Evaluation of 3D-2D registration methods for registration of 3D-DSA and 2D-DSA cerebral images

Abstract

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 Carm 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.

Simultaneous 3D–2D image registration and C‐arm calibration: Application to endovascular image‐guided interventions

“…We show on these datasets that machine-based registration relying solely on DICOM-accessible C-arm parameters leads to a rather poor alignment of 3D and 2D images. Based on our previous work, 39 we propose to improve the machine-based registration by performing C-arm calibration simultaneously with intrinsic image-based 3D-2D registration. We hypothesize that by carefully addressing the problem of large initial alignment errors this approach may lead to more accurate and robust alignment of 3D and 2D images.…”

“…The quality of 3D-2D registration visualized by overlapping the 3D image projected onto 2D and the live 2D image is much less affected by rotations and out-of-plane translation errors than by in-plane translations errors. 11,12,39 The in-plane translations are determined for each view P WP by projecting translations t x ,t y ,t z in S W onto the axes of S D (Fig. 1); then, the obtained 3-vector (t u ,t v ,t w ) consists of the in-plane (t u ,t v ) and out-ofplane t w translations.…”