Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement
Multiphase-flow measurement cannot effectively capture mesoscale dynamic structures owing to limitations of spatial and temporal resolutions of current measuring techniques. Dynamic X-ray computed tomography (CT), as a non-invasive multiphase-flow measurement technique, is promising for measuring th...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Computerized Tomography Theory and Application
2025-05-01
|
| Series: | CT Lilun yu yingyong yanjiu |
| Subjects: | |
| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.097 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849309978542735360 |
|---|---|
| author | Qian CHEN Baodi YU Yanwei QIN Sunyang WANG Xiaohui SU Xin JIN Fanyong MENG |
| author_facet | Qian CHEN Baodi YU Yanwei QIN Sunyang WANG Xiaohui SU Xin JIN Fanyong MENG |
| author_sort | Qian CHEN |
| collection | DOAJ |
| description | Multiphase-flow measurement cannot effectively capture mesoscale dynamic structures owing to limitations of spatial and temporal resolutions of current measuring techniques. Dynamic X-ray computed tomography (CT), as a non-invasive multiphase-flow measurement technique, is promising for measuring the dynamic structures of multiphase flow. Focusing on the gas–liquid two-phase flow in multiphase flow, this paper addresses limited angle artifacts and excessive reconstruction time in mesoscale dynamic structures and proposes a U-Net-enhanced simultaneous iterative reconstruction technique (SIRT) reconstruction algorithm for bubble-structure measurements based on gas–liquid two-phase flow. Subsequently, based on the hardware design of a flowfield dynamic measurement system, which is a limited-angle dynamic X-ray CT system, a simulated gas–liquid two-phase flow dataset for training the deep-learning model is constructed from three-dimensional bubble structures obtained from hydrogel phantoms. The proposed method yields good results in the training and testing of the constructed dataset and significantly reduces the reconstruction time, thus providing a new technical approach for the high-spatiotemporal-resolution measurement of multiphase-flow mesoscale structures. |
| format | Article |
| id | doaj-art-c3f5bb6635fa43bbb70d2ea7ffc2a8e8 |
| institution | Kabale University |
| issn | 1004-4140 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj-art-c3f5bb6635fa43bbb70d2ea7ffc2a8e82025-08-20T03:53:56ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402025-05-0134341942610.15953/j.ctta.2025.0972025-097Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow MeasurementQian CHEN0Baodi YU1Yanwei QIN2Sunyang WANG3Xiaohui SU4Xin JIN5Fanyong MENG6State Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaState Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaNuray Technology Co, Ltd., Changzhou 213299, ChinaState Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaMultiphase-flow measurement cannot effectively capture mesoscale dynamic structures owing to limitations of spatial and temporal resolutions of current measuring techniques. Dynamic X-ray computed tomography (CT), as a non-invasive multiphase-flow measurement technique, is promising for measuring the dynamic structures of multiphase flow. Focusing on the gas–liquid two-phase flow in multiphase flow, this paper addresses limited angle artifacts and excessive reconstruction time in mesoscale dynamic structures and proposes a U-Net-enhanced simultaneous iterative reconstruction technique (SIRT) reconstruction algorithm for bubble-structure measurements based on gas–liquid two-phase flow. Subsequently, based on the hardware design of a flowfield dynamic measurement system, which is a limited-angle dynamic X-ray CT system, a simulated gas–liquid two-phase flow dataset for training the deep-learning model is constructed from three-dimensional bubble structures obtained from hydrogel phantoms. The proposed method yields good results in the training and testing of the constructed dataset and significantly reduces the reconstruction time, thus providing a new technical approach for the high-spatiotemporal-resolution measurement of multiphase-flow mesoscale structures.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.097deep learningdynamic ctmultiphase flow measurementlimited-anglereconstruction algorithm |
| spellingShingle | Qian CHEN Baodi YU Yanwei QIN Sunyang WANG Xiaohui SU Xin JIN Fanyong MENG Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement CT Lilun yu yingyong yanjiu deep learning dynamic ct multiphase flow measurement limited-angle reconstruction algorithm |
| title | Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement |
| title_full | Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement |
| title_fullStr | Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement |
| title_full_unstemmed | Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement |
| title_short | Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement |
| title_sort | deep learning enhanced ct reconstruction algorithm for multiphase flow measurement |
| topic | deep learning dynamic ct multiphase flow measurement limited-angle reconstruction algorithm |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.097 |
| work_keys_str_mv | AT qianchen deeplearningenhancedctreconstructionalgorithmformultiphaseflowmeasurement AT baodiyu deeplearningenhancedctreconstructionalgorithmformultiphaseflowmeasurement AT yanweiqin deeplearningenhancedctreconstructionalgorithmformultiphaseflowmeasurement AT sunyangwang deeplearningenhancedctreconstructionalgorithmformultiphaseflowmeasurement AT xiaohuisu deeplearningenhancedctreconstructionalgorithmformultiphaseflowmeasurement AT xinjin deeplearningenhancedctreconstructionalgorithmformultiphaseflowmeasurement AT fanyongmeng deeplearningenhancedctreconstructionalgorithmformultiphaseflowmeasurement |