3D localization of vena contracta using Doppler ICE imaging in tricuspid valve interventions

Abstract

Purpose Tricuspid valve (TV) interventions face the challenge of imaging the anatomy and tools because of the ‘TEE-unfriendly’ nature of the TV. In edge-to-edge TV repair, a core step is to position the clip perpendicular to the coaptation gap. In this study, we provide a semi-automated method to localize the VC from Doppler intracardiac echo (ICE) imaging in a tracked 3D space, thus providing a pre-mapped location of the coaptation gap to assist device positioning. Methods A magnetically tracked ICE probe with Doppler imaging capabilities is employed in this study for imaging three patient-specific TVs placed in a pulsatile heart phantom. For each of the valves, the ICE probe is positioned to image the maximum regurgitant flow for five cardiac cycles. An algorithm then extracts the regurgitation imaging and computes the exact location of the vena contracta on the image. Results Across the three pathological, patient-specific valves, the average distance error between the detected VC and the ground truth model is $$({1.22 \pm 2.00})$$ ( 1.22 ± 2.00 ) mm. For each of the valves, one case represented the outlier where the algorithm misidentified the vena contracta to be near the annulus. In such cases, it is recommended to retake the five-second imaging data. Conclusion This study presented a method for ultrasound-based localization of vena contracta in 3D space. Mapping such anatomical landmarks has the potential to assist with device positioning and to simplify tricuspid valve interventions by providing more contextual information to the interventionalists, thus enhancing their spatial awareness. Additionally, ICE can be used to provide live US and Doppler imaging of the complex TV anatomy throughout the procedure.

An open-source 3D Slicer module for fluoro-free transcatheter vessel navigation

“…In our Slicer implementation, vessel segmentation is performed using deep learning-based methods which allow for realtime processing of imaging frames. A pre-trained U-Net model as described by Nisar et al 9 processes 2D ICE images and produce a corresponding 2D lumen segment. The model is currently trained on animal (swine) inferior vena cava images acquired using a Foresight™ ICE probe, which is characterized by its 2.5D conical surface images.…”

“…The same group also developed deep learning-based methods for segmenting vessel lumen from ICE imaging in real-time. 9 In this work, we aim to combine the abovementioned ICE-based vessel navigation workflow w ith t he deep learning-based segmentation methods in order to create a complete, open-source, and user-friendly 3D Slicer module 10 as a prototype of a platform that can be easily used by a clinician. The development of a customized 3D Slicer module allows for the methods to be seamlessly used during in-vivo experiments as well as further testing for clinical usage and feasibility -which is currently a major barrier to the clinical adaptation and widespread use of image-guided systems (IGSs) .…”

“…The accuracy of the ICE-generated vessel is established by comparing it to the CT-derived vascular roadmap. The Slicer module implements the methods from 8,9 with two major modifications -A) the vessel segmentation portion of the vessel reconstruction pipeline is replaced with a deep learning-based solution provided by, 9 and B) updated methods are used during the last surface reconstruction step. Figure 1 provides an overview of the workflow of the module, as well as indicating existing Slicer modules we utilize in our vascular reconstruction procedure.…”