ISSN 1004-4140
CN 11-3017/P
| Citation: |
CHEN Q, YU B D, QIN Y W, et al. A Deep Learning Enhanced CT Reconstruction Algorithm for Multiphase Flow Measurement[J]. CT Theory and Applications, xxxx, x(x): 1-7. DOI: 10.15953/j.ctta.2025.097. (in Chinese).
|
Measurement of multiphase flow faces critical challenges in capturing mesoscale dynamic structures due to the limitation from spatial and temporal resolution of current measuring techniques. Dynamic X-ray computed tomography (CT), as a non-invasive multiphase flow measurement technique, is promising for measuring the mesoscale 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 designs a U-Net enhanced SIRT reconstruction algorithm for bubble structure measurement of gas-liquid two-phase flow. Based on the hardware designing of Flowfield Dynamic Measurement System (FDMS), 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 3D bubble structures collected from hydrogel phantoms. The proposed method achieved good results in the training and testing of the constructed dataset and significantly reduced the reconstruction time, providing a new technical approach for the efficient measurement of multiphase flow mesoscale structures.
| [1] |
GE W, CHEN F, GAO J, et al. Analytical multi-scale method for multi-phase complex systems in process engineering—Bridging reductionism and holism[J]. Chemical Engineering Science, 2007, 62(13): 3346-3377. DOI: 10.1016/j.ces.2007.02.049.
|
| [2] |
MENG F. Computed tomography in process engineering[J]. Chemical Engineering Science, 2022: 117272. DOI: 10.1016/j.ces.2021.117272.
|
| [3] |
FISCHER F, HAMPEL U. Ultra fast electron beam X-ray computed tomography for two-phase flow measurement[J]. Nuclear Engineering and Design, 2010, 240(9): 2254-2259. DOI: 10.1016/j.nucengdes.2009.11.016.
|
| [4] |
STüRZEL T, BIEBERLE M, LAURIEN E, et al. Experimental facility for two- and three-dimensional ultrafast electron beam X-ray computed tomography[J]. Review of Scientific Instruments, 2011, 82(2): 023702. DOI: 10.1063/1.3529435.
|
| [5] |
MUDDE R F. Bubbles in a fluidized bed: A fast X-ray scanner[J]. AIChE Journal, 2011, 57(10): 2684-2690. DOI: 10.1002/aic.12469.
|
| [6] |
GRAAS A B M, WAGNER E C, VAN LEEUWEN T, et al. X-ray tomography for fully-3D time-resolved reconstruction of bubbling fluidized beds[J]. Powder Technology, 2024, 434: 119269. DOI: 10.1016/j.powtec.2023.119269.
|
| [7] |
唐华平, 陈志强, 何武, 等. CNT冷阴极微焦点X射线管研制[J]. 真空电子技术, 2023(6): 23-27,44. DOI: 10.16540/j.cnki.cn11-2485/tn.2023.06.04.
TANG H P, CHEN Z Q, HE W, et al. Development of a Micro-focus X-Ray tube based on carbon nanotube cathode[J]. Vacuum Electronics, 2023(6): 23-27,44. DOI: 10.16540/j.cnki.cn11-2485/tn.2023.06.04.
|
| [8] |
KAK A C, SLANEY M. Principles of computerized tomographic imaging[M]. Society for Industrial and Applied Mathematics, 2001. DOI: 10.1137/1.9780898719277.
|
| [9] |
LEULIET T, FRIOT--GIROUX L, BAAZIZ W, et al. Efficiency of TV-regularized algorithms in computed tomography with Poisson-Gaussian noise[C]//2020 28th European Signal Processing Conference (EUSIPCO). 2021. DOI: 10.23919/eusipco47968.2020.9287762.
|
| [10] |
XU J, ZHAO Y, LI H, et al. An image reconstruction model regularized by edge-preserving diffusion and smoothing for limited-angle computed tomography[J]. Inverse Problems, 2019: 085004. DOI: 10.1088/1361-6420/ab08f9.
|
| [11] |
HUANG Y, WÜRFL T, BREININGER K, et al. Some investigations on robustness of deep learning in limited angle tomography[M]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2018, Lecture Notes in Computer Science. 2018: 145-153. DOI: 10.1007/978-3-030-00928-1_17.
|
| [12] |
TAO X, DANG Z, ZHENG Y, et al. Limited-angle artifacts removal and jitter correction in soft x-ray tomography via physical model-driven deep learning[J]. Applied Physics Letters, 2023, 123(19). DOI: 10.1063/5.0167956.
|
| [13] |
石常荣, 肖永顺, 李俊江, 等. 基于WDCT网络的混凝土CT图像增强算法[J]. CT理论与应用研究, 2021, 30(1): 1-8. DOI: 10.15953/j.1004-4140.2021.30.01.01.
SHI C R, XIAO Y S, LI J J, et al. An enhancement algorithm for concrete imaging based on WDCT network[J]. CT Theory and Applications, 2021, 30(1): 1-8. DOI: 10.15953/j.1004-4140.2021.30.01.01.
|
| [14] |
WURFL T, HOFFMANN M, CHRISTLEIN V, et al. Deep learning computed tomography: Learning projection-domain weights from image domain in limited angle problems[J]. IEEE Transactions on Medical Imaging, 2018: 1454-1463. DOI: 10.1109/tmi.2018.2833499.
|
| [15] |
WANG J, ZENG L, WANG C, et al. ADMM-based deep reconstruction for limited-angle CT[J]. Physics in Medicine & Biology, 2019: 115011. DOI: 10.1088/1361-6560/ab1aba.
|
| [16] |
黄清萍, 金鑫, 许晓飞, 等. X射线衍射技术在安检领域的研究进展[J]. CT理论与应用研究, 2023, 32(6): 843-856. DOI: 10.15953/j.ctta.2023.158.
HUANG Q P, JIN X, XU X F, et al. Research progress of X-ray diffraction technology in security inspection[J]. CT Theory and Applications, 2023, 32(6): 843-856. DOI: 10.15953/j.ctta.2023.158.
|
| [17] |
RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[M]//Navab N, Hornegger J, Wells W M, et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 9351. Cham: Springer International Publishing, 2015: 234-241. DOI: 10.1007/978-3-319-24574-4_28.
|
| [18] |
van AARLE W, PALENSTIJN W J, CANT J, et al. Fast and flexible X-ray tomography using the ASTRA toolbox. [J]. Optics Express, 2016: 25129. DOI: 10.1364/oe.24.025129.
|
| [19] |
WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. DOI: 10.1109/TIP.2003.819861.
|
Supported by: Beijing Renhe Information Technology Co. Ltd 
DownLoad: