Accurate models of the mitral valve are highly valuable for studying the physiology of the heart and its various pathologies, as well as creating physical replicas for cardiac surgery training. Currently, heart simulator technologies are used which rely on patient-specific data to create valve replicas. Alternatively, mathematical models of the mitral valve have been developed for computational applications. However, there are no studies that mathematically model both the mitral valve’s leaflets and its saddle-shaped annulus in a single design together in current literature. This results in anatomic inaccuracies in current models, as either only the leaflets or the saddle-shaped annulus are realistically modelled. Mathematical models to date have not been replicated as dynamic, physical valves and validated in a heart simulator system. We propose a new parametric representation of the mitral valve based on a combination of valve models from prior literature, combining both accurate leaflet shape, and annular geometry. A physical silicone replica of the model is created and validated in a pulse duplicator. Using a transesophageal echocardiography probe with color Doppler imaging, we demonstrate that our combined model replicates healthy valve behaviour, showing no regurgitation at realistic pressure gradients across the valve.
KEYWORDS: Image segmentation, Ice, 3D tracking, 3D image processing, Ultrasonography, In vivo imaging, Calibration, Magnetic tracking, 3D modeling, 3D image reconstruction
Vascular navigation is an essential component of transcatheter cardiovascular interventions, conventionally performed using either 2D fluoroscopic imaging or CT- derived vascular roadmaps which can lead to many complications for the patients as well as the clinicians. This study presents an open-source and user-friendly 3D Slicer module that performs vessel reconstruction from tracked intracardiac ultrasound (ICE) imaging using deep learning-based methods. We also validate the methods by performing a vessel-phantom study. The results indicate that our Slicer module is able to reconstruct vessels with sufficient accuracy with an average distance error of 0.86 mm. Future work involves improving the speed of the methods as well as testing the module in an in-vivo setting. Clinical adaptation of this platform will allow the clinicians to navigate the vessels in 3D and will potentially enhance their spatial awareness as well as improve procedural safety.
Segmentation of the mitral annulus is an important step in many cardiac applications. Current methods to delineate the mitral annulus often require extensive user interaction. Several methods have been proposed to automate mitral annulus segmentation, but often use methods which require sampling 2D planes from the 3D volume, discarding some of the contextual information contained in the original 3D volume. We propose a new 4D mitral annulus segmentation method based on 3D CNN regression of Fourier coefficients describing the shape of predicted annulus. Our model predicts a set of ten coefficients for each of the three image axes, which can then be used to sample annulus coordinates through the inverse Fourier transform. We acquired a dataset of 90 cases from diagnostic imaging of mitral valve patients, with corresponding annulus segmentations. This was split into training, validation and test sets of 75, 5, and 10 cases respectively. Following training, our model achieves a curve-to-curve accuracy of 5.5 ± 2.2 mm on the test set, with training accuracy of 0.46 ± 0.21 mm. Our model achieves accuracy similar to current state-of-the-art methods, and can achieve inference speed of 40 frames-per-second, which is suitable for use in real-time image guidance applications.
Vascular navigation is a prerequisite to transcatheter cardiac interventions. The current standard approach to catheter navigation relies on real-time fluoroscopy, while this technique utilizes ionizing radiation and it places the interventionalist at risk for eye cataracts and cancer. The shielding equipment needed to mitigate these risks is associated with spinal issues and neck and back pain which has led towards the coining of the term “interventionalist’s disc disease”. A proposed alternative is to have an ultrasound-guided vascular navigation system where a catheter-based ultrasound probe scans the vessel and reconstructs the vascular roadmap, which can then be navigated by tracked guidewire or catheter. One of the major challenges here is the segmentation of the vessel lumen from the ultrasound images. In this study, we address this challenge using a deep learning based approach. We acquired inferior vena cava (IVC) images from an animal study performed using a radial, forward-looking Foresight intracardiac echocardiography (ICE) ultrasound probe. The ground truth was established using manual segmentations and validated by an expert clinician. We use the MONAI platform to train a U-net architecture on our dataset to perform vessel segmentation. The images are cropped to retain only the central 300 pixels as the traversed vessel will always appear central to the radial ICE image. Data augmentation was performed to enhance the number of images available for training. After post-processing, the segmentation output, a 90 % accuracy was achieved as indicated by the Dice coefficient. We plan on integrating this vessel segmentation pipeline in an image-guided surgical navigation system.
