Applied Mathematics for Modern Challenges最新文献

Resolution improvement and algorithmic dependence of machine learning for post-processing respiratory EIT images 呼吸EIT图像后处理中机器学习的分辨率改进及算法依赖

Applied Mathematics for Modern Challenges Pub Date : 2023-01-01 DOI: 10.3934/ammc.2023003

Talles Batista Rattis Santos, Rafael Mikio Nakanishi, Tayran Milá Mendes Olegário, Raul Gonzalez Lima, Jennifer L. Mueller

{"title":"Resolution improvement and algorithmic dependence of machine learning for post-processing respiratory EIT images","authors":"Talles Batista Rattis Santos, Rafael Mikio Nakanishi, Tayran Milá Mendes Olegário, Raul Gonzalez Lima, Jennifer L. Mueller","doi":"10.3934/ammc.2023003","DOIUrl":"https://doi.org/10.3934/ammc.2023003","url":null,"abstract":"Electrical impedance tomography (EIT) is an imaging modality in which electric fields arising from currents applied on electrodes are used to form dynamic images of physiological processes, such as respiration, by plotting the reconstructed conductivity distribution. This work investigates the effectiveness of using machine learning to post-process EIT images of respiration, validated against a CT scan taken immediately after the EIT data collection, and the dependence of the results on the reconstruction algorithm used to compute the pre-processed image. Here, a training set for post-processing is computed from a set of CT scans, and a deep learning neural network is used to post-process EIT images from patients with cystic fibrosis reconstructed using (a) the D-bar method and (b) one step of a Gauss-Newton method. The images are compared using the structural similarity index measure (SSIM) to 'ground truth' EIT images derived from the CT scans. Results show that while the deep learning post-processing method effectively sharpens edges and reduces blurring, the spatial accuracy depends on the original algorithm used to compute the pre-processed image. The D-bar images result in a higher SSIM than the Gauss-Newton images, and while both sets of images are able to detect a large region of air trapping, they differ when estimating the extent of pathology.","PeriodicalId":493031,"journal":{"name":"Applied Mathematics for Modern Challenges","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135053465","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 deep neural network for a hemiarray EIT system 半阵列EIT系统的深度神经网络

Applied Mathematics for Modern Challenges Pub Date : 2023-01-01 DOI: 10.3934/ammc.2023004

Mason Manning, Nicholas Wharff, Shelby Horth, Jacob Roarty, Rosalind J. Sadleir, Malena I. Español

{"title":"A deep neural network for a hemiarray EIT system","authors":"Mason Manning, Nicholas Wharff, Shelby Horth, Jacob Roarty, Rosalind J. Sadleir, Malena I. Español","doi":"10.3934/ammc.2023004","DOIUrl":"https://doi.org/10.3934/ammc.2023004","url":null,"abstract":"Electrical Impedance Tomography (EIT) can map electrical property distributions within the body using a surface electrode array. EIT systems using a circumferential array applied to the abdomen can be used to monitor acute intra-abdominal hemorrhages in trauma patients. Nevertheless, these patients may also have suffered spinal injuries that might be exacerbated by lifting the patient to place the array. Thus, a half array ('hemiarray') applied only to the anterior abdomen may be more practical. However, severe reconstruction artifacts result in posterior regions using standard EIT reconstruction methods. This study proposes a novel machine learning-based approach for standard full and hemiarray EIT reconstructions, demonstrating superior reconstruction characteristics compared to conventional methods. Notably, our method mitigates the challenges of reconstructing anomalies in posterior regions. This performance advantage was consistently observed across reconstructions from simulated and real tank data. Based on our findings, we conclude that the machine learning-based hemiarray reconstruction method holds significant promise for challenging imaging scenarios, particularly when access to the anterior or posterior abdomen is restricted.","PeriodicalId":493031,"journal":{"name":"Applied Mathematics for Modern Challenges","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135053768","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

Limited-angle tomography reconstruction via deep end-to-end learning on synthetic data 基于合成数据的深度端到端学习的有限角度断层扫描重建

Applied Mathematics for Modern Challenges Pub Date : 2023-01-01 DOI: 10.3934/ammc.2023006

