Autoři: Jiaxi Wang aff001; Jun Liang aff003; Jingye Cheng aff004; Yumeng Guo aff005; Li Zeng aff001 Působiště autorů: Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, China aff001; Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, China aff002; College of Computer Science, Civil Aviation Flight University of China, Guanghan Sichuan, China aff003; College of Mathematics and Statistics, Chongqing University, Chongqing, China aff004; College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China aff005 Vyšlo v časopise: PLoS ONE 15(1) Kategorie: Research Article prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226963
As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle scanning, we use the limited-angle TCT scanning mode to scan an object within a limited angular range. However, this scanning mode introduces some additional noise and limited-angle artifacts that seriously degrade the imaging quality and affect the diagnosis accuracy. To reconstruct a high-quality image for the limited-angle TCT scanning mode, we develop a limited-angle TCT image reconstruction algorithm based on a U-net convolutional neural network (CNN). First, we use the SART method to the limited-angle TCT projection data, then we import the image reconstructed by SART method to a well-trained CNN which can suppress the artifacts and preserve the structures to obtain a better reconstructed image. Some simulation experiments are implemented to demonstrate the performance of the developed algorithm for the limited-angle TCT scanning mode. Compared with some state-of-the-art methods, the developed algorithm can effectively suppress the noise and the limited-angle artifacts while preserving the image structures.
Algorithms – Imaging techniques – Computed axial tomography – Data acquisition – Abdomen – Deep learning – X-ray radiography – Image processing
1. Liu FL, Yu HY, Cong W, Wang G. Top-level design and pilot analysis of low-end CT scanners based on linear scanning for developing countries. Journal of X-ray science and technology. 2014. 22(5):673–86. doi: 10.3233/XST-140453 25265926
2. Wu WW, Quan C, Liu FL. Filtered Back-Projection Image Reconstruction Algorithm for Opposite Parallel Linear CT Scanning. Acta Optica Sinica. 2016. doi: 10.3788/AOS201636.0911009
3. Kong H, Yu HY. Analytic reconstruction approach for parallel translational computed tomography. Journal of X-ray science and technology. 2015. 23(2):213. doi: 10.3233/XST-150482 25882732
4. Andersen AH, Kak AC. Simultaneous Algebraic Reconstruction Technique (SART): A superior implementation of the ART algorithm. Ultrasonic Imaging: An International Journal. 1984. 6(1):81–94. doi: 10.1016/0161-7346(84)90008-7
5. Gordon R, Bender R, Herman GT. Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography. Journal of Theoretical Biology. 1970. 29(3):471–481. doi: 10.1016/0022-5193(70)90109-8 5492997
6. Mcgaffin MG, Fessler JA. Alternating Dual Updates Algorithm for X-ray CT Reconstruction on the GPU. IEEE Transactions on Computational Imaging. 2015. 1(3):186–199. doi: 10.1109/TCI.2015.2479555 26878031
7. Chun SY, Dewaraja YK, Fessler JA. Alternating Direction Method of Multiplier for Tomography with Nonlocal Regularizers. IEEE Transactions on Medical Imaging. 2014. 33(10):1960–1968. doi: 10.1109/TMI.2014.2328660 25291351
8. Wang CX, Zeng L, Guo YM, Zhang LL. Wavelet tight frame and prior image-based image reconstruction from limited-angle projection data. Inverse Problems and Imaging. vol. 11, no. 6, pp. 917–948, 2017. doi: 10.3934/ipi.2017043
9. Wang CX, Zeng L. Error bounds and stability in the L0 regularized for CT reconstruction from small projections. Inverse Problems and Imaging. vol. 10, no. 3, pp. 829–853, 2016.
10. Wu WW, Zhang YB, Wang Q, Liu FL, Chen PJ, Yu HY. Low-dose spectral CT reconstruction using image gradient ℓ0–norm and tensor dictionary. Applied Mathematical Modelling. vol. 63, pp. 538–557, 2018. doi: 10.1016/j.apm.2018.07.006
11. Yu HY, Wang G. Compressed sensing based interior tomography. Phys. Med. Biol. vol. 54, no. 9, pp. 2791–2805, 2009. doi: 10.1088/0031-9155/54/9/014 19369711
12. Lauzier PT, Tang j, Chen GH. Prior image constrained compressed sensing: Implementation and performance evaluation. Medical Physics 39, 66–80 (2012). doi: 10.1118/1.3666946 22225276
13. Chen ZQ, Jin X, L L, Wang G. A limited-angle CT reconstruction method based on anisotropic TV minimization. Physics in Medicine & Biology. 2013. 58(7): 2119. doi: 10.1088/0031-9155/58/7/2119 23470430
14. Wang T, Nakamoto K, Zhang HY, Liu HF. Reweighted Anisotropic Total Variation Minimization for Limited-Angle CT Reconstruction. IEEE Transactions on Nuclear Science, 2017, 64(10):2742–2760. doi: 10.1109/TNS.2017.2750199
15. Yu W, Wang CX, Huang M. Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using l0-regularized gradient prior. Review of Scientific Instruments. 2017. 88(4):043703. doi: 10.1063/1.4981132 28456252
16. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. vol. 521, no. 7553, pp. 436–444, 2015. doi: 10.1038/nature14539 26017442
17. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In International Conference on Neural Information Processing Systems. Curran Associates Inc. pp. 1097–1105, 2012.
