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.
Recent recognition of the poor prognosis of significant tricuspid regurgitation (TR) has resulted in increased indication for tricuspid valve interventions. TR can affect 65% to 85% of the population worldwide and has a 1-year mortality rate greater than 25% in patients with severe regurgitation. A key procedure for patient selection and intraoperative assessment of intervention involves determining the location of the vena contracta width and regurgitant jet area. Manual visualization of the vena contracta (VC) can be time consuming depending on its location. Decreasing the required time for VC visualization would potentially result in decreased time for patient assessment, device deployment and intraprocedural intervention evaluation thus reducing hospital costs. There is currently no commercially available automatic VC detection system. We present a method to automatically localize the VC using 3D intracardiac echocardiography (ICE) on a simplified anthropomorphic phantom as a proof of concept. A beating heart phantom was outfitted with a silicone flange containing a mechanical valve and an orifice to cause a regurgitant jet. We propose an image processing pipeline to segment the regurgitant jet from Doppler ultrasound, as acquired by ICE, to determine the location of the VC automatically. The VC locations output by the algorithm were validated both qualitatively and quantitatively by comparison to manually annotated VC locations. On average, the location of VC detected by the algorithm was within 1.52±35mm to the location of the ground truth VC. We envision that this study will play a major role towards the development of an automated system for VC localization during TV interventions.
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.
Electromagnetic image guidance systems have emerged as more secure methods to improve the performance of several catheter-based minimally invasive surgical procedures. Small sensors are incorporated within catheters and guidewires in order to track and guide in real-time their position and orientation with a reduced intra- procedural radiation exposure and contrast agent injections. One of the major limits of these systems is related to the unsuitable sensorization strategy for the J-tip guidewires, due to the structural constraints of the sensor coils available on the market. In this work we present preliminary results on a sensors bending test in static conditions to assess whether and when the precision of the sensor remains unchanged and/or deteriorates. In the worst case, the highest standard deviation is less than 0:10 mm.
Intracardiac echocardiography (ICE) systems are routinely used in percutaneous cardiac interventions for interventional and surgical navigation. Conavi's Foresight ICE is a new ICE system that uses a mechanically rotating transducer to generate a 2D conical surface image in 3D space, in contrast to the more typical side-firing phased array. When combined with magnetic tracking technology, this unique imaging geometry poses new calibration challenges and opportunities. Existing ultrasound calibration methods are designed for 2D planar images and cannot be trivially applied to unique 2:5D conical surface images provided by the Foresight ICE system. In this work a spatial and temporal calibration technique applied to the unique case of conical ultrasound image data is described and validated. Precision of calibration parameters is used to quantify the validation of our calibration method and the overall system accuracy is validated using point source and sphere centroid localization. We re- port a maximum error of 5:07mm for point reconstruction accuracy and 1:94mm for sphere centroid localization accuracy.
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