Evaluation of 3D-2D Registration Methods for Registration of 3D-DSA and 2D-DSA Cerebral Images

被引:1
|
作者
Mitrovic, Uros [1 ]
Spiclin, Ziga [1 ]
Likar, Bostjan [1 ]
Pernus, Franjo [1 ]
机构
[1] Univ Ljubljana, Fac Elect Engn, Lab Imaging Technol, Ljubljana 1000, Slovenia
来源
关键词
3D-2D image registration; cerebral angiograms; DSA; performance evaluation; gold standard; STANDARDIZED EVALUATION METHODOLOGY; X-RAY IMAGES; 3D/2D REGISTRATION; 2-D-3-D REGISTRATION; 3-D/2-D REGISTRATION; 3D; ANGIOGRAPHY; CT; INFORMATION; FIDUCIALS;
D O I
10.1117/12.2007009
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Recent C-arm systems used for endovascular image-guided interventions enable the acquisition of three-dimensional (3D) and dynamic two-dimensional (2D+t) images in the same interventional suite. The 3D images are used to observe the vascular morphology while the 2D+t images show the current state of the intervention. By spatial alignment of 3D and 2D+t images one can facilitate the endovascular interventions, e.g. by displaying the intra-interventional tools and contrast-agent flow in the augmented 3D+t images. To achieve the spatial alignment several 3D-2D registration methods were proposed that are concerned with finding the rigid-body parameters of the 3D image. Meanwhile, the pose of the C-arm system is usually obtained through a dedicated C-arm calibration. In practice, the calibrated C-arm pose parameters are typically valid only if the imaged object is positioned in the C-arm's isocenter. To compensate this, the 3D-2D registration should search simultaneously for the rigid-body as well as the C-arm pose parameters. For verification, we tested three 3D-2D registration methods on real, clinical 3D and 2D+t angiographic images of twenty patients, ten of which were imaged with attached fiducial markers to obtain a "gold standard" registration. The results indicate that, compared to searching solely the rigid-body parameters, by searching simultaneously for rigid-body and the C-arm pose parameters significantly improves the accuracy and success rate of 3D-2D registration methods. Among the three tested methods the intensity-based method using mutual information was the most robust, as it successfully registered all clinical datasets, and highly accurate, as the maximal fiducial registration error was less or equal than 0 34 mm.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] 3D-2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms
    Mitrovic, Uros
    Likar, Bostjan
    Pernus, Franjo
    Spiclin, Ziga
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2018, 13 (02) : 193 - 202
  • [22] 3D-2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation
    Otake, Yoshito
    Wang, Adam S.
    Uneri, Ali
    Kleinszig, Gerhard
    Vogt, Sebastian
    Aygun, Nafi
    Lo, Sheng-fu L.
    Wolinsky, Jean-Paul
    Gokaslan, Ziya L.
    Siewerdsen, Jeffrey H.
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (05): : 2075 - 2090
  • [23] Correspondenceless 3D-2D Registration Based on Expectation Conditional Maximization
    Kang, X.
    Taylor, R. H.
    Armand, M.
    Otake, Y.
    Yau, W. P.
    Cheung, P. Y. S.
    Hu, Y.
    MEDICAL IMAGING 2011: VISUALIZATION, IMAGE-GUIDED PROCEDURES, AND MODELING, 2011, 7964
  • [24] 3D-2D registration for surgical guidance: effect of projection view angles on registration accuracy
    Uneri, A.
    Otake, Y.
    Wang, A. S.
    Kleinszig, G.
    Vogt, S.
    Khanna, A. J.
    Siewerdsen, J. H.
    PHYSICS IN MEDICINE AND BIOLOGY, 2014, 59 (02): : 271 - 287
  • [25] Segmentation of X-ray Images by 3D-2D Registration Based on Multibody Physics
    Schmid, Jerome
    Chenes, Christophe
    COMPUTER VISION - ACCV 2014, PT II, 2015, 9004 : 674 - 687
  • [26] 2D-3D vascular registration between digital subtraction angiographic (DSA) and magnetic resonance angiographic (MRA) images
    Chan, HM
    Chung, ACS
    Yu, SCH
    Wells, WM
    2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 and 2, 2004, : 708 - 711
  • [27] Automated 3D-2D projective registration of human facial images using edge features
    Haider, AM
    Kaneko, T
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2001, 15 (08) : 1263 - 1276
  • [28] Evaluation of low-dose limits in 3D-2D rigid registration for surgical guidance
    Uneri, A.
    Wang, A. S.
    Otake, Y.
    Kleinszig, G.
    Vogt, S.
    Khanna, A. J.
    Gallia, G. L.
    Gokaslan, Z. L.
    Siewerdsen, J. H.
    PHYSICS IN MEDICINE AND BIOLOGY, 2014, 59 (18): : 5329 - 5345
  • [29] Standardized evaluation of 2D-3D registration
    van de Kraats, EB
    Penney, GP
    Tomazevic, D
    van Walsum, T
    Niessen, WJ
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2004, PT 1, PROCEEDINGS, 2004, 3216 : 574 - 581
  • [30] Evaluation of similarity measures for 3D-2D image registration based on matching structure tensors
    Špiclin, Žiga
    Elektrotehniski Vestnik/Electrotechnical Review, 2015, 82 (1-2): : 66 - 72