冠狀動脈血管攝影術是一種為了輔助心導管手術，以
X光拍攝心血管的一種成像技
術，手術 過程中醫生會拍攝大量 X光片，通常兩張正交 (AP、 LA方向 )的二維影像，透
過兩張影像的位置關係以及經驗判斷，來確定 導管在每個時候的位置是否正確，有時為
了得到能清楚顯示血管結構的影像，必須要多次拍攝得到適當的影像。
為了能提供醫師更直觀的
血管流向 資訊，並減少 X光的拍攝，在本研究中希望能建
立一套以 兩張二 維 X光 影像重建三維心臟血管模型的演算法，包含影像的前處理、取得
兩張影像的對 應 點的方法 最後計 算出三維空間座標。影像前處理的部分，目的是為了將
血管影像從背景中分割出來，其中包含去除雜訊、 不均勻光線校正 與 影像強化 對影像
中血管區域細線化找出血管中心線， 再使用 中心線 分岔點 與 基於 對極幾何 的方法 找出對
應點， 最後計 算出三維模型在空間中的位 置。 重建出的三維模型可提供心血管每個分支
的走向與長度，協助醫師 了解 導管 需 前進的方向和距離 ，降低原本手術過程中須不斷拍
攝 X光影像確認導管位置的需求 。
在驗證重建方法實驗中，使用
2D C-arm拍攝自製的心血管假體，獲得兩張 X光影
像進行重建，並 以 手持驗證工具驗證重建出的空間位置的正確性，重建位置誤差約為
1.84mm，標準差 0.74mm。

Coronary angiography is an imaging technique that uses X-rays to photograph the cardiovascular system to assist in cardiac catheterization. During the operation, the doctor will take a large number of X-ray images, which are usually in two directions ( AP/LA direction). Through the positional relationship of the two images and empirical judgments, it is determined whether the catheter position is correct at each time. Sometimes in order to obtain an image that clearly shows the structure of the blood vessel, it is necessary to take multiple shots to obtain an appropriate image.
To provide physicians with more intuitive blood vessel flow information and reduce X-ray shooting, in this study, we hope to establish an algorithm for reconstructing a three-dimensional heart vessel model from two two-dimensional X-ray images. The algorithm includes the pre-processing of the image, the method to obtain the corresponding points of the two images, and finally calculate the three-dimensional space coordinates. The purpose of the image pre-processing part is to segment the blood vessel image from the background, including noise removal, uneven light calibration, and enhancement filtering; Finding the centerlines of the vessel by image skeleton then can help to find the bifurcation points of the centerlines. Then calculate the corresponding point based on the epipolar geometry and finally measure the position of the three-dimensional model in space. In the experiment of verifying the reconstruction method, 2D C-arm was used to shoot the self-made cardiovascular prosthesis, two X-ray images were obtained for reconstruction, and the verification tool was held to verify the correctness of the reconstructed spatial position. The reconstruction position error is about 1.84mm, and the standard deviation is 0.74mm.

[1] 衛生福利部 . "108年國人死因統計結果 ." https://www.mohw.gov.tw/cp-16-54482-1.html
[2] S. Hosseinian and H. Arefi, "3d Reconstruction from Multi-View Medical X-Ray Images – Review and Evaluation of Existing Methods," ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-1/W5, pp. 319-326, 2015, doi: 10.5194/isprsarchives-XL-1-W5-319-2015.
[3] 陳怡君 . "腦部磁振造影血管攝影 ." https://www1.cgmh.org.tw/branch/lnk/2016/article2.aspx?id=221
[4] J. A. Maintz and M. A. Viergever, "An overview of medical image registration methods," in Symposium of the Belgian hospital physicists association (SBPH/BVZF), 1996, vol. 12, no. V: Citeseer, pp. 1-22.
[5] J. Wang and T. J. Blackburn, "The AAPM/RSNA Physics Tutorial for Residents," RadioGraphics, vol. 20, no. 5, pp. 1471-1477, 2000, doi: 10.1148/radiographics.20.5.g00se181471.
[6] 吳吉春 , "基於 C-arm影像的手術導引定位 ," 機械工程學系 , 國立中央大學 , 2012年
[7] C. Kirbas and F. Quek, "A review of vessel extraction techniques and algorithms," ACM Computing Surveys (CSUR), vol. 36, no. 2, pp. 81-121, 2004.
[8] K. A. S. H. Kulathilake, "Improvement of coronary angiography for quantitative coronary analysis by using a computer vision technique," 2017.