KEYWORDS: Process modeling, 3D modeling, Heart, Silicon, Pathology, Image segmentation, Data modeling, Ultrasonography, Surgery, Visual process modeling
Physical replicas of patient specific heart valve pathologies may improve clinicians’ ability to plan the optimal treatment for patients with complex valvular heart disease. Our previous work has demonstrated the ability to replicate patient pathology of the adult mitral valve (MV) in a dynamic environment [13]. Infant congenital heart defects present possibly the most challenging form of valvular disease, given the range of pathologies, the relative size of these valves compared to adult anatomy, and the rarity of congenital heart disease. Patient specific valve models could be particularly valuable for pediatric cardiologists and surgeons, as a means to both plan for and practice interventions. Our current goal is to assess our ability to apply our workflow to the more challenging case of the tricuspid valve (TV) presented in cases of hypoplastic left heart syndrome (HLHS). We explore the feasibility of adapting our previous workflow for creating dynamic silicone MV models for pre-surgical planning and simulation training, to developing 3D echocardiogram derived, patient specific TV models for use in a physical heart simulator. These models are intended for characterization of the TV, and exploration of the relationship between specific anatomical features and tricuspid regurgitation (TR) severity. The simulations may be relevant to pre-surgical planning of repair of the particularly complex and unique anatomical pathologies presented in children with HLHS.
Echocardiography is widely used for obtaining images of the heart for both preoperative diagnostic and intraoperative purposes. For procedures targeting the mitral valve, transesophageal echocardiography (TEE) is the primary imaging modality used as it provides clear 3D images of the valve and surrounding tissues. However, TEE suffers from image artifacts and signal dropout, particularly for structures lying below the valve including chordae tendineae. In order to see these structures, alternative echo views are required. However due to the limited field of view obtainable, the entire ventricle cannot be directly visualized in sufficient detail from a single image acquisition in 3D. This results in a large learning curve for interpreting these images as the multiple views must be reconciled mentally by a clinician. We propose applying an image compounding technique to TEE images acquired from a mid-esophageal position and a number of transgastric positions in order to reconstruct a high-detail image of the mitral valve and sub-valvular structures. This compounding technique utilizes a semi-simultaneous group-wise registration to align the multiple 3D volumes, followed by a weighted intensity compounding step. This compounding technique is validated using images acquired of a custom silicone phantom, excised porcine mitral valve units, and two patient data sets. We demonstrate that this compounding technique accurately captures the physical structures present, including the mitral valve, chordae tendineae and papillary muscles.
KEYWORDS: Virtual reality, Visualization, Echocardiography, 3D image processing, Ultrasonography, Volume rendering, 3D displays, Medical image visualization, Medical imaging
Cardiac surgeons rely on diagnostic imaging for preoperative planning. Recently, developments have been made on improving 3D ultrasound (US) spatial compounding tailored for cardiac images. Compounded 3D ultrasound volumes are able to capture complex anatomical structures at a level similar to a CT scan, however these images are difficult to display and visualize due to an increased amount of surrounding tissue captured including excess noise at the volume boundaries. Traditional medical image visualization software does not easily allow for viewing 2D slices at arbitrary angles, and 3D rendering techniques do not adequately capture depth information without the use of advanced transfer functions or other depth-encoding techniques that must be tuned to each individual data set. Previous studies have shown that the effective use of virtual reality (VR) can improve image visualization, usability and reduce surgical errors in case planning. We demonstrate the novel use of a VR system for the application of measuring chordal lengths from compounded transesophageal and transgastric echocardiography (TEE, TTE) ultrasound images. Compounded images are constructed from TEE (en-face) views registered and spatially compounded with multiple TEE transgastric views in order to capture both the mitral valve leaflets and chordae tendineae with high levels of detail. Users performed the task of taking linear measurements of chordae visible in these images using both traditional software and a VR platform. Compared to traditional software, the VR platform offered a more intuitive experience with respect to orientation, however users felt there was a lack of precision when performing the measurement tasks.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.