Registration of three-dimensional left atrial computed tomographic images with projection images obtained using fluoroscopy.

BACKGROUND: Anatomic structures such as the left atrium and the pulmonary veins (PVs) are not delineated by fluoroscopy because there is no contrast differentiation between them and the surrounding anatomy. Representation of an anatomic structure via a 3D model obtained from computed tomography (CT) imaging and subsequent projection of these images over the fluoroscopy system may help in navigation of the mapping and ablation catheter to the appropriate sites during electrophysiology procedures. METHODS AND RESULTS: In this feasibility study, in vitro experiments were performed with a plastic heart model (phantom) with 2 catheters or radiopaque platinum beads placed in the phantom at the time of CT imaging and fluoroscopy. Subsequently, 20 consecutive patients underwent contrast-enhanced, ECG-gated CT scanning. Left atrial volumes were generated from the reconstructed data at &75% of the R-R interval during the cardiac cycle. Similarly, the superior vena cava and the coronary sinus were also reconstructed from these images. During the electrophysiology procedure, digital records (cine sequences) were obtained. Using predetermined algorithms, both the phantom model and the patients' 3D left atrial models derived from the CT were registered with projection images of fluoroscopy. Registration was performed with a transformation that linked the superior vena cava and the coronary sinus from the CT model with a catheter placed inside the coronary sinus via the superior vena cava. Registration was successfully accomplished with the plastic phantom and in all 20 patients. Registration accuracy was assessed in the phantom by assessing the overlapping beads seen both in the CT and the fluoroscopy images. The mean registration error was 1.4 mm (range 0.9 to 2.3 mm). Accuracy of the registered images was assessed in patients with recordings from a basket catheter placed sequentially in the superior PVs and by injecting contrast into the PVs to assess overlapping of contrast-filled PVs with the corresponding vessels on the registered images. The images could be calibrated quite accurately. Any rotational error, which was usually minor, could be corrected by rotating the images as needed. CONCLUSIONS: Registration of 3D models of the left atrium and PVs with fluoroscopic images of the same is feasible and could enable appropriate navigation and localization of the mapping and ablation catheter during procedures such as atrial fibrillation ablation.

Step-and-shoot data acquisition and reconstruction for cardiac x-ray computed tomography.

Coronary artery imaging with x-ray computed tomography (CT) is one of the most recent advancements in CT clinical applications. Although existing "state-of-the-art" clinical protocols today utilize helical data acquisition, it suffers from the lack of ability to handle irregular heart rate and relatively high x-ray dose to patients. In this paper, we propose a step-and-shoot data acquisition protocol that significantly overcomes these shortcomings. The key to the proposed protocol is the large volume coverage (40 mm) enabled by the cone beam CT scanner, which allows the coverage of the entire heart in 3 to 4 steps. In addition, we propose a gated complementary reconstruction algorithm that overcomes the longitudinal truncation problem resulting from the cone beam geometry. Computer simulations, phantom experiments, and clinical studies were conducted to validate our approach.

Feasibility and validation of registration of three-dimensional left atrial models derived from computed tomography with a noncontact cardiac mapping system.

OBJECTIVES: The purpose of this study was to determine the feasibility and assess the validity of registering three-dimensional (3D) models from computed tomographic (CT) images using a cardiac mapping system. BACKGROUND: Registration of 3D anatomic models with an interventional system could help identify and navigate mapping and ablation catheters over a complex structure such as the left atrium (LA). METHODS: ECG-gated, contrast-enhanced cardiac CT imaging was performed in 14 patients with atrial fibrillation. Segmentation was used to create 3D models of the LA. The 3D models were registered with the mapping system using a series of fiducial points. Registration was accomplished retrospectively in the first 10 patients, and catheter navigation was visualized from recorded data. In the final four patients, registration was accomplished in real time during electrophysiologic study. The mapping catheter position, as it was navigated inside the LA, was applied to the registered model in real time. For the validation study, temporary pacing leads were implanted in the LA of 10 dogs. Following this, CT scanning, segmentation, LA model importation, and registration was described previously. After registration, a mapping catheter was positioned at the site of each buried lead according to the registered model with no fluoroscopic guidance. A radiofrequency lesion was created at this location, and the dog was sacrificed, the heart removed and stained, and the distance between the buried lead and the lesion measured. RESULTS: During the feasibility study, the location of the catheter in the registered model correlated with fluoroscopy, angiography, and intracardiac electrograms. LA endocardial potentials during sinus rhythm and any premature atrial contractions also were successfully delineated over the registered models. In the validation study, the mean target registration error was 2.0 +/- 3.6 mm. CONCLUSIONS: Registration of CT-derived 3D models of the LA using a cardiac mapping system is feasible and accurate.

Nonparametric intensity priors for level set segmentation of low contrast structures.

Segmentation of low contrast objects is an important task in clinical applications like lesion analysis and vascular wall remodeling analysis. Several solutions to low contrast segmentation that exploit high-level information have been previously proposed, such as shape priors and generative models. In this work, we incorporate a priori distributions of intensity and low-level image information into a nonparametric dissimilarity measure that defines a local indicator function for the likelihood of belonging to a foreground object. We then integrate the indicator function into a level set formulation for segmenting low contrast structures. We apply the technique to the clinical problem of positive remodeling of the vessel wall in cardiac CT angiography images. We present results on a dataset of twenty five patient scans, showing improvement over conventional gradient-based level sets.

High-definition multidetector computed tomography for evaluation of coronary artery stents: comparison to standard-definition 64-detector row computed tomography.

BACKGROUND: The assessment of coronary stents with present-generation 64-detector row computed tomography scanners that use filtered backprojection and operating at standard definition of 0.5-0.75 mm (standard definition, SDCT) is limited by imaging artifacts and noise. OBJECTIVES: We evaluated the performance of a novel, high-definition 64-slice CT scanner (HDCT), with improved spatial resolution (0.23 mm) and applied statistical iterative reconstruction (ASIR) for evaluation of coronary artery stents. METHODS: HDCT and SDCT stent imaging was performed with the use of an ex vivo phantom. HDCT was compared with SDCT with both smooth and sharp kernels for stent intraluminal diameter, intraluminal area, and image noise. Intrastent visualization was assessed with an ASIR algorithm on HDCT scans, compared with the filtered backprojection algorithms by SDCT. RESULTS: Six coronary stents (2.5, 2.5, 2.75, 3.0, 3.5, 4.0mm) were analyzed by 2 independent readers. Interobserver correlation was high for both HDCT and SDCT. HDCT yielded substantially larger luminal area visualization compared with SDCT, both for smooth (29.4+/-14.5 versus 20.1+/-13.0; P<0.001) and sharp (32.0+/-15.2 versus 25.5+/-12.0; P<0.001) kernels. Stent diameter was higher with HDCT compared with SDCT, for both smooth (1.54+/-0.59 versus1.00+/-0.50; P<0.0001) and detailed (1.47+/-0.65 versus 1.08+/-0.54; P<0.0001) kernels. With detailed kernels, HDCT scans that used algorithms showed a trend toward decreased image noise compared with SDCT-filtered backprojection algorithms. CONCLUSIONS: On the basis of this ex vivo study, HDCT provides superior detection of intrastent luminal area and diameter visualization, compared with SDCT. ASIR image reconstruction techniques for HDCT scans enhance the in-stent assessment while decreasing image noise.