Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach

• URL: http://arxiv.org/abs/2505.10902v1
• Date: Fri, 16 May 2025 06:13:55 GMT
• Title: Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach
• Authors: Shuo Wang, Tong Ren, Nan Cheng, Rong Wang, Li Zhang,
• Abstract summary: Existing training systems lack accurate simulation of cardiac physiological dynamics.<n>This study develops a comprehensive dynamic cardiac model research framework based on 4D-CTA.
• Score: 33.92599418560439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background and Objective: Precise preoperative planning and effective physician training for coronary interventions are increasingly important. Despite advances in medical imaging technologies, transforming static or limited dynamic imaging data into comprehensive dynamic cardiac models remains challenging. Existing training systems lack accurate simulation of cardiac physiological dynamics. This study develops a comprehensive dynamic cardiac model research framework based on 4D-CTA, integrating digital twin technology, computer vision, and physical model manufacturing to provide precise, personalized tools for interventional cardiology. Methods: Using 4D-CTA data from a 60-year-old female with three-vessel coronary stenosis, we segmented cardiac chambers and coronary arteries, constructed dynamic models, and implemented skeletal skinning weight computation to simulate vessel deformation across 20 cardiac phases. Transparent vascular physical models were manufactured using medical-grade silicone. We developed cardiac output analysis and virtual angiography systems, implemented guidewire 3D reconstruction using binocular stereo vision, and evaluated the system through angiography validation and CABG training applications. Results: Morphological consistency between virtual and real angiography reached 80.9%. Dice similarity coefficients for guidewire motion ranged from 0.741-0.812, with mean trajectory errors below 1.1 mm. The transparent model demonstrated advantages in CABG training, allowing direct visualization while simulating beating heart challenges. Conclusion: Our patient-specific digital-physical twin approach effectively reproduces both anatomical structures and dynamic characteristics of coronary vasculature, offering a dynamic environment with visual and tactile feedback valuable for education and clinical planning.

Related papers

• Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images [29.39287623923477]
  We present Cardiac-CLIP, a multi-modal foundation model designed for 3D cardiac CT images.<n>CLIP is developed through a two-stage pre-training strategy.<n>CLIP is evaluated across multiple tasks, including cardiovascular abnormality classification, information retrieval and clinical analysis.
  arXiv  Detail & Related papers  (2025-07-29T17:20:32Z)
• ClipGS: Clippable Gaussian Splatting for Interactive Cinematic Visualization of Volumetric Medical Data [51.095474325541794]
  We introduce ClipGS, an innovative Gaussian splatting framework with the clipping plane supported, for interactive cinematic visualization of medical data.<n>We validate our method on five volumetric medical data, and reach an average 36.635 PSNR rendering quality with 156 FPS and 16.1 MB model size.
  arXiv  Detail & Related papers  (2025-07-09T08:24:28Z)
• Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning [33.92599418560439]
  The aim of this study is to develop and evaluate a dynamic cardiovascular visualization tool for preoperative coronary artery bypass grafting (CABG) planning.<n>The tool produces clinically relevant dynamic holograms from patient-specific data, with clinical feedback confirming its effectiveness for preoperative planning.
  arXiv  Detail & Related papers  (2025-04-28T00:56:06Z)
• EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance [79.66329903007869]
  We present EchoWorld, a motion-aware world modeling framework for probe guidance.<n>It encodes anatomical knowledge and motion-induced visual dynamics.<n>It is trained on more than one million ultrasound images from over 200 routine scans.
  arXiv  Detail & Related papers  (2025-04-17T16:19:05Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations.<n>We incorporate elements modeling effects to better align simulated data with real-world measurements.<n>The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• Sequence-aware Pre-training for Echocardiography Probe Guidance [66.35766658717205]
  Cardiac ultrasound faces two major challenges: (1) the inherently complex structure of the heart, and (2) significant individual variations. Previous works have only learned the population-averaged 2D and 3D structures of the heart rather than personalized cardiac structural features. We propose a sequence-aware self-supervised pre-training method to learn personalized 2D and 3D cardiac structural features.
  arXiv  Detail & Related papers  (2024-08-27T12:55:54Z)
• 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)
• Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets [2.0286377328378737]
  Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed.
  arXiv  Detail & Related papers  (2024-04-30T14:16:45Z)
• Continuous 3D Myocardial Motion Tracking via Echocardiography [30.19879953016694]
  Myocardial motion tracking is an essential clinical tool in the prevention and detection of cardiovascular diseases. Current techniques suffer from incomplete and inaccurate motion estimation of the myocardium in both spatial and temporal dimensions. This paper introduces the Neural Cardiac Motion Field (NeuralCMF) to model the 3D structure and the comprehensive 6D forward/backward motion of the heart.
  arXiv  Detail & Related papers  (2023-10-04T13:11:20Z)
• Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach [39.36827689390718]
  We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
  arXiv  Detail & Related papers  (2023-08-16T14:57:12Z)
• Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations [2.208529796170897]
  We use Latent Neural Ordinary Differential Equations to learn the temporal pressure-volume dynamics of a heart failure patient. Our surrogate model based on LNODEs is trained from 400 3D-0D whole-heart closed-loop electromechanical simulations. This paper introduces the most advanced surrogate model of cardiac function available in the literature.
  arXiv  Detail & Related papers  (2023-06-08T16:13:29Z)
• Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets [5.178090215294132]
  We introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features. We show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information.
  arXiv  Detail & Related papers  (2020-10-02T15:31:36Z)

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.