Time-Varying Coronary Artery Deformation: A Dynamic Skinning Framework for Surgical Training

• URL: http://arxiv.org/abs/2503.02218v1
• Date: Tue, 04 Mar 2025 02:51:37 GMT
• Title: Time-Varying Coronary Artery Deformation: A Dynamic Skinning Framework for Surgical Training
• Authors: Shuo Wang, Tong Ren, Nan Cheng, Rong Wang, Li Zhang,
• Abstract summary: This study proposes a novel dynamic modeling framework for coronary arteries using skeletal skinning weights.<n>It aims to achieve precise control over vessel deformation while maintaining real-time performance for surgical simulation applications.
• Score: 33.92599418560439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: This study proposes a novel anatomically-driven dynamic modeling framework for coronary arteries using skeletal skinning weights computation, aiming to achieve precise control over vessel deformation while maintaining real-time performance for surgical simulation applications. Methods: We developed a computational framework based on biharmonic energy minimization for skinning weight calculation, incorporating volumetric discretization through tetrahedral mesh generation. The method implements temporal sampling and interpolation for continuous vessel deformation throughout the cardiac cycle, with mechanical constraints and volume conservation enforcement. The framework was validated using clinical datasets from 5 patients, comparing interpolated deformation results against ground truth data obtained from frame-by-frame segmentation across cardiac phases. Results: The proposed framework effectively handled interactive vessel manipulation. Geometric accuracy evaluation showed mean Hausdorff distance of 4.96 +- 1.78 mm and mean surface distance of 1.78 +- 0.75 mm between interpolated meshes and ground truth models. The Branch Completeness Ratio achieved 1.82 +- 0.46, while Branch Continuity Score maintained 0.84 +- 0.06 (scale 0-1) across all datasets. The system demonstrated capability in supporting real-time guidewire-vessel collision detection and contrast medium flow simulation throughout the complete coronary tree structure. Conclusion: Our skinning weight-based methodology enhances model interactivity and applicability while maintaining geometric accuracy. The framework provides a more flexible technical foundation for virtual surgical training systems, demonstrating promising potential for both clinical practice and medical education applications. The code is available at https://github.com/ipoirot/DynamicArtery.

Related papers

• 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)
• Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach [33.92599418560439]
  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.
  arXiv  Detail & Related papers  (2025-05-16T06:13:55Z)
• AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows [8.062885940500259]
  This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images. The proposed approach demonstrates state-of-the-art performance in segmentation and mesh quality while significantly reducing manual effort and processing time.
  arXiv  Detail & Related papers  (2025-03-16T14:18:25Z)
• Thermodynamics-informed graph neural networks for real-time simulation of digital human twins [2.6811507121199325]
  This paper presents a novel methodology aimed at advancing current lines of research in soft tissue simulation.<n>The proposed approach integrates the geometric bias of graph neural networks with the physical bias derived from the imposition of a metriplectic structure.<n>Based on the adopted methodologies, we propose a model that predicts human liver responses to traction and compression loads in as little as 7.3 milliseconds.
  arXiv  Detail & Related papers  (2024-12-16T18:01:40Z)
• Image2Flow: A hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data [0.0]
  This study used 135 3D cardiac MRIs from both a public and private dataset. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values.
  arXiv  Detail & Related papers  (2024-02-28T11:01:14Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane [58.871015937204255]
  We introduce Fast Orthogonal Plane (plane) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of neural planes. This factorization iscretizes four-dimensional space, leading to a decreased memory usage and faster optimization.
  arXiv  Detail & Related papers  (2023-12-23T13:27:50Z)
• Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics [24.8579242043367]
  Local hemodynamic forces play an important role in determining the functional significance of coronary arterial stenosis. We propose an end-to-end deep learning framework, which could predict the coronary artery hemodynamics from CCTA images.
  arXiv  Detail & Related papers  (2023-05-30T15:12:52Z)
• Machine Learning based Extraction of Boundary Conditions from Doppler Echo Images for Patient Specific Coarctation of the Aorta: Computational Fluid Dynamics Study [0.0]
  This study aims to investigate the application of machine learning (ML) methods to obtain boundary conditions (BCs) from Doppler Echocardiography images. Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest.
  arXiv  Detail & Related papers  (2022-09-19T16:00:38Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Prediction of progressive lens performance from neural network simulations [62.997667081978825]
  The purpose of this study is to present a framework to predict visual acuity (VA) based on a convolutional neural network (CNN) The proposed holistic simulation tool was shown to act as an accurate model for subjective visual performance.
  arXiv  Detail & Related papers  (2021-03-19T14:51:02Z)
• Appearance Learning for Image-based Motion Estimation in Tomography [60.980769164955454]
  In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Patient motion corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object.
  arXiv  Detail & Related papers  (2020-06-18T09:49:11Z)

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.