[1]周正东,管绍林,涂佳丽,等.基于图像加权的多能光子计数X线CT全能谱图像重建改进方法[J].东南大学学报(自然科学版),2017,47(5):892-896.[doi:10.3969/j.issn.1001-0505.2017.05.009]
　Zhou Zhengdong,Guan Shaolin,Tu Jiali,et al.Improved image-based weighting method for full spectral image reconstruction of multi-energy photon counting X-ray CT[J].Journal of Southeast University (Natural Science Edition),2017,47(5):892-896.[doi:10.3969/j.issn.1001-0505.2017.05.009]
点击复制

基于图像加权的多能光子计数X线CT全能谱图像重建改进方法()
分享到：

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第5期
页码:
892-896
栏目:
计算机科学与工程
出版日期:
2017-09-20

文章信息/Info

Title:
Improved image-based weighting method for full spectral image reconstruction of multi-energy photon counting X-ray CT
作者:
周正东; 管绍林; 涂佳丽; 李剑波; 张雯雯
南京航空航天大学核科学与工程系, 南京 210016
Author(s):
Zhou Zhengdong;  Guan Shaolin;  Tu Jiali;  Li Jianbo;  Zhang Wenwen
Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
关键词:
光子计数X线CT;  基于图像加权;  最大后验概率;  对比噪声比
Keywords:
photon counting X-ray computed tomography(CT);  image-based weighting;  maximum a posteriori probability(MAP);  contrast-to-noise ratio(CNR)
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2017.05.009
摘要:
为了提高基于图像加权的多能光子计数X线CT全能谱重建图像的质量,提出了一种图像重建改进方法.首先,对每个能量段的投影数据采用最大后验概率(MAP)统计重建算法进行图像重建;然后,对各能量段图像进行优化加权求和,获得全能谱图像.仿真实验结果表明,MAP统计重建算法可显著提高全能谱重建图像的对比噪声比(CNR).与滤波反投影算法相比,当能量段数分别为2,4,6,9时,MAP统计重建算法使得钙图像的CNR分别提高659.7%,643.4%,621.2%,586.1%,碘图像的CNR分别提高663.8%,648.6%,635.1%,600.9%,软组织图像的CNR分别提高596.2%,638.5%,592.6%,596.3%;与能量积分法相比,当能量段数分别为2,4,6,9时,MAP统计重建算法使得钙图像的CNR分别提高43.3%,49.1%,49.3%,44.5%,碘图像的CNR分别提高43.2%,45.7%,45.7%,40.2%,软组织图像的CNR分别提高21.5%,28.9%,25.5%,26.2%.
Abstract:
To improve the full spectral reconstruction image quality for the image-based weighting multi-energy photon counting X-ray computed tomography(CT), an improved image reconstruction method was proposed. First, the maximum a posteriori probability(MAP)statistical reconstruction algorithm was used to reconstruct the image in each energy bin. Then, the images from each energy bin were summarized with optimal weights to obtain the full spectral image. The simulation experimental results demonstrate that the MAP statistical reconstruction algorithm can significantly improve the contrast-to-noise ratio(CNR)of the full spectral reconstruction image. Compared with the filtered back projection(FBP)algorithm, for the cases with the energy spectrum split into 2, 4, 6 and 9 bins, the MAP statistical reconstruction algorithm can offer the CNR improvement up to 659.7%, 643.4%, 621.2% and 586.1%for calcium,663.8%, 648.6%, 635.1% and 600.9% for iodine, 596.2%, 638.5%,592.6% and 596.3% for soft tissue, respectively.Compared with the energy-integrating method,for the energy-resolved cases with 2, 4, 6 and 9 energy bins, the MAP statistical reconstruction algorithm can offer the CNR improvement up to 43.3%, 49.1%, 49.3% and 44.5% for calcium, 43.2%, 45.7%, 45.7% and 40.2% for iodine, 21.5%, 28.9%, 25.5% and 26.2% for soft tissue,respectively.

