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A parallelized 4D reconstruction algorithm for vascular structures and motions based on energy optimization

Authors:

Hong QinAuthors Info & Claims

The Visual Computer: International Journal of Computer Graphics, Volume 31, Issue 11

Pages 1431 - 1446

https://doi.org/10.1007/s00371-014-1024-4

Published: 01 November 2015 Publication History

Abstract

In this paper, we present a parallel 4D vessel reconstruction algorithm that simultaneously recovers 3D structure, shape, and motion based on multiple views of X-ray angiograms. The fundamental goal is to assist the analysis and diagnosis of interventional surgery in the most efficient way towards interactive and accurate performance. We start with a fully parallelized algorithm to extract vessels as well as their skeletons and topologies from dynamic image sequences. Then, instead of resorting to registration, we present an algorithm to formulate the reconstruction problem as an energy minimization problem with color, coherence, and topology constraints to reconstruct the 3D vessel initially, which is robust to combat noise and incomplete information in images. Next, we incorporate temporal information into our energy optimization framework to track and reconstruct 4D kinematics of the dynamic vessels, which is also capable of recovering previous incomplete and misleading shapes acquired from static images otherwise. We demonstrate our system in coronary arteries reconstruction and movement tracking for percutaneous coronary intervention surgery to help medical practitioners learn about the 3D shapes and their motions of the coronary arteries of specific patient. We envision that our system would be of high assistance for diagnosis and therapy to treat vessel-related diseases in a clinical setting in the near future.

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Cited By

Varga LLékó GBalázs P(2022)Grayscale uncertainty and errors of tomographic reconstructions based on projection geometries and projection setsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02428-y39:4(1557-1569)Online publication date: 21-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-022-02428-y
Lukić TBalázs P(2022)Limited-view binary tomography reconstruction assisted by shape centroidThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-02044-838:2(695-705)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-020-02044-8
Szirmay-Kalos LKacsó ÁMagdics MTóth B(2022)Robust compartmental model fitting in direct emission tomography reconstructionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-02041-x38:2(655-668)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-020-02041-x
Show More Cited By

A parallelized 4D reconstruction algorithm for vascular structures and motions based on energy optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics

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cover image The Visual Computer: International Journal of Computer Graphics

The Visual Computer: International Journal of Computer Graphics Volume 31, Issue 11

November 2015

135 pages

ISSN:0178-2789

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Copyright © Copyright © 2015 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2015

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Cited By

Varga LLékó GBalázs P(2022)Grayscale uncertainty and errors of tomographic reconstructions based on projection geometries and projection setsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02428-y39:4(1557-1569)Online publication date: 21-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-022-02428-y
Lukić TBalázs P(2022)Limited-view binary tomography reconstruction assisted by shape centroidThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-02044-838:2(695-705)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-020-02044-8
Szirmay-Kalos LKacsó ÁMagdics MTóth B(2022)Robust compartmental model fitting in direct emission tomography reconstructionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-02041-x38:2(655-668)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-020-02041-x
Szűcs JBalázs P(2021)Local Q-concavity histograms for binary image classification and reconstructionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02290-438:12(4221-4234)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1007/s00371-021-02290-4
Chen CLi SWang YQin HHao A(2017)Video Saliency Detection via Spatial-Temporal Fusion and Low-Rank Coherency DiffusionIEEE Transactions on Image Processing10.1109/TIP.2017.267014326:7(3156-3170)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2670143

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