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4D Vessel Reconstruction for Benchtop Thrombectomy Analysis

arXiv 2604.06671 ↗First posted 8 Apr 2026eess.IV cs.CV physics.med-phcited by 0

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Abstract (arXiv)

Introduction: Mechanical thrombectomy can cause vessel deformation and procedure-related injury. Benchtop models are widely used for device testing, but time-resolved, full-field 3D vessel-motion measurements remain limited. Methods: We developed a nine-camera, low-cost multi-view workflow for benchtop thrombectomy in silicone middle cerebral artery phantoms (2160p, 20 fps). Multi-view videos were calibrated, segmented, and reconstructed with 4D Gaussian Splatting. Reconstructed point clouds were converted to fixed-connectivity edge graphs for region-of-interest (ROI) displacement tracking and a relative surface-based stress proxy. Stress-proxy values were derived from edge stretch using a Neo-Hookean mapping and reported as comparative surface metrics. A synthetic Blender pipeline with known deformation provided geometric and temporal validation. Results: In synthetic bulk translation, the stress proxy remained near zero for most edges (median ≈ 0 MPa; 90th percentile 0.028 MPa), with sparse outliers. In synthetic pulling (1-5 mm), reconstruction showed close geometric and temporal agreement with ground truth, with symmetric Chamfer distance of 1.714-1.815 mm and precision of 0.964-0.972 at τ = 1 mm. In preliminary benchtop comparative trials (one trial per condition), cervical aspiration catheter placement showed higher max-median ROI displacement and stress-proxy values than internal carotid artery terminus placement. Conclusion: The proposed protocol provides standardized, time-resolved surface kinematics and comparative relative displacement and stress proxy measurements for thrombectomy benchtop studies. The framework supports condition-to-condition comparisons and methods validation, while remaining distinct from absolute wall-stress estimation. Implementation code and example data are available at https://ethanuser.github.io/vessel4D

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References · 12

  1. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  2. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,157
  3. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 616
  4. 2023Tracking Anything with Decoupled Video Segmentationcited by 197
  5. 2023Putting the Object Back into Video Object Segmentationcited by 188
  6. 2024SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memorycited by 94
  7. 2024Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenescited by 22
  8. 2024DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splattingcited by 19
  9. 2025GauSTAR: Gaussian Surface Tracking and Reconstructioncited by 13
  10. 2024MaGS: Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splattingcited by 9
  11. 2019Temporally Coherent General Dynamic Scene Reconstructioncited by 6
  12. 2025SWiT-4D: Sliding-Window Transformer for Lossless and Parameter-Free Temporal 4D Generationcited by 2

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