Technical and clinical study of x-ray-based surface echo probe tracking using an attached fiducial apparatus.

PURPOSE: Several types of structural heart intervention (SHI) use information from multiple imaging modalities to complete an interventional task. For example, in transcatheter aortic valve replacement (TAVR), placement and deployment of a bioprosthetic aortic valve in the aorta is primarily guided by x-ray fluoroscopy (XRF), and echocardiography provides visualization of cardiac anatomy and blood flow. However, simultaneous interpretation of independent x-ray and echo displays remains a challenge for the interventionalist. The purpose of this work was to develop a novel echo/x-ray co-registration solution in which volumetric transthoracic echo (TTE) is transformed to the x-ray coordinate system by tracking the three-dimensional (3D) pose of a probe fiducial attachment from its appearance in two-dimensional (2D) x-ray images. METHODS: A fiducial attachment for a commercial TTE probe consisting of rings of high-contrast ball bearings was designed and fabricated. The 3D pose (position and orientation) of the fiducial attachment is estimated from a 2D x-ray image using an algorithm in which a virtual point cloud model of the attachment is iteratively rotated, translated, and forward-projected onto the image until the average sum-of-squares of grayscale values at the projected points is minimized. Fiducial registration error (FRE) and target registration error (TRE) of this approach were evaluated in phantom studies using TAVR-relevant gantry orientations and four standard acoustic windows for the TTE probe. A patient study was conducted to assess the clinical suitability of the fiducial attachment prototype during TTE imaging of patients undergoing SHI. TTE image quality for the task of guiding a transcatheter procedure was evaluated in a reviewer study. RESULTS: The 3D FRE ranged from 0.32 ± 0.03 mm (mean ± SD) to 1.31 ± 0.05 mm, depending on C-arm orientation and probe acoustic window. The 3D TRE ranged from 1.06 ± 0.03 mm to 2.42 ± 0.06 mm. Fiducial pose estimation was stable when >75% of the fiducial markers were visible in the x-ray image. A panel of reviewers graded the presentation of heart valves in TTE images from 48 SHI patients. While valve presentation did not differ significantly between acoustic windows (P > 0.05), the mitral valve did achieve a significantly higher image quality compared to the aortic and tricuspid valves (P < 0.001). Overall, reviewers perceived sufficient image quality in 76.5% of images of the mitral valve, 54.9% of images of the aortic valve, and 48.6% of images of the tricuspid valve. CONCLUSIONS: Fiducial-based tracking of a commercial TTE probe is compatible with clinical SHI workflows and yields 3D target registration error of less than 2.5 mm for a variety of x-ray gantry geometries and echo probe acoustic windows. Although TTE image quality with respect to target valve anatomy was sufficient for the majority of cases examined, prescreening of patients for sufficient TTE quality would be helpful.

A dynamic model-based approach to motion and deformation tracking of prosthetic valves from biplane x-ray images.

PURPOSE: Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure in which a prosthetic heart valve is placed and expanded within a defective aortic valve. The device placement is commonly performed using two-dimensional (2D) fluoroscopic imaging. Within this work, we propose a novel technique to track the motion and deformation of the prosthetic valve in three dimensions based on biplane fluoroscopic image sequences. METHODS: The tracking approach uses a parameterized point cloud model of the valve stent which can undergo rigid three-dimensional (3D) transformation and different modes of expansion. Rigid elements of the model are individually rotated and translated in three dimensions to approximate the motions of the stent. Tracking is performed using an iterative 2D-3D registration procedure which estimates the model parameters by minimizing the mean-squared image values at the positions of the forward-projected model points. Additionally, an initialization technique is proposed, which locates clusters of salient features to determine the initial position and orientation of the model. RESULTS: The proposed algorithms were evaluated based on simulations using a digital 4D CT phantom as well as experimentally acquired images of a prosthetic valve inside a chest phantom with anatomical background features. The target registration error was 0.12 ± 0.04 mm in the simulations and 0.64 ± 0.09 mm in the experimental data. CONCLUSIONS: The proposed algorithm could be used to generate 3D visualization of the prosthetic valve from two projections. In combination with soft-tissue sensitive-imaging techniques like transesophageal echocardiography, this technique could enable 3D image guidance during TAVR procedures.

Automated 3D coronary sinus catheter detection using a scanning-beam digital x-ray system.

Scanning-beam digital x-ray (SBDX) is an inverse geometry x-ray fluoroscopy system capable of tomosynthesis-based 3D tracking of catheter electrodes concurrent with fluoroscopic display. To facilitate respiratory motion-compensated 3D catheter tracking, an automated coronary sinus (CS) catheter detection algorithm for SBDX was developed. The technique uses the 3D localization capability of SBDX and prior knowledge of the catheter shape. Candidate groups of points representing the CS catheter are obtained from a 3D shape-constrained search. A cost function is then minimized over the groups to select the most probable CS catheter candidate. The algorithm was implemented in MATLAB and tested offline using recorded image sequences of a chest phantom containing a CS catheter, ablation catheter, and fiducial clutter. Fiducial placement was varied to create challenging detection scenarios. Table panning and elevation was used to simulate motion. The CS catheter detection method had 98.1% true positive rate and 100% true negative rate in 2755 frames of imaging. Average processing time was 12.7 ms/frame on a PC with a 3.4 GHz CPU and 8 GB memory. Motion compensation based on 3D CS catheter tracking was demonstrated in a moving chest phantom with a fixed CS catheter and an ablation catheter pulled along a fixed trajectory. The RMS error in the tracked ablation catheter trajectory was 1.41 mm, versus 10.35 mm without motion compensation. A computationally efficient method of automated 3D CS catheter detection has been developed to assist with motion-compensated 3D catheter tracking and registration of 3D cardiac models to tracked catheters.