CT Reconstruction with Priori MRI Images Through Multi-Group Datasets Expansion

doi:10.1007/s12204-017-1897-2

Abstract

Abstract: Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.

Key words: multi-group datasets expanding| computed tomography (CT)| priori magnetic regonance imaging (MRI) images

摘要： Computed tomography (CT) reconstruction with a well-registered priori magnetic resonance imaging (MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.

关键词: multi-group datasets expanding| computed tomography (CT)| priori magnetic regonance imaging (MRI) images

CLC Number:

R 318.6

WANG Qihui (王齐辉), XI Yan (奚岩), CHEN Yi (陈毅),ZHANG Weikang (张伟康), ZHAO Jun* (赵俊). CT Reconstruction with Priori MRI Images Through Multi-Group Datasets Expansion[J]. Journal of Shanghai Jiao Tong University (Science), 2017, 22(6): 756-762.

References 11

[1]	TOWNSEND D W, BEYER T, KINAHAN P E, etal. The SMART scanner: A combined PET/CT tomographfor clinical oncology [C]//Nuclear Science SymposiumConference Record. Toronto, Canada: IEEE,1998, 2: 1170-1174.
[2]	LU Y, ZHAO J, ZHUANG T G, et al. Unified dualmodalityimage reconstruction with dual dictionaries[C]//SPIE Optical Engineering+ Applications. SanDiego, USA: SPIE, 2012, 8506: 85061V-1-85061V-7.
[3]	XI Y, ZHAO J, BENNETT J, et al. Simultaneous CTMRIreconstruction for constrained imaging geometriesusing structural coupling and compressive sensing[J]. IEEE Transactions on Biomedical Engineering,2016, 63(6): 1301-1309.
[4]	ROWEOS S T, LAWRENCE K S. Nonlinear dimensionalityreduction by locally linear embedding [J]. Science,2000, 290(5500): 2323-2326.
[5]	LI L, WANG B, WANG G. Edge-oriented dualdictionaryguided enrichment (EDGE) for MRI-CTimage reconstruction [J]. Journal of X-ray Science andTechnology, 2016, 24(1): 161-175.
[6]	YANG L, ZHAO J, WANG G. Edge-guided dualmodalityimage reconstruction [J]. IEEE Access, 2014,2: 1359-1363.
[7]	GOLDSTEIN T, OSHER S. The split Bregmanmethod for L1-regularized problems [J]. SIAM Journalon Imaging Sciences, 2009, 2(2): 323-343.
[8]	RUDIN L I, OSHER S, FATEMI E. Nonlinear totalvariation based noise removal algorithms [J]. PhysicaD: Nonlinear Phenomena, 1992, 60(1): 259-268.
[9]	CHAMBOLLE A. An algorithm for total variationminimization and applications [J]. Journal of MathematicalImaging and Vision, 2004, 20(1/2): 89-97.
[10]	CLARK K, VENDT B, SMITH K, et al. The cancerimaging archive (TCIA): Maintaining and operatinga public information repository [J]. Journal of DigitalImaging, 2013, 26(6): 1045-1057.
[11]	LEVINSON N. The Wiener (root mean square) error criterion in filter design and prediction [J]. Journal ofMathematics and Physics. 1946, 25(1): 261-278.