Advanced XR-Based 6-DOF Catheter Tracking System for Immersive Cardiac Intervention Training
- URL: http://arxiv.org/abs/2411.02611v1
- Date: Mon, 04 Nov 2024 21:05:40 GMT
- Title: Advanced XR-Based 6-DOF Catheter Tracking System for Immersive Cardiac Intervention Training
- Authors: Mohsen Annabestani, Sandhya Sriram, S. Chiu Wong, Alexandros Sigaras, Bobak Mosadegh,
- Abstract summary: This paper presents a novel system for real-time 3D tracking and visualization of intracardiac echocardiography (ICE) catheters.
A custom 3D-printed setup captures biplane video of the catheter, while a specialized computer vision algorithm reconstructs its 3D trajectory.
The system's data is integrated into an interactive Unity-based environment, rendered through the Meta Quest 3 XR headset.
- Score: 37.69303106863453
- License:
- Abstract: Extended Reality (XR) technologies are gaining traction as effective tools for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a novel system for real-time 3D tracking and visualization of intracardiac echocardiography (ICE) catheters, with precise measurement of the roll angle. A custom 3D-printed setup, featuring orthogonal cameras, captures biplane video of the catheter, while a specialized computer vision algorithm reconstructs its 3D trajectory, localizing the tip with sub-millimeter accuracy and tracking the roll angle in real-time. The system's data is integrated into an interactive Unity-based environment, rendered through the Meta Quest 3 XR headset, combining a dynamically tracked catheter with a patient-specific 3D heart model. This immersive environment allows the testing of the importance of 3D depth perception, in comparison to 2D projections, as a form of visualization in XR. Our experimental study, conducted using the ICE catheter with six participants, suggests that 3D visualization is not necessarily beneficial over 2D views offered by the XR system; although all cardiologists saw its utility for pre-operative training, planning, and intra-operative guidance. The proposed system qualitatively shows great promise in transforming catheter-based interventions, particularly ICE procedures, by improving visualization, interactivity, and skill development.
Related papers
- Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization [1.0580610673031074]
In medical image visualization, path tracing of volumetric medical data like CT scans produces lifelike visualizations.
We propose a novel approach utilizing GS to create an efficient but static intermediate representation of CT scans.
Our approach achieves interactive frame rates while preserving anatomical structures, with quality adjustable to the target hardware.
arXiv Detail & Related papers (2024-10-22T12:56:58Z) - 3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models [51.855377054763345]
This paper introduces 3D-CT-GPT, a Visual Question Answering (VQA)-based medical visual language model for generating radiology reports from 3D CT scans.
Experiments on both public and private datasets demonstrate that 3D-CT-GPT significantly outperforms existing methods in terms of report accuracy and quality.
arXiv Detail & Related papers (2024-09-28T12:31:07Z) - SLAM assisted 3D tracking system for laparoscopic surgery [22.36252790404779]
This work proposes a real-time monocular 3D tracking algorithm for post-registration tasks.
Experiments from in-vivo and ex-vivo tests demonstrate that the proposed 3D tracking system provides robust 3D tracking.
arXiv Detail & Related papers (2024-09-18T04:00:54Z) - SurgTrack: CAD-Free 3D Tracking of Real-world Surgical Instruments [21.536823332387993]
Vision-based surgical navigation has received increasing attention due to its non-invasive, cost-effective, and flexible advantages.
In particular, a critical element of the vision-based navigation system is tracking surgical instruments.
We propose the SurgTrack, a two-stage 3D instrument tracking method for CAD-free and robust real-world applications.
arXiv Detail & Related papers (2024-09-04T10:29:59Z) - CardioSpectrum: Comprehensive Myocardium Motion Analysis with 3D Deep Learning and Geometric Insights [6.415915756409993]
Conventional neural networks have difficulty predicting subtle tangential movements.
We present a comprehensive approach to address this problem.
Our 3D deep learning architecture, based on the ARFlow model, is optimized to handle complex 3D motion analysis tasks.
arXiv Detail & Related papers (2024-07-04T09:57:44Z) - Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train [66.35766658717205]
Successful echocardiography requires a thorough understanding of the structures on the two-dimensional plane and the spatial relationships between planes in three-dimensional space.
We propose a large-scale self-supervised pre-training method to acquire a cardiac structure-aware world model.
arXiv Detail & Related papers (2024-06-28T08:54:44Z) - Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction [53.93674177236367]
Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging.
Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image.
This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses.
We introduce a novel geometry-aware encoder-decoder framework to solve this problem.
arXiv Detail & Related papers (2023-03-26T14:38:42Z) - VRContour: Bringing Contour Delineations of Medical Structures Into
Virtual Reality [16.726748230138696]
Contouring is an indispensable step in Radiotherapy (RT) treatment planning.
Today's contouring software is constrained to only work with a 2D display, which is less intuitive and requires high task loads.
We present VRContour and investigate how to effectively bring contouring for radiation oncology into VR.
arXiv Detail & Related papers (2022-10-21T23:22:21Z) - Agent with Tangent-based Formulation and Anatomical Perception for
Standard Plane Localization in 3D Ultrasound [56.7645826576439]
We introduce a novel reinforcement learning framework for automatic SP localization in 3D US.
First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space.
Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search.
arXiv Detail & Related papers (2022-07-01T14:53:27Z) - Revisiting 3D Context Modeling with Supervised Pre-training for
Universal Lesion Detection in CT Slices [48.85784310158493]
We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices.
With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset.
The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
arXiv Detail & Related papers (2020-12-16T07:11:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.