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Updated: Aug 19, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training
Published on: January 18, 2021
3D elastic registration of vessel structures from IVUS data on biplane angiography
Benoit Godbout1, Jacques A de Guise, Gilles Soulez
1Laboratoire de Recherche en Imagerie et Orthopédie, CRCHUM, 1560 Sherbooke Est (Y-1615), Montréal, Quebec H2L-4M1, Canada.
Rationale And Objectives:
Planar angiograms and intravascular ultrasound (IVUS) imaging provide important insight for the evaluation of atherosclerotic diseases and blood flow abnormalities. The construction of realistic three-dimensional models is essential to efficiently follow the progression of arterial plaque. This requires an explicit localization of IVUS frames from angiograms. Because of the difficulties encountered when trying to track the position of an IVUS transducer, we propose an elastic registration approach that relies on a virtual catheter path.
Materials And Methods:
Deformable surface models of the lumen and external wall are constructed from segmented IVUS contours. A crude registration is obtained using a three-dimensional vessel centerline, reconstructed from two calibrated angiograms. Robust optimization of the virtual catheter path, position, absolute orientation, and regulation of the external wall shape is performed until near-perfect alignment of the back-projected silhouettes on image edges is reached.
Results:
Visual assessment of the reconstructed vessels showed a good superposition of virtual models on the angiograms. We measured a 0.4-mm residual error value. A preliminary study of convergence properties on 15 datasets showed that initial absolute orientation may affect the solution. However, for follow-ups, coherent solutions were found among datasets.
Conclusion:
The advantages of the virtual catheter path approach are demonstrated. Future work will look at ways to single out the true solution with a better use of the available information in both modalities and additional validation studies on improved datasets.
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