Felipe Uribe, Jakob S. Jørgensen

{"title":"Structural Gaussian priors for Bayesian CT reconstruction of subsea pipes","authors":"Silja L. Christensen, Nicolai A. B. Riis, Felipe Uribe, Jakob S. Jørgensen","doi":"10.1080/27690911.2023.2224918","DOIUrl":"https://doi.org/10.1080/27690911.2023.2224918","url":null,"abstract":"A non-destructive testing (NDT) application of X-ray computed tomography (CT) is inspection of subsea pipes in operation via 2D cross-sectional scans. Data acquisition is time-consuming and costly due to the challenging subsea environment. While reducing the number of projections in a CT scan yields time and cost savings, this compromises the reconstruction quality when conventional reconstruction methods are used. To address this issue we take a Bayesian approach to CT reconstruction and focus on designing an effective prior that introduces additional information to the problem. We propose a new class of structural Gaussian priors to enforce expected material properties in different regions of the reconstructed image based on available a-priori information about the pipe's layered structure. The prior is composed from Gaussian distributions, and we utilize this to propose an efficient computational strategy to sample the resulting posterior distribution. This is essential in practical NDT applications for fast processing of the large-scale data we see in CT. Numerical experiments with synthetic and real data show that the proposed structural Gaussian prior reduces artifacts and enhances contrast in the reconstruction, compared to using only a conventional Gaussian Markov random field prior or no prior information at all. Furthermore, we obtain uncertainty estimates that indicate the certainty of the reconstructions increase when more prior information is added.","PeriodicalId":475946,"journal":{"name":"Applied mathematics in science and engineering","volume":"288 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135608972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-rank flat-field correction for artifact reduction in spectral computed tomography 光谱计算机断层扫描中伪影减少的低秩平场校正

Applied mathematics in science and engineering Pub Date : 2023-03-06 DOI: 10.1080/27690911.2023.2176000

Katrine Ottesen Bangsgaard, Genoveva Burca, Evelina Ametova, Martin Skovgaard Andersen, Jakob Sauer Jørgensen

{"title":"Low-rank flat-field correction for artifact reduction in spectral computed tomography","authors":"Katrine Ottesen Bangsgaard, Genoveva Burca, Evelina Ametova, Martin Skovgaard Andersen, Jakob Sauer Jørgensen","doi":"10.1080/27690911.2023.2176000","DOIUrl":"https://doi.org/10.1080/27690911.2023.2176000","url":null,"abstract":"Spectral computed tomography has received considerable interest in recent years since spectral measurements contain much richer information about the object of interest. In spectral computed tomography, we are interested in the energy channel-wise reconstructions of the object. However, such reconstructions suffer from a low signal-to-noise ratio and share the challenges of conventional low-dose computed tomography such as ring artifacts. Ring artifacts arise from errors in the flat fields and can significantly degrade the quality of the reconstruction. We propose an extended flat-field model that exploits high correlation in the spectral flat fields to reduce ring artifacts in channel-wise reconstructions. The extended model relies on the assumption that the spectral flat fields can be well-approximated by a low-rank matrix. Our proposed model works directly on the spectral flat fields and can be combined with any existing reconstruction model, e.g. filtered back projection and iterative methods. The proposed model is validated on a neutron data set. The results show that our method successfully diminishes ring artifacts and improves the quality of the reconstructions. Moreover, the results indicate that our method is robust; it only needs a single spectral flat-field image, whereas existing methods need multiple spectral flat-field images to reach a similar level of ring reduction.","PeriodicalId":475946,"journal":{"name":"Applied mathematics in science and engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135079991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian approach to CT reconstruction with uncertain geometry 几何不确定CT重建的贝叶斯方法

Applied mathematics in science and engineering Pub Date : 2023-01-21 DOI: 10.1080/27690911.2023.2166041

Frederik Hagsholm Pedersen, Jakob Sauer Jørgensen, Martin Skovgaard Andersen

{"title":"A Bayesian approach to CT reconstruction with uncertain geometry","authors":"Frederik Hagsholm Pedersen, Jakob Sauer Jørgensen, Martin Skovgaard Andersen","doi":"10.1080/27690911.2023.2166041","DOIUrl":"https://doi.org/10.1080/27690911.2023.2166041","url":null,"abstract":"Computed tomography (CT) is a method for synthesizing volumetric or cross-sectional images of an object from a collection of projections. Popular reconstruction methods for CT are based on the assumption that the projection geometry, which describes the relative location and orientation of the radiation source, object, and detector for each projection, is exactly known. However, in practice, these geometric parameters are estimated quantities with uncertainty. A failure to accurately estimate the geometry may lead to reconstructions with severe misalignment artifacts that significantly decrease their scientific or diagnostic value. We propose a novel reconstruction method that jointly estimates the reconstruction and the center-of-rotation offset for fan-beam tomography. The reconstruction method is based on a Bayesian approach that yields a point estimate for the reconstruction and center-of-rotation offset and, in addition, provides valuable information regarding their uncertainty. This is achieved by approximately sampling from the joint posterior distribution of the reconstruction and center-of-rotation offset using a hierarchical Gibbs sampler. Our methodology is highly flexible and can be adapted to other geometric parameters and/or scan modes. Through numerical experiments based on real tomographic data, we compare the proposed Bayesian method to two existing approaches to the problem of correcting the geometry and demonstrate that our method achieves comparable or better results under challenging conditions.","PeriodicalId":475946,"journal":{"name":"Applied mathematics in science and engineering","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135046718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0