C形臂图像校正与手术定位技术研究
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摘要
基于C形臂X光机(简称C形臂)图像的手术导航是计算机辅助外科手术领域的研究热点之一。影像增强器型C形臂具有成本低、使用广泛的特点。但影像增强器会使C形臂存在不可避免的图像失真,导致C臂图像不能正确进行空间定位及导航。针对基于影像增强器型C形臂图像的手术导航中存在的问题,本文研究了C形臂投影图像的失真校正、C形臂成像模型标定及利用C形臂进行目标的空间定位等方法,对C形臂手术导航实用化具有重要的理论意义和实际价值。
论文的主要工作如下：
首先,分析C形臂图像失真成因,并模拟其失真类型。针对图像失真校正,在利用全局校正法进行校正的基础上,提出采用移动最小二乘法增加虚拟标记点来提高校正精度,初步分析了该方法的可行性；提出小孔成像投影法建立理想模型并与等距法、相似法等建立理想模型的校正结果进行了比较,结合模型原理及其实验效果,最终选取小孔成像投影法方法实现理想模型的理想化,改进了全局校正法的校正效果。
其次,为实现理想模型的建立,本文针对小孔成像投影模型设计了新型双层校准靶,针对投影图像特征,提出用形态学方法处理图像；在此基础上,分析C形臂投影图像的标记点排列规律,实现了对投影图像中不同平面标记物坐标信息的自动识别与提取,并对标记点排序,使图像点与空间点对应。最后对不同角度的C形臂投影图像进行了坐标提取实验,并与人工提取结果进行了比较,验证了提取技术的可靠性、精确性、时效性。
再次,为实现空间定位,并为后续三维重建建立基础,本文提出利用Tsai摄像机数学模型,对C形臂成像系统进行标定,以减小标定误差；并针对校正图像,提出利用归一化8点法的匹配技术,实现C形臂不同角度下投影图像中的空间点对应。最后通过利用设计的极线约束板,对其空间点进行了匹配验证,并通过测距检验其了精度。
最后,本文针对所研究的技术进行了临床实体实验,得到了较好的实现。通过狗的动物标本实验验证了校正技术的可行性；通过人脑图像实验,验证了空间定位技术的可靠性,初步达到了系统要求,并为性能优化提供了基础。
The surgical operations navigation which is based on C- arm x Ray (referred as C-arm) images is one of the hot topics within area of utilizing computers to assist in surgical operations. Though Image Intensifier c-arm is well known for its low cost and widely usage, C-arm image cannot orientate and navigate location correctly, due the unavoidable image distortion. In this paper it did researches on correcting the distortion of C-Arm X-Ray projection images, calibrating C-arm image models and how to utilize C-arm to locate a target in space etc., which are important for civilizing C-arm surgery navigation in practice from both theory and practical value's perspective.
The main tasks in this paper are as follows:
Firstly, it analyzes the type of C-arm image distortion and the causes. In order to correct the image distortion, in this paper it suggests applying Moving Least-Squares (MLS) to increase virtual makers which could eventually improve the correcting accuracy. The MLS is feasible based on a feasibility analysis that has been conducted in this research. By comparing the correcting results of ideal models created by pinhole image projection method, isometric method, similarity method etc, along with model theory and experimental effect, pinhole image projection method is finally standing out as the method, which will be employed in this research, to generate the idealization of an ideal model. The pinhole image projection method improves the effect of correcting in whole.
Then, in order to building an ideal model, in this paper it designs a new double layered correcting target based on pinhole image projection model. Additionally, complying with the features of projection images, this paper still suggests using morphological method to process images. Further, by analyzing the markers'regular pattern of C-arm projection images, auto-identification and extraction of coordinate information of different flat markers on the projection images are achieved. By sorting markers, image flat points are able to match with spatial point. Then according to coordinate extraction experiments on C-arm projection image from different angles, and the comparison of differences with the manual extraction result, this paper proofs the reliability, accuracy, and timeliness of the extraction techniques which applies in the experiments could be achieved.
