Registration of 3D Angiographic and X-Ray Images Using Sequential Monte Carlo Sampling. In: Liu Y, Jiang T, Zhang C, editors. Computer Vision for Biomedical Image Applications. Berlin: Springer Berlin Heidelberg; 2005. pp. 427-36. [DOI: 10.1007/11569541_43]

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

PURPOSE

Three-dimensional to two-dimensional (3D-2D) image registration is a key to fusion and simultaneous visualization of valuable information contained in 3D pre-interventional and 2D intra-interventional images with the final goal of image guidance of a procedure. In this paper, the authors focus on 3D-2D image registration within the context of intracranial endovascular image-guided interventions (EIGIs), where the 3D and 2D images are generally acquired with the same C-arm system. The accuracy and robustness of any 3D-2D registration method, to be used in a clinical setting, is influenced by (1) the method itself, (2) uncertainty of initial pose of the 3D image from which registration starts, (3) uncertainty of C-arm's geometry and pose, and (4) the number of 2D intra-interventional images used for registration, which is generally one and at most two. The study of these influences requires rigorous and objective validation of any 3D-2D registration method against a highly accurate reference or "gold standard" registration, performed on clinical image datasets acquired in the context of the intervention.

METHODS

The registration process is split into two sequential, i.e., initial and final, registration stages. The initial stage is either machine-based or template matching. The latter aims to reduce possibly large in-plane translation errors by matching a projection of the 3D vessel model and 2D image. In the final registration stage, four state-of-the-art intrinsic image-based 3D-2D registration methods, which involve simultaneous refinement of rigid-body and C-arm parameters, are evaluated. For objective validation, the authors acquired an image database of 15 patients undergoing cerebral EIGI, for which accurate gold standard registrations were established by fiducial marker coregistration.

RESULTS

Based on target registration error, the obtained success rates of 3D to a single 2D image registration after initial machine-based and template matching and final registration involving C-arm calibration were 36%, 73%, and 93%, respectively, while registration accuracy of 0.59 mm was the best after final registration. By compensating in-plane translation errors by initial template matching, the success rates achieved after the final stage improved consistently for all methods, especially if C-arm calibration was performed simultaneously with the 3D-2D image registration.

CONCLUSIONS

Because the tested methods perform simultaneous C-arm calibration and 3D-2D registration based solely on anatomical information, they have a high potential for automation and thus for an immediate integration into current interventional workflow. One of the authors' main contributions is also comprehensive and representative validation performed under realistic conditions as encountered during cerebral EIGI.

Stochastic 3D motion compensation of coronary arteries from monoplane angiograms

Image-based navigation during percutaneous coronary interventions is highly challenging since it involves estimating the 3D motion of a complex topology using 2D angiographic views. A static coronary tree segmented in a pre-operative CT-scan can be overlaid on top of the angiographic frames to outline the coronary vessels, but this overlay does not account for coronary motion, which has to be mentally compensated by the cardiologist. In this paper, we propose a new approach to the motion estimation problem, where the temporal evolution of the coronary deformation over the cardiac cycle is modeled as a stochastic process. The sequence of angiographic frames is interpreted as a probabilistic evidence of the succession of unknown deformation states, which can be optimized using particle filtering. Iterative and non-rigid registration is performed in a projective manner, and relies on a feature-based similarity measure. Experiments show promising results in terms of registration accuracy, learning capability and computation time.

An adaptive Monte Carlo approach to phase-based multimodal image registration

In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.

Deformable 2D-3D registration of vascular structures in a one view scenario

Alignment of angiographic 3D scans to 2D projections is an important issue for 3D depth perception and navigation during interventions. Currently, in a setting where only one 2D projection is available, methods employing a rigid transformation model present the state of the art for this problem. In this work, we introduce a method capable of deformably registering 3D vessel structures to a respective single projection of the scene. Our approach addresses the inherent ill-posedness of the problem by incorporating a priori knowledge about the vessel structures into the formulation. We minimize the distance between the 2D points and corresponding projected 3D points together with regularization terms encoding the properties of length preservation of vessel structures and smoothness of deformation. We demonstrate the performance and accuracy of the proposed method by quantitative tests on synthetic examples as well as real angiographic scenes.

Vesselness-based 2D-3D registration of the coronary arteries

PURPOSE

Robust and accurate automated co-registration of the coronary arteries in 3D CTA and 2D X-ray angiography during percutaneous coronary interventions (PCI), in order to present a fused visualization.

METHODS

A novel vesselness-based similarity measure was developed, that avoids an explicit segmentation of the X-ray image. A stochastic optimizer searches the optimal registration using the similarity measure.

RESULTS

Both simulated data and clinical data were used to investigate the accuracy and capture range of the proposed method. The experiments show that the proposed method outperforms the iterative closest point method in terms of accuracy (average residual error of 0.42 mm vs. 1.44 mm) and capture range (average 71.1 mm/20.3 degrees vs. 14.1 mm/5.2 degrees ).

CONCLUSION

The proposed method has proven to be accurate and the capture range is ample for usage in PCI. Especially the absence of an explicit segmentation of the interventionally acquired X-ray images considerably aids the robustness of the method.

Evaluation of a stochastic approach to 2D-3D intensity-based registration

this paper assesses a new 2D-3D rigid registration method based on stochastic clustering and an enhanced estimator for mutual information. It combines precision, accuracy and acceptable computation time. Spine datasets (fluoroscopy and computed tomography) with their gold standard transformations are used. Both optimization method and similarity measure are assessed separately using standardized evaluation methodologies. Sub-millimeter accuracy and the high convergence rate obtained within one minute are compared to other quasi-global optimization processes such as particle filtering.

New CTA protocol and 2D-3D registration method for liver catheterization

2D-3D registration for angiographic liver interventions is an unsolved problem mainly because of two reasons. First, a suitable protocol for Computed Tomography Angiography (CTA) to contrast liver arteries is not used in clinical practice. Second, an adequate registration algorithm which addresses the difficult task of aligning deformed vessel structures has not been developed yet. We address the first issue by introducing an angiographic CT scanning phase and thus create a strong link between radiologists and interventionalists. The scan visualizes arteries similar to the vasculature captured with an intraoperative C-arm acquiring Digitally Subtracted Angiograms (DSAs). Furthermore, we propose a registration algorithm using the new CT phase that aligns arterial structures in two steps: (a) Initialization of one corresponding feature using vessel diameter information, (b) optimization on three rotational and one translational parameter to register vessel structures that are represented as centerline graphs. We form a space of good features by iteratively creating new graphs from projected centerline images and by restricting the correspondence search only on branching points (the vertices) of the vessel tree. This algorithm shows good convergence and proves to be robust against deformation changes, which is demonstrated through studies on one phantom and three patients.