参考文献/References:

[1] Sossin A, Rebuffel V, Tabary J, et al. A novel scatter separation method for multi-energy X-ray imaging[J]. Physics in Medicine & Biology, 2016, 61(12):4711-4728. DOI:10.1088/0031-9155/61/12/4711.
[2] Lee S W, Choi Y N, Cho H M, et al. A Monte Carlo simulation study of the effect of energy windows in computed tomography images based on an energy-resolved photon counting detector[J]. Physics in Medicine & Biology, 2012, 57(15):4931-4949. DOI:10.1088/0031-9155/57/15/4931.
[3] Schmidt T G. CT energy weighting in the presence of scatter and limited energy resolution[J]. Medical Physics, 2010, 37(3):1056-1067. DOI:10.1118/1.3301615.
[4] Schmidt T G. Optimal “image-based” weighting for energy-resolved CT[J]. Medical Physics, 2009, 36(7):3018-3027. DOI:10.1118/1.3148535.
[5] Kalluri K S, Mahd M, Glick S J. Investigation of energy weighting using an energy discriminating photon counting detector for breast CT[J]. Medical Physics, 2013, 40(8):081923. DOI:10.1118/1.4813901.
[6] Berglund J, Johansson H, Maack H I, et al. Energy weighting improves the image quality of spectral mammograms: Implementation on a photon-counting mammography system[C]//SPIE Medical Imaging. San Diego, CA, USA, 2014:90331. DOI:10.1117/12.2042238.
[7] Choi Y N, Lee Y J, Kim H J. The effect of energy weighting on X-ray imaging based on photon counting detector: A Monte Carlo simulation[J]. Proceeding of SPIE, 2012, 8313(2):193. DOI:10.1117/12.910845.
[8] Rupcich F, Gilat-Schmidt T. Experimental study of optimal energy weighting in energy-resolved CT using a CZT detector[C]//SPIE Medical Imaging. Lake Buena Vista, Florida, USA, 2013:303-311. DOI:10.1117/12.2008439.
[9] Lee Y, Lee S, Kim H J. Comparison of spectral CT imaging methods based a photon-counting detector: Experimental study[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2016, 815: 68-74. DOI:10.1016/j.nima.2016.01.069.
[10] Ansari A, Danyali H, Helfroush M S. HS remote sensing image restoration using fusion with MS images by EM algorithm[J]. IET Signal Processing, 2017, 11(1): 95-103. DOI:10.1049/iet-spr.2016.0141.
[11] Helin T, Burger M. Maximum a posteriori probability estimates in infinite-dimensional Bayesian inverse problems[J]. Inverse Problems, 2015, 31(8): 085009. DOI:10.1088/0266-5611/31/8/085009.
[12] Punnoose J, Xu J, Sisniega A, et al. Technical note: Spektr 3.0—A computational tool for X-ray spectrum modeling and analysis[J]. Medical Physics, 2016, 43(8):4711-4717. DOI:10.1118/1.4955438.
[13] Caldeira L, Scheins J J, Almeida P, et al. Maximum a posteriori reconstruction using PRESTO and PET/MR data acquired simultaneously with the 3TMR-BrainPET[J]. Nuclear Science Symposium Conference Record, 2010, 612(3):2879-2884. DOI:10.1109/nssmic.2010.5874322.
[14] Chen Y, Ma J, Feng Q, et al. Nonlocal prior Bayesian tomographic reconstruction[J]. Journal of Mathematical Imaging and Vision, 2008, 30(2):133-146. DOI:10.1007/s10851-007-0042-5.
[15] 刘祎, 桂志国, 张权, 等. 一种基于指数型先验分布的正电子发射断层图像重建算法[J]. 中国组织工程研究与临床康复, 2010, 14(52): 9760-9763. DOI:10.3969/j.issn.1673-8225.2010.52.018.
Liu Yi, Gui Zhiguo, Zhang Quan, et al. Positron emission tomography image reconstruction algorithm based on an exponential Markov random field prior model[J]. Journal of Clinical Rehabilitative Tissue Engineering Research, 2010, 14(52): 9760-9763. DOI:10.3969/j.issn.1673-8225.2010.52.018. (in Chinese)

备注/Memo

备注/Memo:
收稿日期: 2017-04-02.
作者简介: 周正东(1969—),男,博士,副教授,zzd_msc@nuaa.edu.cn.
基金项目: 国家自然科学基金资助项目(51575256)、中央高校基本科研业务费专项资金资助项目(NZ2016102).
引用本文: 周正东,管绍林,涂佳丽,等.基于图像加权的多能光子计数X线CT全能谱图像重建改进方法[J].东南大学学报(自然科学版),2017,47(5):892-896. DOI:10.3969/j.issn.1001-0505.2017.05.009.
更新日期/Last Update: 2017-09-20