Thomas Germer, Jan Robine, Sebastian Konietzny, Stefan Harmeling, Tobias Uelwer

引用次数: 0

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs 原木稀疏序列x射线测量的重建和分割

Applied Mathematics for Modern Challenges Pub Date : 2023-01-01 DOI: 10.3934/ammc.2023002

Sebastian Springer, Aldo Glielmo, Angelina Senchukova, Tomi Kauppi, Jarkko Suuronen, Lassi Roininen, Heikki Haario, Andreas Hauptmann

{"title":"Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs","authors":"Sebastian Springer, Aldo Glielmo, Angelina Senchukova, Tomi Kauppi, Jarkko Suuronen, Lassi Roininen, Heikki Haario, Andreas Hauptmann","doi":"10.3934/ammc.2023002","DOIUrl":"https://doi.org/10.3934/ammc.2023002","url":null,"abstract":"In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call it a sequential scanning geometry. In this case, each slice on its own does not carry sufficient information to reconstruct a useful tomographic image. Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object. Additionally, we propose to use an unsupervised clustering approach known as Density Peak Advanced, to perform a segmentation and spot density anomalies in the internal structure of the reconstructed objects. We evaluate the method in a proof of concept study for the application of wood log scanning for the industrial sawing process, where the goal is to spot anomalies within the wood log to allow for optimal sawing patterns. Reconstruction and segmentation quality are evaluated from experimental measurement data for various scenarios of severely undersampled X-measurements. Results show clearly that an improvement in reconstruction quality can be obtained by employing the Dimension reduced Kalman Filter allowing to robustly obtain the segmented logs.","PeriodicalId":493031,"journal":{"name":"Applied Mathematics for Modern Challenges","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135053807","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

HD-DCDM: Hybrid-domain network for limited-angle computed tomography with deconvolution and conditional diffusion model HD-DCDM:具有反褶积和条件扩散模型的有限角度计算机断层扫描混合域网络

Applied Mathematics for Modern Challenges Pub Date : 2023-01-01 DOI: 10.3934/ammc.2023008

Jianyu Wang, Rongqian Wang, Lide Cai, Xintong Liu, Guochang Lin, Fukai Chen, Lingyun Qiu

{"title":"HD-DCDM: Hybrid-domain network for limited-angle computed tomography with deconvolution and conditional diffusion model","authors":"Jianyu Wang, Rongqian Wang, Lide Cai, Xintong Liu, Guochang Lin, Fukai Chen, Lingyun Qiu","doi":"10.3934/ammc.2023008","DOIUrl":"https://doi.org/10.3934/ammc.2023008","url":null,"abstract":"Limited-angle computed tomography (LACT) has gained significant attention in recent years due to its wide range of applications. Despite the numerous algorithms proposed to improve imaging quality, reconstructing fine details remains a challenging problem. In this paper, we propose a novel hybrid domain framework that combines classical methods and learning-based methods to address this challenge. Our framework decomposes the solution of the least-squares problem into back-projection and deconvolution steps, leading to a significant improvement in reconstruction quality. Furthermore, we employ a conditional diffusion model to further fine-tune the reconstruction results, simultaneously preserving data consistency and enhancing the realness of the reconstructed images. The effectiveness of the proposed framework is evaluated using the Helsinki Tomography Challenge 2022 (HTC 2022) dataset. Comparative evaluations demonstrate that our framework outperforms previous methods in both visual quality and quantitative measures. These findings highlight the potential of the proposed framework in improving LACT reconstruction and offer valuable insights for advancing imaging techniques in various fields.","PeriodicalId":493031,"journal":{"name":"Applied Mathematics for Modern Challenges","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135106491","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

Leveraging joint sparsity in 3D synthetic aperture radar imaging 利用三维合成孔径雷达成像中的关节稀疏性

Applied Mathematics for Modern Challenges Pub Date : 2023-01-01 DOI: 10.3934/ammc.2023005

Dylan Green, JR Jamora, Anne Gelb

引用次数: 0

Model-based deep learning approaches to the Helsinki Tomography Challenge 2022 2022年赫尔辛基断层扫描挑战赛的基于模型的深度学习方法

Applied Mathematics for Modern Challenges Pub Date : 2023-01-01 DOI: 10.3934/ammc.2023007

Clemens Arndt, Alexander Denker, Sören Dittmer, Johannes Leuschner, Judith Nickel, Maximilian Schmidt

引用次数: 0