Feng ZHANG, Bin YAN, Lei LI, et al. Derivative-Hilbert-Backprojection based image reconstruction from truncated projections in helical cone-beam CT. [J]. Nuclear Science and Techniques 26(2):020401(2015)
DOI:
Feng ZHANG, Bin YAN, Lei LI, et al. Derivative-Hilbert-Backprojection based image reconstruction from truncated projections in helical cone-beam CT. [J]. Nuclear Science and Techniques 26(2):020401(2015) DOI: 10.13538/j.1001-8042/nst.26.020401.
Derivative-Hilbert-Backprojection based image reconstruction from truncated projections in helical cone-beam CT
In helical cone-beam computed tomography (CT), Feldkamp-Davis-Kress (FDK) based image reconstruction algorithms are by far the most popular. However, artifacts are commonly met in the presence of lateral projection truncation. The reason is that the ramp filter is global. To restrain the truncation artifacts, an approximate reconstruction formula is proposed based on the Derivative-Hilbert-Backprojection (DHB) framework. In the method, the first order derivative filter is followed by the Hilbert transform. Since the filtered projection values are almost zero by the first order derivative filter, the following Hilbert transform has little influence on the projection values, even though the projections are laterally truncated. The proposed method has two main advantages. First, it has comparable computational efficiency and image quality as well as the conventional helical FDK algorithm for non-truncated projections. The second advantage is that images can be reconstructed with acceptable quality and much lower computational cost in comparison to the Laplace operator based algorithm in cases with truncated projections. To point out the advantages of our method, simulations on the computer and real data experiments on our laboratory industrial cone-beam CT are conducted. The simulated and experimental results demonstrate that the method is feasible for image reconstruction in the case of projection truncation.
F Zhang, B Yan, L Li, et al. Practical geometric calibration for helical cone-beam industrial computed tomography. J X-ray Sci Technol, 2014, 22: 19-35. DOI: 10.3233/XST-130406http://doi.org/10.3233/XST-130406
G Wang, T H Lin, P C Cheng, et al. A general cone-beam reconstruction algorithm. IEEE T Med Imaging, 1993, 12: 486-496. DOI: 10.1109/42.241876http://doi.org/10.1109/42.241876
A Katsevich. A general scheme for constructing inversion algorithms for cone beam CT. Int J Math Math Sci, 2003, 21: 1305-1321. DOI: 10.1155/S0161171203209315http://doi.org/10.1155/S0161171203209315
Y Zou, X Pan and E Y Sidky. Theory and algorithms for image reconstruction on chords and within regions of interest. J Opt Soc Am A, 2005, 22: 2372-2384. DOI: 10.1364/JOSAA.22.002372http://doi.org/10.1364/JOSAA.22.002372
F Natterer. The Mathematics of Computerized Tomography. American Society for Industrial & Applied Mathematics Press, 2001, 121-125. DOI: 10.1137/1.9780898719284http://doi.org/10.1137/1.9780898719284
K S Sharma, C Holzner, D M Vasilescu, et al. Scout-view assisted interior micro-CT. Phys Med Biol, 2013, 58: 4297-4314. DOI: 10.1088/0031-9155/58/12/4297http://doi.org/10.1088/0031-9155/58/12/4297
H Y Yu and G Wang. Compressed sensing based interior tomography. Phys Med Biol, 2009, 54: 2791-2805. DOI: 10.1155/2009/125871http://doi.org/10.1155/2009/125871
J Yang, H Yu, M Jiang, et al. High order total variation minimization for interior tomography. Inverse Probl, 2010, 26: 035013. DOI: 10.1088/0266-5611/26/3/035013http://doi.org/10.1088/0266-5611/26/3/035013
X Jin, A Katsevich, H Yu, et al. Interior tomography with continuous singular value decomposition. IEEE T Med Imaging, 2012, 31: 2108-2119. DOI: 10.1109/TMI.2012.2213304http://doi.org/10.1109/TMI.2012.2213304
F Dennerlein. Cone-beam ROI reconstruction using the Laplace operator. 11th international meeting on Fully Three-dimensional image reconstruction in Radiology and Nuclear Medicine, 2011, 80-83.
Y Xia, A Maier, F Dennerlein, et al. Efficient 2D filtering for cone-beam VOI reconstruction, Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Anaheim, CA, October 27, 2012, 2415-2420. DOI: 10.1109/NSSMIC.2012.6551549http://doi.org/10.1109/NSSMIC.2012.6551549
F Dennerlein and A Maier. Region-of-interest reconstruction on medical C-arms with the ATRACT algorithm, SPIE Medical Imaging 2012: Physics of Medical Imaging, San Diego, CA, February 2012, 83131B. DOI: 10.1117/12.913274http://doi.org/10.1117/12.913274
Y Xia, A Maier, H G Hofmann, et al. Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering, SPIE Medical Imaging 2013: Physics of Medical Imaging, Lake Buena Vista, FL, February 2013, 86681C. DOI: 10.1117/12.2007484http://doi.org/10.1117/12.2007484
F Dennerlein and A Maier. Approximate truncation robust computed tomography-ATRACT. Phys Med Biol, 2013, 58: 6133-6148. DOI: 10.1088/0031-9155/58/17/6133http://doi.org/10.1088/0031-9155/58/17/6133
Y Xia, H Hofmann, F Dennerlein, et al. Towards clinical application of a Laplace Operator-based region of interest reconstruction algorithm in C-Arm CT. IEEE T Med Imaging, 2014, 33: 593-606. DOI: 10.1109/TMI.2013.2291622http://doi.org/10.1109/TMI.2013.2291622
L A Feldkamp, L C Davis and J W Kress. Practical cone-beam algorithm. J Opt Soc Am A, 1984, 1: 612-619. DOI: 10.1364/JOSAA.1.000612http://doi.org/10.1364/JOSAA.1.000612
M Grass, T Köhler and R Proksa. 3D cone-beam CT reconstruction for circular trajectories. Phys Med Biol, 2000, 45: 329-347. DOI: 10.1088/0031-9155/45/2/306http://doi.org/10.1088/0031-9155/45/2/306
X B Zou, H Yu and L Zeng. Laplace operator based reconstruction algorithm for truncated spiral cone beam computed tomography. J X-ray Sci Technol, 2013, 21: 515-526. DOI: 10.3233/XST-130398http://doi.org/10.3233/XST-130398
X C Wang, B Yan, L Li, et al. Cone-beam local reconstruction based on a Radon inversion transformation. Chinese Phys B, 2012, 21: 8702. DOI: 10.1088/1674-1056/21/11/118702http://doi.org/10.1088/1674-1056/21/11/118702
P A Toft. The Radon transform, theory and implementation. Ph.D. Thesis, Technical University of Denmark, 1996.
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