Rapid 2D-3D Medical Image Registration Based on CUDA_Journal of Biomedical Engineering

Authors：

LILingzhi ^1,2 ,  ZOUBeiji ^1,2

1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. Mobile Health Ministry of Education-China Mobile Joint Laboratory, Central South University, Changsha 410083, China;

Corresponding author：

ZOUBeiji, Email: bjzou@vip.163.com

Keywords：

image registration; dissimilar dimensionality; mix character; GPU acceleration

DOI：

10.7507/1001-5515.20140170

Video：

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The medical image registration between preoperative three-dimensional (3D) scan data and intraoperative two-dimensional (2D) image is a key technology in the surgical navigation. Most previous methods need to generate 2D digitally reconstructed radiographs (DRR) images from the 3D scan volume data, then use conventional image similarity function for comparison. This procedure includes a large amount of calculation and is difficult to archive real-time processing. In this paper, with using geometric feature and image density mixed characteristics, we proposed a new similarity measure function for fast 2D-3D registration of preoperative CT and intraoperative X-ray images. This algorithm is easy to implement, and the calculation process is very short, while the resulting registration accuracy can meet the clinical use. In addition, the entire calculation process is very suitable for highly parallel numerical calculation by using the algorithm based on CUDA hardware acceleration to satisfy the requirement of real-time application in surgery.

Citation： LILingzhi, ZOUBeiji. Rapid 2D-3D Medical Image Registration Based on CUDA. Journal of Biomedical Engineering, 2014, 31(4): 905-909. doi: 10.7507/1001-5515.20140170 Copy

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8.	RUSSAKOFF D B, ROHLFING T, HO A, et al. Evaluation of intensity-based 2-D-3-D spine image registration using clinical gold-standard data[M]//GEE J C, ANTOINE MAINTZ J B, VANNIER M W. Biomedical image registration. Berlin Heidelberg: Springer Berlin Heidelberg, 2003, 2717: 151-160.
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11.	GALVIN J M, SIMS C, DOMINIAK G, et al. The use of digitally reconstructed radiographs for three-dimensional treatment planning and CT-simulation[J]. Int J Radiat Oncol Biol Phys, 1995, 31(4): 935-942.
12.	梁玮,鲍旭东,罗立民.基于互信息的2D-3D医学图像配准[J].生物医学工程研究,2004,23(1):14-16.
13.	张薇,黄毓瑜,栾胜,等.基于灰度的二维/三维图像配准方法及其在骨科导航手术中的实现[J].中国医学影像技术,2007,23(7):1080-1084.
14.	DORGHAM O, FISHER M, LAYCOCK S. Performance of a 2D-3D image registration system using (lossy) compressed X-ray CT[J]. Ann BMVA,2008,2008(3):1-11.
15.	ROHLFING T, RUSSAKOFF D B, DENZLER J, et al. Progressive attenuation fields: fast 2D-3D image registration without precomputation[J]. Med Phys, 2005, 32(9): 2870-2880.
16.	徐建,秦安,卜祥磊,等.基于CUDA的2D-3D配准技术的研究[J].计算机工程与应用, 2010, 46(11): 56-59.

1. TOMAZEVIC D, LIKAR B, PERNUS F. 3-D/2-D registration by integrating 2-D information in 3-D[J]. IEEE Trans Med Imaging, 2006, 25(1): 17-27.
2. GUÉZIEC A, KAZANZIDES P, WILLIAMSON B, et al. Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot[J]. IEEE Trans Med Imaging, 1998, 17(5):715-728.
3. FELDMAR J, AYACHE N, BETTING F. 3D-2D projective registration of free-form curves and surfaces[J]. Comput Vis Image Underst, 1997, 65(3): 403-424.
4. GUÉZIEC A, WU K, KALVIN A, et al. Providing visual information to validate 2-D to 3-D registration[J]. Med Image Anal, 2000, 4(4): 357-374.
5. HAMADEH A, LAVALLEE S, CINQUIN P. Automated 3-dimensional computed tomographic and fluoroscopic image registration[J]. Comput Aided Surg, 1998, 3(1): 11-19.
6. LEMIEUX L, JAGOE R, FISH D R, et al. A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs[J]. Med Phys, 1994, 21(11): 1749-1760.
7. PENNEY G P, WEESE J, LITTLE J A, et al. A comparison of similarity measures for use in 2-D-3-D medical image registration[J]. IEEE Trans Med Imaging, 1998, 17(4): 586-595.
8. RUSSAKOFF D B, ROHLFING T, HO A, et al. Evaluation of intensity-based 2-D-3-D spine image registration using clinical gold-standard data[M]//GEE J C, ANTOINE MAINTZ J B, VANNIER M W. Biomedical image registration. Berlin Heidelberg: Springer Berlin Heidelberg, 2003, 2717: 151-160.
9. KHAMENE A, BLOCH P, WEIN W, et al. Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy[J]. Med Image Anal, 2006, 10(1): 96-112.
10. SHEROUSE G W, NOVINS K, CHANEY E L. Computation of digitally reconstructed radiographs for use in radiotherapy treatment design[J]. Int J Radiat Oncol Biol Phys, 1990, 18(3): 651-658.
11. GALVIN J M, SIMS C, DOMINIAK G, et al. The use of digitally reconstructed radiographs for three-dimensional treatment planning and CT-simulation[J]. Int J Radiat Oncol Biol Phys, 1995, 31(4): 935-942.
12. 梁玮,鲍旭东,罗立民.基于互信息的2D-3D医学图像配准[J].生物医学工程研究,2004,23(1):14-16.
13. 张薇,黄毓瑜,栾胜,等.基于灰度的二维/三维图像配准方法及其在骨科导航手术中的实现[J].中国医学影像技术,2007,23(7):1080-1084.
14. DORGHAM O, FISHER M, LAYCOCK S. Performance of a 2D-3D image registration system using (lossy) compressed X-ray CT[J]. Ann BMVA,2008,2008(3):1-11.
15. ROHLFING T, RUSSAKOFF D B, DENZLER J, et al. Progressive attenuation fields: fast 2D-3D image registration without precomputation[J]. Med Phys, 2005, 32(9): 2870-2880.
16. 徐建,秦安,卜祥磊,等.基于CUDA的2D-3D配准技术的研究[J].计算机工程与应用, 2010, 46(11): 56-59.

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