[9] S. Yang et al., "Deep learning segmentation of major vessels in X-ray coronary angiography," Sci Rep, vol. 9, no. 1, p. 16897, Nov 15 2019, doi: 10.1038/s41598-019-53254-7.
[10] A. Ajam, A. A. Aziz, V. S. Asirvadam, A. S. Muda, I. Faye, and S. J. Safdar Gardezi, "A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning," IEEE Access, vol. 5, pp. 15222-15240, 2017, doi: 10.1109/access.2017.2718590.
[11] S. J. Chen and J. D. Carroll, "3-D reconstruction of coronary arterial tree to optimize angiographic visualization," IEEE transactions on medical imaging, vol. 19, no. 4, pp. 318-336, 2000.
[12] C. Pellot, A. Herment, M. Sigelle, P. Horain, H. Maître, and P. Peronneau, "A 3D reconstruction of vascular structures from two X-ray angiograms using an adapted simulated annealing algorithm," IEEE transactions on medical imaging, vol. 13, no. 1, pp. 48-60, 1994.
[13] A. Zifan, M. Gavaises, P. Liatsis, I.Pantos, "A new method of three-dimensional
45
coronary artery reconstruction from X-ray angiography: validation against a virtual phantom and multislice computed tomography," Catheter Cardiovasc Interv, vol. 71, no. 1, pp. 28-43, Jan 1 2008, doi: 10.1002/ccd.21414.
[14] W. Schreiner, F. Neumann, M. Neumann, A. End, S. M. Roedler, and S. Aharinejad, "The influence of optimization target selection on the structure of arterial tree models generated by constrained constructive optimization," Journal of General Physiology, vol. 106, no. 4, pp. 583-599, 1995, doi: 10.1085/jgp.106.4.583.
[15] J. Alastruey, K. H. Parker, J. Peiró, S. M. Byrd, and S. J. Sherwin, "Modelling the circle of Willis to assess the effects of anatomical variations and occlusions on cerebral flows," Journal of Biomechanics, vol. 40, no. 8, pp. 1794-1805, 2007, doi: 10.1016/j.jbiomech.2006.07.008.
[16] P. Perdikaris, L. Grinberg, and G. E. Karniadakis, "Multiscale modeling and simulation of brain blood flow," Physics of Fluids, vol. 28, no. 2, p. 021304, 2016, doi: 10.1063/1.4941315.
[17] K. Kolev and D. Cremers, "Integration of Multiview Stereo and Silhouettes Via Convex Functionals on Convex Domains," in Computer Vision – ECCV 2008, (Lecture Notes in Computer Science, 2008, ch. Chapter 57, pp. 752-765.
[18] T. Zhang and C. Y. Suen, "A fast parallel algorithm for thinning digital patterns," Communications of the ACM, vol. 27, no. 3, pp. 236-239, 1984.
[19] R. Bansal, P. Sehgal, and P. Bedi, "Minutiae extraction from fingerprint images-a review," arXiv preprint arXiv:1201.1422, 2011.
[20] J. H. Challis, "Quaternions as a solution to determining the angular kinematics of human movement," BMC Biomedical Engineering, vol. 2, no. 1, 2020, doi: 10.1186/s42490-020-00039-z.
[21] Z. Zhang, "Determining the Epipolar Geometry and its Uncertainty: A Review," International Journal of Computer Vision, vol. 27, no. 2, pp. 161-195, 1998, doi: 10.1023/a:1007941100561.
[22] A. M. Aibinu, M. I. Iqbal, M. Nilsson, and M.-J. E. Salami, "A new method of correcting uneven illumination problem in fundus image," 2007.
[23] M. D. Vlachos and E. S. Dermatas, "Non-uniform illumination correction in infrared images based on a modified fuzzy c-means algorithm," Journal of Biomedical Graphics and Computing, vol. 3, no. 1, 2012, doi: 10.5430/jbgc.v3n1p6.
[24] J. Zhu, B. Liu, and S. C. Schwartz, "General illumination correction and its application to face normalization," in 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). 2003, vol. 3: IEEE, pp. III-133.
[25] M. Agarwal and R. Mahajan, "Medical Images Contrast Enhancement using Quad Weighted Histogram Equalization with Adaptive Gama Correction and Homomorphic Filtering," Procedia Computer Science, vol. 115, pp. 509-517, 2017,
46
doi: 10.1016/j.procs.2017.09.107.
[26] A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, "Multiscale vessel enhancement filtering," in International conference on medical image computing and computer-assisted intervention, 1998: Springer, pp. 130-137.
[27] P. Anupama and S. Nandyal, "Blood vessel segmentation using hessian matrix for diabetic retinopathy detection," in 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2017: IEEE, pp. 1-5.