Thirdly, in order to achieve the space positioning, and build foundation for the following 3D -rebuilding, this paper recommends using Tsai camera mathematical model to process C-arm image system marking, which could reduce marking errors. To correct image accurately, this paper suggests adopting 8 points normalization method, which would achieve the correspondence of spatial points of projection image from different angle of C-arm. Then, by utilizing the epipolar Constraint panel which is specially designed for this research to verify the correspondence level of the spatial points, and along with the distance measurement, this paper testifies the accuracy.
Lastly, all the techniques that have been researched in this paper are testified by clinic solid tests, which proof a good systematic achievement. It testifies the correcting technique's feasibility on animal speciums of dogs, and the reliability of the space positioning technique on human brain image experiments, which basically all reach the system's requirements and provide the foundation for further performance optimization.
论文的主要工作如下：
首先,分析C形臂图像失真成因,并模拟其失真类型。针对图像失真校正,在利用全局校正法进行校正的基础上,提出采用移动最小二乘法增加虚拟标记点来提高校正精度,初步分析了该方法的可行性；提出小孔成像投影法建立理想模型并与等距法、相似法等建立理想模型的校正结果进行了比较,结合模型原理及其实验效果,最终选取小孔成像投影法方法实现理想模型的理想化,改进了全局校正法的校正效果。
其次,为实现理想模型的建立,本文针对小孔成像投影模型设计了新型双层校准靶,针对投影图像特征,提出用形态学方法处理图像；在此基础上,分析C形臂投影图像的标记点排列规律,实现了对投影图像中不同平面标记物坐标信息的自动识别与提取,并对标记点排序,使图像点与空间点对应。最后对不同角度的C形臂投影图像进行了坐标提取实验,并与人工提取结果进行了比较,验证了提取技术的可靠性、精确性、时效性。
再次,为实现空间定位,并为后续三维重建建立基础,本文提出利用Tsai摄像机数学模型,对C形臂成像系统进行标定,以减小标定误差；并针对校正图像,提出利用归一化8点法的匹配技术,实现C形臂不同角度下投影图像中的空间点对应。最后通过利用设计的极线约束板,对其空间点进行了匹配验证,并通过测距检验其了精度。
最后,本文针对所研究的技术进行了临床实体实验,得到了较好的实现。通过狗的动物标本实验验证了校正技术的可行性；通过人脑图像实验,验证了空间定位技术的可靠性,初步达到了系统要求,并为性能优化提供了基础。
The surgical operations navigation which is based on C- arm x Ray (referred as C-arm) images is one of the hot topics within area of utilizing computers to assist in surgical operations. Though Image Intensifier c-arm is well known for its low cost and widely usage, C-arm image cannot orientate and navigate location correctly, due the unavoidable image distortion. In this paper it did researches on correcting the distortion of C-Arm X-Ray projection images, calibrating C-arm image models and how to utilize C-arm to locate a target in space etc., which are important for civilizing C-arm surgery navigation in practice from both theory and practical value's perspective.
The main tasks in this paper are as follows:
Firstly, it analyzes the type of C-arm image distortion and the causes. In order to correct the image distortion, in this paper it suggests applying Moving Least-Squares (MLS) to increase virtual makers which could eventually improve the correcting accuracy. The MLS is feasible based on a feasibility analysis that has been conducted in this research. By comparing the correcting results of ideal models created by pinhole image projection method, isometric method, similarity method etc, along with model theory and experimental effect, pinhole image projection method is finally standing out as the method, which will be employed in this research, to generate the idealization of an ideal model. The pinhole image projection method improves the effect of correcting in whole.
Then, in order to building an ideal model, in this paper it designs a new double layered correcting target based on pinhole image projection model. Additionally, complying with the features of projection images, this paper still suggests using morphological method to process images. Further, by analyzing the markers'regular pattern of C-arm projection images, auto-identification and extraction of coordinate information of different flat markers on the projection images are achieved. By sorting markers, image flat points are able to match with spatial point. Then according to coordinate extraction experiments on C-arm projection image from different angles, and the comparison of differences with the manual extraction result, this paper proofs the reliability, accuracy, and timeliness of the extraction techniques which applies in the experiments could be achieved.
