An image fusion tool for echo-guided left ventricular lead placement in cardiac resynchronization therapy: Performance and workflow integration analysis

Optimizing CRT Lead Placement Accuracy With CMR-Guided On-Screen Targeting: A Randomized Controlled Trial (ADVISE-CRT III)

BACKGROUND

To improve cardiac resynchronization therapy (CRT) an on-screen image-guidance platform, CARTBox-Suite (CART-Tech B.V.), was developed to identify left ventricular pacing electrode (LVPE) implantation sites and facilitate precise LVPE placement. This multicenter randomized trial evaluated the efficacy of image guidance on LVPE implantation accuracy and its impact on left ventricular end-systolic volume (LVESV) reduction 6 months after CRT.

OBJECTIVES

The aim of this trial is to improve the accuracy and efficacy of LVPE placement in CRT.

METHODS

A total of 131 heart failure patients (80% with Class I CRT indication) were enrolled across 7 hospitals in the Netherlands. CARTBox-Suite, which utilizes a cloud-based AI algorithm, was used to identify a target area with late mechanical activation based on cardiac magnetic resonance imaging. Scarred areas marked by late gadolinium enhancement were excluded. Patients were randomized to image-guided implantation, with on-screen guidance during the procedure or conventional implantation.

RESULTS

The primary endpoint, LVPE implantation in the target area, was achieved significantly more often in the image-guided group (66.7% vs 29.2%; P < 0.001). The secondary endpoint was fewer LVPE placed in scarred areas in the image-guided group (7.1% vs 36.4%; P = 0.006). Mean LVESV reduction was greater in the image-guided group (43.2% vs. 37.6%), although not significantly (P = 0.166). Patients with myocardial scar showed greater LVESV reduction with image guidance (40.7% vs 27.7%; P = 0.028).

CONCLUSIONS

Image-guided implantation resulted in significantly more LVPE placed in the target area and greater LVESV reduction in patients with myocardial scar.

Spatiotemporal registration and fusion of transthoracic echocardiography and volumetric coronary artery tree

PURPOSE

Cardiac multimodal image fusion can offer an image with various types of information in a single image. Many coronary stenosis, which are anatomically clear, are not functionally significant. The treatment of such kind of stenosis can cause irreversible effects on the patient. Thus, choosing the best treatment planning depend on anatomical and functional information is very beneficial.

METHODS

An algorithm for the fusion of coronary computed tomography angiography (CCTA) as an anatomical and transthoracic echocardiography (TTE) as a functional modality is presented. CCTA and TTE are temporally registered using manifold learning. A pattern search optimization algorithm, using normalized mutual information, is used to find the best match slice to TTE frame from CCTA volume. By employing a free-form deformation, the heart's non-rigid deformations are modeled. The spatiotemporal registered TTE frame is embedded to achieve the fusion result.

RESULTS

The accuracy is evaluated on CCTA and TTE data obtained from 10 patients. In temporal registration, mean absolute error of 1.97 [Formula: see text] 1.23 is resulted from comparing the output frame numbers from the algorithm and from manual assignment by an expert. In spatial registration, the accuracy of the similarity between the best match slice from CCTA volume and TTE frame is resulted in 1.82 [Formula: see text] 0.024 mm, 6.74 [Formula: see text] 0.013 mm, and 0.901 [Formula: see text] 0.0548 due to mean absolute distance, Hausdorff distance, and Dice similarity coefficient, respectively.

CONCLUSION

Without the use of ECG and Optical tracking systems, a semiautomatic framework of spatiotemporal registration and fusion of CCTA volume and TTE frame is presented. The experimental results showed the effectiveness of our proposed method to create complementary information from TTE and CCTA, which may help in the early diagnosis and effective treatment of cardiovascular diseases (CVDs).