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Expanding point cloud statistical shape model applications: Generalized vascular modeling for population-level

Yao Zeng1, Zheng Sun1, Mengfei Wang2

  • 1Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, PR China

Computer Methods and Programs in Biomedicine
|July 1, 2025
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A new workflow using statistical shape modeling and clustering improves hemodynamic simulations for large populations. This method enhances accuracy and efficiency for analyzing vascular geometries and blood flow patterns.

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Area of Science:

  • Biomedical Engineering
  • Computational Fluid Dynamics
  • Medical Imaging

Background:

  • Population-scale hemodynamic research is limited by the computational cost of patient-specific models and the oversimplification of cylindrical models.
  • A novel Tier-2 workflow is proposed to address these limitations by integrating point-cloud statistical shape modeling (Pcd-SSM) with HDBSCAN clustering.
  • This approach aims to efficiently characterize complex internal carotid artery (ICA) geometries and analyze associated blood flow patterns.

Purpose of the Study:

  • To develop and validate an efficient computational framework for population-scale hemodynamic analysis.
  • To accurately characterize vascular morphology and predict blood flow dynamics in large cohorts.
  • To improve the understanding of hemodynamics in both normal and stenosed arterial segments.

Main Methods:

  • Utilized Time-of-flight Magnetic Resonance Angiography (TOF-MRA) data from 229 ICAs.
  • Converted TOF-MRA data into point-cloud correspondences using Point2SSM and applied Principal Component Analysis (PCA).
  • Employed unsupervised clustering (HDBSCAN) and validated models using steady, non-Newtonian Computational Fluid Dynamics (CFD) simulations.

Main Results:

  • Achieved significant enhancements in accuracy (77-95% error reduction) and efficiency (approx. 65x reduction in computational cost) for hemodynamic simulations in large cohorts.
  • Successfully captured approximately 91% of velocity increases and 51% of pressure drops in stenosed arterial segments.
  • Accurately identified high wall shear stress distributions in diseased arterial segments.

Conclusions:

  • The developed "tier-and-cluster" framework, driven by deep learning Pcd-SSM, offers a unified and transferable approach for analyzing vascular morphology and blood flow.
  • This method provides a robust tool for population-level blood flow studies, risk stratification, pathological mechanism exploration, and intervention planning for vascular stenosis.