利用噪声水平估计实现工业相关的基于全变分的少视点计算机断层扫描重建

IF 5.4 2区工程技术 Q2 ENGINEERING, MANUFACTURING

CIRP Journal of Manufacturing Science and Technology Pub Date : 2025-06-29 DOI:10.1016/j.cirpj.2025.06.008

Maryam Bahrkazemi , Alexander Rohde , Jonathan Hess , Sven Gondrom-Linke , Patricio Guerrero , Wim Dewulf

{"title":"利用噪声水平估计实现工业相关的基于全变分的少视点计算机断层扫描重建","authors":"Maryam Bahrkazemi , Alexander Rohde , Jonathan Hess , Sven Gondrom-Linke , Patricio Guerrero , Wim Dewulf","doi":"10.1016/j.cirpj.2025.06.008","DOIUrl":null,"url":null,"abstract":"<div><div>To extend the applicability of in-line computed tomography (CT) within Industry 4.0, accelerating the data acquisition and image reconstruction process is essential to meet the demands of real-time, high-throughput inspection. This paper focuses on accelerating in-line CT by addressing the trade-off between image quality and angular sampling reduction through the development of dedicated reconstruction algorithms. Various inherent properties of in-line CT are leveraged as a priori knowledge, specifically the noise level, within a total variation (TV)-based reconstruction framework to enhance reconstruction quality, support automation, and enable accurate image analysis using 2%–5% of the data required by standard methods such as Feldkamp–Davis–Kress (FDK).</div></div>","PeriodicalId":56011,"journal":{"name":"CIRP Journal of Manufacturing Science and Technology","volume":"61 ","pages":"Pages 336-352"},"PeriodicalIF":5.4000,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards industrially relevant total variation based reconstruction of few-view computed tomography by exploiting noise level estimation\",\"authors\":\"Maryam Bahrkazemi , Alexander Rohde , Jonathan Hess , Sven Gondrom-Linke , Patricio Guerrero , Wim Dewulf\",\"doi\":\"10.1016/j.cirpj.2025.06.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>To extend the applicability of in-line computed tomography (CT) within Industry 4.0, accelerating the data acquisition and image reconstruction process is essential to meet the demands of real-time, high-throughput inspection. This paper focuses on accelerating in-line CT by addressing the trade-off between image quality and angular sampling reduction through the development of dedicated reconstruction algorithms. Various inherent properties of in-line CT are leveraged as a priori knowledge, specifically the noise level, within a total variation (TV)-based reconstruction framework to enhance reconstruction quality, support automation, and enable accurate image analysis using 2%–5% of the data required by standard methods such as Feldkamp–Davis–Kress (FDK).</div></div>\",\"PeriodicalId\":56011,\"journal\":{\"name\":\"CIRP Journal of Manufacturing Science and Technology\",\"volume\":\"61 \",\"pages\":\"Pages 336-352\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CIRP Journal of Manufacturing Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1755581725000999\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CIRP Journal of Manufacturing Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755581725000999","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}

引用次数: 0

摘要

为了在工业4.0中扩展在线计算机断层扫描（CT）的适用性，加快数据采集和图像重建过程对于满足实时、高通量检测的需求至关重要。本文的重点是通过开发专用重建算法来解决图像质量和角度采样减少之间的权衡，从而加速在线CT。在基于总变差（TV）的重建框架中，利用直列CT的各种固有属性作为先验知识，特别是噪声水平，以提高重建质量，支持自动化，并使用feldkam - davis - kress （FDK）等标准方法所需的2%-5%的数据实现准确的图像分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Towards industrially relevant total variation based reconstruction of few-view computed tomography by exploiting noise level estimation

To extend the applicability of in-line computed tomography (CT) within Industry 4.0, accelerating the data acquisition and image reconstruction process is essential to meet the demands of real-time, high-throughput inspection. This paper focuses on accelerating in-line CT by addressing the trade-off between image quality and angular sampling reduction through the development of dedicated reconstruction algorithms. Various inherent properties of in-line CT are leveraged as a priori knowledge, specifically the noise level, within a total variation (TV)-based reconstruction framework to enhance reconstruction quality, support automation, and enable accurate image analysis using 2%–5% of the data required by standard methods such as Feldkamp–Davis–Kress (FDK).

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

CIRP Journal of Manufacturing Science and Technology Engineering-Industrial and Manufacturing Engineering

CiteScore

9.10

自引率

6.20%

发文量

166

审稿时长

63 days

期刊介绍： The CIRP Journal of Manufacturing Science and Technology (CIRP-JMST) publishes fundamental papers on manufacturing processes, production equipment and automation, product design, manufacturing systems and production organisations up to the level of the production networks, including all the related technical, human and economic factors. Preference is given to contributions describing research results whose feasibility has been demonstrated either in a laboratory or in the industrial praxis. Case studies and review papers on specific issues in manufacturing science and technology are equally encouraged.