18. Girshick R, Donahue J, Darrell T, Malik J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Computer Science. pp. 580–587, 2013. doi: 10.1109/CVPR.2014.81
19. Wang G, Ye JC, Mueller K, Fessler A. Image Reconstruction Is a New Frontier of Machine Learning. IEEE Transactions on Medical Imaging PP. 99, 2018.
20. Wang G. A Perspective on Deep Imaging. IEEE Access. vol. 4, no. 99, pp. 8914–8924, 2017. doi: 10.1109/access.2016.2624938
21. Pelt DM. Batenburg KJ. Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society. vol. 22, no. 12, pp. 5238, 2013. doi: 10.1109/TIP.2013.2283142 24108463
22. Boublil D, Elad M, Shtok J, Zibulevsky M. Spatially-Adaptive Reconstruction in Computed Tomography Using Neural Networks. IEEE Transactions on Medical Imaging. vol. 34, no. 7, pp. 1474–1485, 2015. doi: 10.1109/TMI.2015.2401131 25675453
23. Chen H, Zhang Y, Zhang WH. Low-dose CT via convolutional neural network. Biomedical Optics Express. vol. 8, no. 2, pp. 679, 2017. doi: 10.1364/BOE.8.000679 28270976
24. Yang Q, Yan PK, Zhang YB, Yu HY, Shi YY, Mou XQ, et al. Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss. IEEE Transactions on Medical Imaging, 2018 : 1–1.
25. Jin KH, Mccann MT, Froustey E, Unser M. Deep Convolutional Neural Network for Inverse Problems in Imaging. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society. vol. 26, no. 9, pp. 4509–4522, 2017. doi: 10.1109/TIP.2017.2713099 28641250
26. Fuchs VR, S H Jr. Physicians’ views of the relative importance of thirty medical innovations. Health Aff. vol. 20, no. 5, pp. 30–42, 2001. doi: 10.1377/hlthaff.20.5.30 11558715
27. Natterer F. The mathematics of computerized tomography. Medical Physics. vol. 29, no. 1. pp. 107–109, 1986. doi: 10.1137/1.9780898719284
28. Gao HW, Zhang L, Chen ZQ, Xing YX, Cheng JG, Qi ZH. Direct filtered-backprojection-type reconstruction from a straight-line trajectory. Optical Engineering. vol. 46, no. 5, 2007. doi: 10.1117/1.2739624
29. Magnusson MB, Danielsson PE. Scanning of logs with linear cone-beam tomography. Computers & Electronics in Agriculture. vol. 41, no. 1, pp. 45–62, 2003. doi: 10.1016/s0168-1699(03)00041-3
30. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. vol. 9351, pp. 234–241, 2015. doi: 10.1007/978-3-319-24574-4_28
31. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In International Conference on Neural Information Processing Systems. Curran Associates Inc, 2012, pp. 1097–1105.
32. Kim J, Lee JK, Lee KM. Accurate Image Super-Resolution Using Very Deep Convolutional Networks. pp. 1646–1654, 2015. doi: 10.1109/CVPR.2016.182
33. He KM, Zhang XY, Ren SQ, Sun J. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2016, pp. 770–778.
34. Vedaldi A, Lenc K. MatConvNet: Convolutional neural networks for MATLAB. In Proc. 23rd ACM Int. Conf. Multimedia, pp. 689–692, 2012.
35. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing. 13 (2004), 600–612, doi: 10.1109/tip.2003.819861 15376593
36. Jin X, Li L, Chen ZQ, Zhang L, Xing YX. Anisotropic total variation for limited-angle CT reconstruction[C]// IEEE Nuclear Science Symposuim & Medical Imaging Conference. IEEE, 2010.
37. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, Vol. 26, no. 6, pp. 1045–1057, 2013. doi: 10.1007/s10278-013-9622-7 23884657
38. Liu Y, Ma J, Fan Y, Liang Z. Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction. Physics in Medicine & Biology. vol. 57, no. 23, pp. 7923, 2012. doi: 10.1088/0031-9155/57/23/7923 23154621
Zvýšte si kvalifikáciu online z pohodlia domova
Nemáte účet? Registrujte sa
Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.
