北京生物醫(yī)學工程

基于DRR及相似性測度的2D-3D醫(yī)學圖像配準算法

2D/3D medical image registration algorithm based on DRR and the similarity measure

作者：麥永鋒孫啟昌賈鵬飛陳曉軍

單位：上海交通大學機械與動力工程學院（上海 200240）上海交通大學數字醫(yī)學臨床轉化教育部工程研究中心（上海 200240）上海交通大學醫(yī)療機器人研究院（上海 200240）

關鍵詞： 2D/3D配準；數字重建放射影像；相似性測度；Powell算法；歸一化互相關；梯度差分

分類號：R318.04

出版年·卷·期（頁碼）：2021·40·3（263-272）

摘要：

目的針對術前三維電子計算機斷層掃描(computed tomography,CT)圖像和術中二維X線圖像的配準問題提出了精確高效的全自動配準算法，使得該算法能夠適應不同X線圖像的風格。方法首先通過數字重建放射影像(digitally reconstructured radiograph， DRR)技術，把CT圖像轉換成DRR圖像，從而將CT和X線圖像的配準轉換成DRR圖像和X線圖像的配準。然后在傳統的歸一化互相關 (normalized cross correlation，NCC)和梯度差分(gradiant difference，GD)相似性測度指標基礎上提出融合NCC和GD的歸一化互相關-梯度差分(normalized cross correlation - gradient difference，NG)指標，并基于NG指標計算DRR圖像和X線圖像的相似性。通過Powell算法迭代求取相似性測度的極值，從而獲得配準矩陣。最后在人體頭顱CT數據上采用“黃金標準”判斷法量化配準系統的精度，并通過脊柱CT和X線配準案例驗證系統在實際場景的性能。結果基于DRR及NG相似性測度的2D/3D圖像配準系統的配準距離誤差為0.51 mm，角度誤差為0.40°。結論基于DRR及NG相似性測度的2D/3D圖像配準算法具有較好的配準精度，能適應不同風格的二維輸入圖像，基于該算法的配準系統具有較好的魯棒性。

Objective To accurately, efficiently, and automatically register the preoperative computed tomography（CT） image and the inoperative X-ray image in order to improve the adaptability to different X-ray image styles. Methods CT was firstly converted into digital reconstruction radiography （DRR) images through the digital reconstruction radiography technology. The registration of CT and X-ray was converted into the registration of DRR and X-ray images. Then, based on the traditional similarity measures, normalized cross correlation（NCC） and gradient difference（GD）, normalized cross correlation - gradient difference（NG） was proposed to calculate the similarity between DRR and X-ray. Powell algorithm was used to get the extremum value of the similarity metric. The registration matrix was obtained after the optimization process. At last，using the CT image of the skull, the “Gold standard” judgment method was used to quantitatively evaluate the registration algorithm. The spine CT and X-ray registration case was also applied to qualitatively evaluate the performance of the registration system in practical application. Results The registration distance errors of the angle and position of the 2D/3D medical image registration algorithm based on DRR and the NG similarity measure were 0.40°and 0.51mm. Conclusions The 2D / 3D image registration algorithm based on DRR and the NG similarity measure can improve the accuracy and adapt to different image styles. The registration system based on the algorithm is robust.

參考文獻：

[1]?張薇, 黃毓瑜, 欒勝, 等. 基于灰度的二維/三維圖像配準方法及其在骨科導航手術中的實現[J]. 中國醫(yī)學影像技術, 2007, 23(7): 1080-1084.?

Zhang W, Huang SY, Luan S, et al.?Implementation of intensity-based 2D/3D image registration in orthopaedic navigation system[J]. Chinese journal of medical imaging technology, 2007,23(7):1080-1084.

[2]?Qin C , Cao Z , Fan S , et al. An oral and maxillofacial navigation system for implant placement with automatic identification of fiducial points[J]. International Journal of Computer Assisted Radiology and Surgery, 2019.

[3]?張冉,王雷,夏威,高欣.2D/3D圖像配準中的相似性測度和優(yōu)化算法[J].激光與紅外,2014,44(01):98-102.?

Zhang R,Wang L,Xia W,et al.Comparison of similarity measurement and optimization methods in 2D/3D image registration[J]. Laser&Infrared,2014,44(1):98-102.

[4]?李言, 武王將, 楊博鑫, 等. 基于2D-3D配準的術中腓骨旋轉不良檢測方法[J]. 北京生物醫(yī)學工程, 2019, 38(1):56-62.

Li Y, Wu WJ, Yang BX, et al. Method of detectingintraoperative fibular malrotation based on 2D-3D registration technology?[J].?Beijing Biomedical Engineering,?2019,?38(1):52－58．

[5]?Wang M,Song Z. Automatic localization of the center of fiducial markers in 3D CT/MRI images for image-guided neurosurgery[J]. Pattern Recognition Letters, 2009, 30 (4): 414-420.

