3D reconstruction from projection matrices in a C-arm based 3D-angiography system

被引:0
作者
Navab, N
Bani-Hashemi, A
Nadar, MS
Wiesent, K
Durlak, P
Brunner, T
Barth, K
Graumann, R
机构
[1] Siemens Corp Res Inc, Princeton, NJ 08540 USA
[2] Siemens AG, Med Engn Grp, D-8520 Erlangen, Germany
来源
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI'98 | 1998年 / 1496卷
关键词
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
3D reconstruction of arterial vessels from planar radiographs obtained at several angles around the object has gained increasing interest. The motivating application has been interventional angiography. In order to obtain a three-dimensional reconstruction from a C-arm mounted X-Ray Image Intensifier (XRII) traditionally the trajectory of the source and the detector system is characterized and the pixel size is estimated. The main use of the imaging geometry characterization is to provide a correct 3D-2D mapping between the 3D voxels to be reconstructed and the 2D pixels on the radiographic images. We propose using projection matrices directly in a voxel driven backprojection for the reconstruction as opposed to that of computing all the geometrical parameters, including the imaging parameters. We discuss the simplicity of the entire calibration-reconstruction process, and the fact that it makes the computation of the pixel size, source to detector distance, and other explicit imaging parameters unnecessary. A usual step in the reconstruction is sinogram weighting, in which the projections containing corresponding data from opposing directions have to be weighted before they are filtered and backprojected into the object space. The rotation angle of the C-arm is used in the sinogram weighting. This means that the C-arm motion parameters must be computed from projection matrices. The numerical instability associated with the decomposition of the projection matrices into intrinsic and extrinsic parameters is discussed in the context. The paper then describes our method of computing motion parameters without matrix decomposition. Examples of the calibration results and the associated volume reconstruction are also shown.
引用
收藏
页码:119 / 129
页数:11
相关论文
共 16 条
[1]  
[Anonymous], 1993, Three-Dimensional Computer Vision: A Geometric Viewpoint
[2]  
CLACK R, 1994, P SOC PHOTO-OPT INS, V2299, P230, DOI 10.1117/12.179253
[3]   Three-dimensional computed tomographic reconstruction using a C-arm mounted XRII: Correction of image intensifier distortion [J].
Fahrig, R ;
Moreau, M ;
Holdsworth, DW .
MEDICAL PHYSICS, 1997, 24 (07) :1097-1106
[4]   Characterization of a C-arm mounted XRII for 3D image reconstruction during interventional neuroradiology [J].
Fahrig, R ;
Fox, AJ ;
Holdsworth, DW .
PHYSICS OF MEDICAL IMAGING: MEDICAL IMAGING 1996, 1996, 2708 :351-360
[5]  
Faugeras O. D., 1986, Proceedings CVPR '86: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.86CH2290-5), P15
[6]   PRACTICAL CONE-BEAM ALGORITHM [J].
FELDKAMP, LA ;
DAVIS, LC ;
KRESS, JW .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 1984, 1 (06) :612-619
[7]  
GANAPATHY S, 1984, P INT C ROBOTICS, P130
[8]   The accuracy and reproducibility of a global method to correct for geometric image distortion in the x-ray imaging chain [J].
Gronenschild, E .
MEDICAL PHYSICS, 1997, 24 (12) :1875-1888
[9]   Object pose: The link between weak perspective, paraperspective, and full perspective [J].
Horaud, R ;
Dornaika, F ;
Lamiroy, B ;
Christy, S .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 1997, 22 (02) :173-189
[10]  
KOPPE R, 1995, COMPUTER ASSISTED RADIOLOGY, P101