Thirdly, in order to achieve the space positioning, and build foundation for the following 3D -rebuilding, this paper recommends using Tsai camera mathematical model to process C-arm image system marking, which could reduce marking errors. To correct image accurately, this paper suggests adopting 8 points normalization method, which would achieve the correspondence of spatial points of projection image from different angle of C-arm. Then, by utilizing the epipolar Constraint panel which is specially designed for this research to verify the correspondence level of the spatial points, and along with the distance measurement, this paper testifies the accuracy.
Lastly, all the techniques that have been researched in this paper are testified by clinic solid tests, which proof a good systematic achievement. It testifies the correcting technique's feasibility on animal speciums of dogs, and the reliability of the space positioning technique on human brain image experiments, which basically all reach the system's requirements and provide the foundation for further performance optimization.
引文
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[2]Yongmin k,Steven CH.Handbook of medical imaging,vol.3:Display and PACS.Bellingham, WA:SPIE Press,2000.
[3]D.P.Chakraborty.Image intensifier distortion correction Med.Phys,1987; 14:249-252
[4]Digioia AM, Simon D, Jaramaz B, et al.HipNav.a system for preoperative planning and intraoperative navigational guidance of acetabular implant placement in total hip replacement surgery.Proceedings of the 1st annual North American Symposium on Computer Assisted Orthopedic Surgery(CAOS/USA).1997:147-150
[5]Langlotz U, Lawrence J, Hu Q, et al.Image guided cup placement. Computer Assisted Radiology and Surgery(CAR'99), Paris,1999:717-721.
[6]Hsu, L Y, Loew M H. Fully automatic 3D feature-based registration of multi-modality medical images. Image and vision computing,2001,19:75-85.
[7]Rohr K. Landmark-Based Image Analysis, Using Geometric and Intensity Models.Computational imaging and vision [M], Kuwer Academic Publishers, Dordrecht, 2001.
[8]Audette M A, Peters T M. Level-set surface segmentation and registration for computing intra-surgical deformations. In Medical Imaging:Image Processing [C], K. M. Hanson (eds.), vol.3661 of Proc. SPIE, SPIE Press, Bellingham, WA,1999:110-121.
[9]Sati M, Staubli H U, Bouquin Y, et al. Real-time computerized in situ guidance system for ACL graft placement. Computer Assisted Surgery,2002,7:25-40.
[10]Klos T V, Habets R J, Banks A Z, et al. Computer assistance in arthroscopic anterior cruciate ligament reconstruction. Clin Orthop,1998:65-69.
[11]Dessenne V, Lavallee S, Julliard R, et al. Computer-assisted knee anterior cruciate ligament reconstruction:first clinical tests. Journal of Image Guided Surgery,1995,1:59-64.
[12]Joskowicz L. Fluoroscopy-based navigation in computer-aided orthopaedic surgery. Proc.of the IFAC Conference on Mechatronic Systems, Darmstadt, Germany,2000.
[13]Marwan S, Hans U, Staubli M D, et al. Clinical integration of computer-assisted technology for arthroscopic anterior cruciate ligament reconstruction. Operative techniques in orthopedics,2000,10(1):40-49.
[14]Schep N W L, Broeders I, Werken C. Computer assisted orthopaedic and trauma surgery:State of the art and future perspectives. Injury,2003,34(4):299-306.
[15]Schep NWL, VanWalsum T, Graaf J S, et al. Validation of Fluoroscopy-Based Navigation in the Hip Region:What You See Is What You Get. Computer Aided Surgery, 2002,7:279-283.
[16]Suhm N. Intraoperative Accuracy Evaluation of Virtual Fluoroscopy-A Method for Application in Computer-Assisted Distal Locking. Computer aided surgery,2001,6: 221-224.
[17]Hufner T, Pohlemann T, Tarte S, et al. Computer-assisted fracture reduction:novel method for Analysis of Accuracy, Computer Aided Surgery,2001,6:153-159.