[6]?張建法,張峰峰,孫立寧, 等.基于C形臂的手術導航相機標定方法[J].計算機應用,2016,36(8):2327-2331.

Zhang?JF,?Zhang?FF,?Sun?LN,?et al. Camera calibration method of surgical navigation based on C-arm[J]. ?Journal of Computer Applications,?2016,36(8):2327-2331.

[7]?Yu W , Tannast M , Zheng G . Non-rigid free-form 2D-3D registration using A B-spline-based statistical deformation model[J]. Pattern Recognition, 2016:S003132031630293X.

[8]?劉坤, 呂曉琪, 谷宇, 等. 快速數字影像重建的2維/3維醫(yī)學圖像配準[J]. 中國圖象圖形學報, 2016, 21(1): 69-77.

Liu?K,?Lyu XQ,?Gu Y,?et al． The 2D/3D medical image registration algorithm based on rapid digital image reconstruction[J].?Journal of image and graphics,?2016,?21(1): 69-77.

[9]?劉達, 謝永召, 倪自強, 等. 基于局部方差采樣的2D/3D醫(yī)學圖像配準技術[J]. 高技術通訊, 2013, 23(8): 827-832.

Liu D,?Xie YZ,?Ni?ZQ,?et al.?2D/3D medical image registration based on local variance sampling[J]. Chinese high technology letters,?2013,?23(8): 827-832.

[10]?陳龍, 張峰峰, 于凌濤, 等. 歸一化互信息與多分辨率融合的2D-3D配準方法[J]. 哈爾濱工程大學學報, 2020, 41(2):243-249.

Chen?L,?Zhang?FF,?Yu LT,?et al. A 2D-3D registration method based on normalized mutual information and multi-resolution fusion[J]. Journal of Harbin Engineering University,?2019,?41(2): 243-249．

[11]?Pengfei Jia, Lu Xu, Xiaojun Chen, et al. An intensity-based rigid registration of 2D C-Arm and 3D CT images for image-assisted navigation system. Proceedings of the 31st International Conference on Computer Assisted Radiology and Surgery (CARS 2017), June, 2017.

[12]?Tornai?GJ, Cserey G , Pappas I . Fast DRR generation for 2D to 3D registration on GPUs[J]. Medical Physics, 2012, 39(8).

[13]?Toth D , Panayiotou M , Brost A , et al. 3D/2D Registration with Superabundant Vessel Reconstruction for Cardiac Resynchronization Therapy[J]. Medical Image Analysis, 2017, 42:160.

[14]?Mu Z. A Fast DRR Generation Scheme for 3D-2D Image Registration Based on the Block Projection Method[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2016: 169-177.

[15]?Folkerts M , Jia X , Choi D , et al. SU-E-I-35: A GPU Optimized DRR Algorithm[J]. Medical Physics, 2011, 38(6):3403.

[16]?Cong W, Liang X, Dong J, et al. Accurate CT-ultrasound image registration using simulated transformation optimization[C] //Eleventh International Conference on Digital Image Processing (ICDIP 2019). International Society for Optics and Photonics, 2019, 11179: 111790V.

[17]?ébastien Clippe S, Sarrut D, Malet C, et al. Patient setup error measurement using 3D intensity-based image registration techniques[J]. International Journal of Radiation Oncology* Biology* Physics, 2003, 56(1): 259-265.

[18]?Kim J, Fessler J A, Lam K L, et al. A feasibility study of mutual information based setup error estimation for radiotherapy[J]. Medical Physics, 2001, 28(12): 2507-2517.

[19]?Alam F, Rahman S U, Ullah S, et al. Medical image registration in image guided surgery: Issues, challenges and research opportunities[J]. Biocybernetics and Biomedical Engineering, 2018, 38(1): 71-89.

[20]?蘇長慧, 夏桂梅, 屈向紅. 基于Powell算法的改進微粒群算法[J]. 寧夏師范學院學報, 2013(06):70-73.

Su CH, Xia GM, Qu XH. Improved Particle Swarm Optimization Algorithm Based on the Powell Algorithm. Journal of Ningxia Normal University, 2013(06):70-73.

[21]?楊帆,張汗靈. 蟻群算法和Powell法結合的多分辨率三維圖像配準[J]. 電子與信息學報, 2007, 29 (3): 622-625.

Yang F, Zhang HL. Multiresolution 3D Image Registration Using Hybrid Ant Colony Algorithm and Powell's Method[J]. Journal of Electronics & Information Technology, 2007, 29 (3): 622-625.

[22]?Flood PDL , Banks SA . Automated Registration of 3-D Knee Implant Models to Fluoroscopic Images Using Lipschitzian Optimization[J]. IEEE Transactions on Medical Imaging, 2018, 37(1):326-335.

[23]?Li?GH, Gao?JY, Kafka OL, et al. Implementation and application of the multiresolution continuum theory[J]. Computational mechanics, 2019, 63(4): 631-647.

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