Peter Durlak

Enhanced 3-D-reconstruction algorithm for C-arm systems suitable for interventional procedures

Increasingly, three dimensional (3-D) imaging technologies are used in medical diagnosis, for therapy planning, and during interventional procedures. The authors describe the possibilities of fast 3-D-reconstruction of high-contrast objects with high spatial resolution from only a small series of two-dimensional (2-D) planar radiographs. The special problems arising from the intended use of an open, mechanically unstable C-arm system are discussed. For the description of the irregular sampling geometry, homogeneous coordinates are used thoroughly. The well-known Feldkamp algorithm is modified to incorporate corresponding projection matrices without any decomposition into intrinsic and extrinsic parameters. Some approximations to speed up the whole reconstruction procedure and the tradeoff between image quality and computation time are also considered. Using standard hardware the reconstruction of a 256/sup 3/ cube is now possible within a few minutes, a time that is acceptable during interventions. Examples for cranial vessel imaging from some clinical test installations will be shown as well as promising results for bone imaging with a laboratory C-arm system.

Hierarchical Learning of Curves Application to Guidewire Localization in Fluoroscopy

In this paper we present a method for learning a curve model for detection and segmentation by closely integrating a hierarchical curve representation using generative and discriminative models with a hierarchical inference algorithm. We apply this method to the problem of automatic localization of the guidewire in fluoroscopic sequences. In fluoroscopic sequences, the guidewire appears as a hardly visible, non-rigid one-dimensional curve. Our paper has three main contributions. Firstly, we present a novel method to learn the complex shape and appearance of a free-form curve using a hierarchical model of curves of increasing degrees of complexity and a database of manual annotations. Secondly, we present a novel computational paradigm in the context of Marginal Space Learning, in which the algorithm is closely integrated with the hierarchical representation to obtain fast parameter inference. Thirdly, to our knowledge this is the first full system which robustly localizes the whole guidewire and has extensive validation on hundreds of frames. We present very good quantitative and qualitative results on real fluoroscopic video sequences, obtained in just one second per frame.

3D Reconstruction from Projection Matrices in a C-Arm Based 3D-Angiography System

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.

User-constrained guidewire localization in fluoroscopy

In this paper we present a learning-based guidewire localization algorithm which can be constrained by user inputs. The proposed algorithm automatically localizes guidewires in fluoroscopic images. In cases where the results are not satisfactory, the user can provide input to constrain the algorithm by clicking on the guidewire segment missed by the detection algorithm. The algorithm then re-localizes the guidewire and updates the result in less than 0.3 second. In extreme cases, more constraints can be provided until a satisfactory result is reached. The proposed algorithm can not only serve as an efficient initialization tool for guidewire tracking, it can also serve as an efficient annotation tool, either for cardiologists to mark the guidewire, or to build up a labeled database for evaluation. Through the improvement of the initialization of guidewire tracking, it also helps to improve the visibility of the guidewire during interventional procedures. Our study shows that even highly complicated guidewires can mostly be localized within 5 seconds by less than 6 clicks.

Hierarchical guidewire tracking in fluoroscopic sequences

In this paper, we present a novel hierarchical framework of guidewire tracking for image-guided interventions. Our method can automatically and robustly track a guidewire in fluoroscopy sequences during interventional procedures. The method consists of three main components: learning based guidewire segment detection, robust and fast rigid tracking, and nonrigid guidewire tracking. Each component aims to handle guidewire motion at a specific level. The learning based segment detection identifies small segments of a guidewire at the level of individual frames, and provides unique primitive features for subsequent tracking. Based on identified guidewire segments, the rigid tracking method robustly tracks the guidewire across successive frames, assuming that a major motion of guidewire is rigid, mainly caused by the breathing motion and table movement. Finally, a non-rigid tracking algorithm is applied to finely deform the guidewire to provide accurate shape. The presented guidewire tracking method has been evaluated on a test set of 47 sequences with more than 1000 frames. Quantitative evaluation demonstrates that the mean tracking error on the guidewire body is less than 2 pixels. Therefore the presented guidewire tracking method has a great potential for applications in image guided interventions.

Real time assistance for stent positioning and assessment by self-initialized tracking

Detailed visualization of stents during their positioning and deployment is critical for the success of an interventional procedure. This paper presents a novel method that relies on balloon markers to enable real-time enhanced visualization and assessment of the stent positioning and expansion, together with the blood flow over the lesion area. The key novelty is an automatic tracking framework that includes a self-initialization phase based on the Viterbi algorithm and an online tracking phase implementing the Bayesian fusion of multiple cues. The resulting motion compensation stabilizes the image of the stent and by compounding multiple frames we obtain a much better stent contrast. Robust results are obtained from more than 350 clinical data sets.