[18]Potaminos P, Davies BL, Hibberd RD. A robotic system for minimal access surgery.Proc Inst Mech Eng.1994,208:119-126.
[19]王田苗,刘文勇,胡磊,孙磊,张利军.基于多项式拟合的C形臂投影全局校正法.高技术通讯.第17卷第9期.2007.9.
[20]Shiju Yan, Chengtao Wang, and Ming Ye. A method based on moving least squares for XRII image distortion correction. Med. Phys.34(11), November 2007
[21]P.Cerveri, C.Forlani and N. A.Borghese.Distortion correction for x-ray image intensifiers:Local unwarping polynomials and RBF neural networks. Med.Phys,2002; 29: 1759-1771.
[22]Lizhong Liu, Daniel A.Bassano, Satish C.Prasad,etal.On the use of C-arm fluoroscopy for treatment planning in high dose rate brachytherapy. Med.Phys,2003; 30(9):2297-2302.
[23]张文强.基于X线透视的手术导航关键技术的研究.博士论文,上海：上海交通大学,2004.
[24]Lizhong Liu, Daniel A.Bassano, Satish C.Prasad,etal.On the use of C-arm fluoroscopy for treatment planning in high dose rate brachytherapy. Med.Phys,2003; 30(9):2297-2302.
[25]Ruijie Rachel Liu, Stephen Rudin and Daniel R.Bednarek.Super-global distortion correction for a rotational C-arm x-ray image intensifier. Med.Phys,1999; 26(9):1802-1810.
[26]R.Fahrig, M.Moreau,and D.W.Holdsworth.Three-dimensional computed tomographic reconstruction using a C-arm mounted XRⅡ:correction of image intensifier
[27]Kato A, et al. J N euro surgery 1991; 74:845～849
[28]Reinhardt H F. N euronavigat ion:A Ten Years Review. In:Thaylo r R, eds Computer-Inte2 grated Surgery, M IT P ress (Cambridge, MA),1995
[29]Devernay F, et al. IEEE P roceedings of Computer Vision and Pat tern Recognition(CV PR'94), Seattle, USA,1994:6
[30]Tanade T. P roceeding of ARPA Image Understanding work shop 94. MonterreY CA, 1994:9
[33]Co t A, et al. IEEE Computer Graphics App li2cat ions 1996; 1:55～64
[34]会玉博.医用X射线影像增强器的原理和检测.中外医疗.2008, (21)：165-166.
[35]C Forlani, G Farrago. Automatic real-time XRⅡ local distortion correction method for digital linear tomography. Proc. of International Conference on Computational Science,2001: 23-26.
[36]P Cerveri, C Forlani, N A Borghese. Distortion correction for x-ray image intensifiers: Local unwarping polynomials and RBF neural networks. Medical Physics,2002,29(8): 1759-1771.
[37]高上凯编著.医学成像系统.北京：清华大学出版社.2000.
[38]E Gronenschild. Correction for geometric image distortion in the x-ray image chain: local technique versus global technique. Medical Physics.1999,26:2602-2616.
[39]L W Casperson, P Spiegler, J H Grollman. Characterization of aberrations in image-intensified fluoroscopy. Medical Physics,1976,3(2):103-106.
[40]S Rudin, D R Bednarek, R Wong. Accurate characterization of image intensifier distortion. Medical Physics,1991,18:1145-1151.
[41]E Gronenschild. The accuracy and reproducibility of a global method to correct for geometric image distortion in the x-ray imaging chain. Medical Physics,1997,24(12): 1875-1888.
[42]赵礼菁.脑血管DSA图像处理技术研究.北京交通大学硕士学位论文.2008.
[43]U.Solzbach, H.WollschlHger, A. Zeiher, H.Just.OPTICAL DISTORTION DUE TO GEOMAGNETISM IN QUANTITATIVE ANGIOGRAPHY.1989 IEEE
[44]Yamaguchi F. Curves and Surfaces in Computer Aided Geometric Design. Berlin: Springer,1988.
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