A pilot study on magnetic navigation for transcatheter aortic valve implantation using dynamic aortic model and US image guidance

The effects of positioning of transcatheter aortic valves on fluid dynamics of the aortic root

Transcatheter aortic valve implantation is a novel treatment for severe aortic valve stenosis. Due to the recent use of this technology and the procedural variability, there is very little data that quantify the hemodynamic consequences of variations in valve placement. Changes in aortic wall stresses and fluid retention in the sinuses of Valsalva can have a significant effect on the clinical response a patient has to the procedure. By comprehensively characterizing complex flow in the sinuses of Valsalva using digital particle image velocimetry and an advanced heart-flow simulator, various positions of a deployed transcatheter valve with respect to a bioprosthetic aortic valve (valve-in-valve) were tested in vitro. Displacements of the transcatheter valve were axial and directed below the simulated native valve annulus. It was determined that for both blood residence time and aortic Reynolds stresses, it is optimal to have the annulus of the transcatheter valve deployed as close to the aortic valve annulus as possible.

Real-time aortic valve segmentation from transesophageal echocardiography sequence

PURPOSE

Geometric features of the aortic valve play an important role in many applications, such as the clinical diagnostics, shape modeling and image-guided cardiac interventions, especially for the transcatheter aortic valve implantation (TAVI) procedure. However, few works have been reported on the topic of aortic valve segmentation from transesophageal echocardiography (TEE) sequences. To obtain accurate segmentation results and further provide valid support for TAVI, this paper presents a real-time method for segmenting the aortic valve from intraoperative, short-axis view TEE sequences, using an improved probability estimation and continuous max-flow (CMF) approach.

METHODS

The proposed segmentation method includes two key stages: (1) In the probability estimation stage, five different prior frames spanning a cardiac circle are firstly selected with the aortic valve manually segmented by an expert. Then, the improved composite probability estimation (CPE) and single probability estimation (SPE) over the five prior frames are, respectively, constructed based on their radial average intensity and radial distance. (2) In the energy function construction stage, the similarity metric is calculated to find out the matching exponents between the current input TEE frame and the prior frames. The typical foreground and background intensities of prior images are therefore used to construct the corresponding energy function. Finally, the CMF approach, accelerated with a graphic processing unit (GPU), is employed to achieve the aortic valve contours in real time.

RESULTS

The evaluation study contained 30 sequences, with each containing 62-146 short-axis TEE frames. The results were compared with the manual segmentation (ground truth). The average symmetric contour distance (ASCD), dice metric (DM) and the reliability of the algorithm reached 0.85 ± 0.21 mm, 0.96 ± 0.01 and 0.90 (d = 0.95), respectively, and the computation time was 57.04 ± 8.98 ms per frame.

CONCLUSION

The experiment results reveal that the proposed method can achieve accurate and real-time segmentation of aortic valve from TEE sequence of short-axis view.

Semiautomatic segmentation of aortic valve from sequenced ultrasound image using a novel shape-constraint GCV model

PURPOSE

Effective and accurate segmentation of the aortic valve (AV) from sequenced ultrasound (US) images remains a technical challenge because of intrinsic factors of ultrasound images that impact the quality and the continuous changes of shape and position of segmented objects. In this paper, a novel shape-constraint gradient Chan-Vese (GCV) model is proposed for segmenting the AV from time serial echocardiography.

METHODS

The GCV model is derived by incorporating the energy of the gradient vector flow into a CV model framework, where the gradient vector energy term is introduced by calculating the deviation angle between the inward normal force of the evolution contour and the gradient vector force. The flow force enlarges the capture range and enhances the blurred boundaries of objects. This is achieved by adding a circle-like contour (constructed using the AV structure region as a constraint shape) as an energy item to the GCV model through the shape comparison function. This shape-constrained energy can enhance the image constraint force by effectively connecting separate gaps of the object edge as well as driving the evolution contour to quickly approach the ideal object. Because of the slight movement of the AV in adjacent frames, the initial constraint shape is defined by users, with the other constraint shapes being derived from the segmentation results of adjacent sequence frames after morphological filtering. The AV is segmented from the US images by minimizing the proposed energy function.

RESULTS

To evaluate the performance of the proposed method, five assessment parameters were used to compare it with manual delineations performed by radiologists (gold standards). Three hundred and fifteen images acquired from nine groups were analyzed in the experiment. The area-metric overlap error rate was 6.89% ± 2.88%, the relative area difference rate 3.94% ± 2.63%, the average symmetric contour distance 1.08 ± 0.43 mm, the root mean square symmetric contour distance 1.37 ± 0.52 mm, and the maximum symmetric contour distance was 3.57 ± 1.72 mm.

CONCLUSIONS

Compared with the CV model, as a result of the combination of the gradient vector and neighborhood shape information, this semiautomatic segmentation method significantly improves the accuracy and robustness of AV segmentation, making it feasible for improved segmentation of aortic valves from US images that have fuzzy boundaries.

Intra-operative 2-D ultrasound and dynamic 3-D aortic model registration for magnetic navigation of transcatheter aortic valve implantation

We propose a navigation system for transcatheter aortic valve implantation that employs a magnetic tracking system (MTS) along with a dynamic aortic model and intra-operative ultrasound (US) images. This work is motivated by the desire of our cardiology and cardiac surgical colleagues to minimize or eliminate the use of radiation in the interventional suite or operating room. The dynamic 3-D aortic model is constructed from a preoperative 4-D computed tomography dataset that is animated in synchrony with the real time electrocardiograph input of patient, and then preoperative planning is performed to determine the target position of the aortic valve prosthesis. The contours of the aortic root are extracted automatically from short axis US images in real-time for registering the 2-D intra-operative US image to the preoperative dynamic aortic model. The augmented MTS guides the interventionist during positioning and deployment of the aortic valve prosthesis to the target. The results of the aortic root segmentation algorithm demonstrate an error of 0.92±0.85 mm with a computational time of 36.13±6.26 ms. The navigation approach was validated in porcine studies, yielding fiducial localization errors, target registration errors, deployment distance, and tilting errors of 3.02±0.39 mm, 3.31±1.55 mm, 3.23±0.94 mm, and 5.85±3.06(°) , respectively.