A practical and robust method for beam blocker-based cone beam CT scatter correction

被引:4
作者
Cui, Hehe [1 ]
Jiang, Xiao [1 ]
Tang, Wei [2 ]
Lu, Hsiao-Ming [2 ]
Yang, Yidong [3 ,4 ,5 ]
机构
[1] Univ Sci & Technol China, Dept Engn & Appl Phys, Hefei 230026, Anhui, Peoples R China
[2] Univ Sci & Technol China, Affiliated Hosp USTC 1, Hefei Ion Med Ctr, Div Life Sci & Med, Hefei 231283, Anhui, Peoples R China
[3] Univ Sci & Technol China, Affiliated Hosp USTC 1, Dept Radiat Oncol, Div Life Sci & Med, Hefei 230001, Anhui, Peoples R China
[4] Univ Sci & Technol China, Sch Phys Sci, Hefei 230026, Anhui, Peoples R China
[5] Univ Sci & Technol China, Ion Med Res Inst, Hefei 230026, Anhui, Peoples R China
关键词
beam blocker; scatter correction; cone beam CT; X-RAY SCATTER; COMPUTED-TOMOGRAPHY; RADIATION-THERAPY; SHADING CORRECTION; EFFICIENT; QUALITY;
D O I
10.1088/1361-6560/acb2aa
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective. In the traditional beam-blocker based cone beam CT (CBCT) scatter correction, the scatter measured in the region shaded by lead strips was multiplied by a correction factor to directly represent the scatter in the unblocked region. The correction factor optimization is a tedious process and lacks an objective stop criterion. To skip the optimization process, an indirect scatter estimation method was developed and validated in phantom imaging. Approach. A beam-blocker made of lead strips was mounted between the x-ray source and object for scatter estimation. The primary signal between lead strips in the blocked region was first calculated by subtracting the measured scatter, and then used to calculate the scatter signal in the unblocked region corresponding to the same attenuation path. The calculated scatter signal was smoothed via local filtration and used to correct the measured projection in the unblocked region. Finally, the CBCT was reconstructed via Feldkamp-Davis-Kress algorithm. A Catphan and a head phantom were used to verify the performance of the proposed method in both full-and half-blocker scenarios, and with and without a bow-tie filter. Main Results. For scans without the bow-tie filter, the CT number error was reduced to 3.97 +/- 2.27 and 5.51 +/- 3.90 HU in the full-and half-blocker scenarios, respectively, for the Catphan, and to 4.01 +/- 2.18 and 7.97 +/- 4.05 HU for the head phantom. When the bow-tie filter was applied, the CT number error was reduced to 2.29 +/- 1.42 and 6.72 +/- 0.77 HU in the full-and half-blocker scenarios, respectively, for the Catphan, and 2.35 +/- 1.25 and 4.96 +/- 1.89 HU for the head phantom. Significance. The proposed method effectively avoids the influence of the inserted beam blocker itself on the scatter intensity estimation, and proves a more practical and robust way for the beam-blocker based scatter correction in CBCT scanning.
引用
收藏
页数:12
相关论文
共 31 条
[1]   The optimal balance between quality and efficiency in proton radiography imaging technique at various proton beam energies: A Monte Carlo study [J].
Biegun, A. K. ;
van Goethem, M-J. ;
van der Graaf, E. R. ;
van Beuzekom, M. ;
Koffeman, E. N. ;
Nakaji, T. ;
Takatsu, J. ;
Visser, J. ;
Brandenburg, S. .
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2017, 41 :141-146
[2]   Efficient scatter distribution estimation and correction in CBCT using concurrent Monte Carlo fitting [J].
Bootsma, G. J. ;
Verhaegen, F. ;
Jaffray, D. A. .
MEDICAL PHYSICS, 2015, 42 (01) :54-68
[3]   Optimization of the geometry and speed of a moving blocker system for cone-beam computed tomography scatter correction [J].
Chen, Xi ;
Ouyang, Luo ;
Yan, Hao ;
Jia, Xun ;
Li, Bin ;
Lyu, Qingwen ;
Zhang, You ;
Wang, Jing .
MEDICAL PHYSICS, 2017, 44 (09) :E215-E229
[4]   Planning CT-guided robust and fast cone-beam CT scatter correction using a local filtration technique [J].
Cui, Hehe ;
Jiang, Xiao ;
Fang, Chengyijue ;
Zhu, Lei ;
Yang, Yidong .
MEDICAL PHYSICS, 2021, 48 (11) :6832-6843
[5]   ScatterNet: A convolutional neural network for cone-beam CT intensity correction [J].
Hansen, David C. ;
Landry, Guillaume ;
Kamp, Florian ;
Li, Minglun ;
Belka, Claus ;
Parodi, Katia ;
Kurz, Christopher .
MEDICAL PHYSICS, 2018, 45 (11) :4916-4926
[6]   Flat-panel cone-beam computed tomography for image-guided radiation therapy [J].
Jaffray, DA ;
Siewerdsen, JH ;
Wong, JW ;
Martinez, AA .
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2002, 53 (05) :1337-1349
[7]   A GPU tool for efficient, accurate, and realistic simulation of cone beam CT projections [J].
Jia, Xun ;
Yan, Hao ;
Cervino, Laura ;
Folkerts, Michael ;
Jiang, Steve B. .
MEDICAL PHYSICS, 2012, 39 (12) :7368-7378
[8]   Scatter correction of cone-beam CT using a deep residual convolution neural network (DRCNN) [J].
Jiang, Yangkang ;
Yang, Chunlin ;
Yang, Pengfei ;
Hu, Xi ;
Luo, Chen ;
Xue, Yi ;
Xu, Lei ;
Hu, Xiuhua ;
Zhang, Luhan ;
Wang, Jing ;
Sheng, Ke ;
Niu, Tianye .
PHYSICS IN MEDICINE AND BIOLOGY, 2019, 64 (14)
[9]   Combining scatter reduction and correction to improve image quality in cone-beam computed tomography (CBCT) [J].
Jin, Jian-Yue ;
Ren, Lei ;
Liu, Qiang ;
Kim, Jinkoo ;
Wen, Ning ;
Guan, Huaiqun ;
Movsas, Benjamin ;
Chetty, Indrin J. .
MEDICAL PHYSICS, 2010, 37 (11) :5634-5644
[10]   Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network [J].
Kida, Satoshi ;
Nakamoto, Takahiro ;
Nakano, Masahiro ;
Nawa, Kanabu ;
Haga, Akihiro ;
Kotoku, Jun'ichi ;
Yamashita, Hideomi ;
Nakagawa, Keiichi .
CUREUS, 2018, 10 (04)