Co-registration of pre- And post-stent intravascular OCT images for validation of finite element model simulation of stent expansion

Yazan Gharaibeh, Juhwan Lee, David Prabhu, Pengfei Dong, Vladislav N. Zimin, Luis A. Dallan, Hiram Bezerra, Linxia Gu, David Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Intravascular optical coherence tomography (IVOCT) provides high-resolution images of coronary calcifications and detailed measurements of acute stent deployment following stent implantation. Since pre- and post-stent IVOCT image “pull-back” acquisitions start from different locations, registration of corresponding pullbacks is needed for assessing treatment outcomes. In particular, we are interested in assessing finite element model (FEM) prediction of lumen gain following stenting, requiring registration. We used deep learning to segment calcifications in corresponding pre- and post-stent IVOCT pullbacks. We created 1D representations of calcium thickness as a function of the angle of the helical IVOCT scans. Registration of two scans was done by maximizing the cross correlation of these two 1D representations. Registration was accurate, as determined by visual comparisons of 2D image frames. We used our pre-stent calcification segmentations to create a lesion-specific FEM, which took into account balloon size, balloon pressure, and stent measurements. We then compared simulated lumen gain from FEM analysis to actual stent deployment results. Actual lumen gain across ~200 registered pre and post-stent images was 1.52 ± 0.51, while FEM prediction was 1.43 ± 0.41. Comparison between actual and FEM results showed no significant difference (p < 0.001), suggesting accurate prediction of FEM modeling. Registered image data showed good visual agreement regarding lumen gain and stent strut malapposition. Hence, we have developed a platform for evaluation of FEM prediction of lumen gain. This platform can be used to guide development of FEM prediction software, which could ultimately help physicians with stent treatment planning of calcified lesions.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsAndrzej Krol, Barjor S. Gimi
PublisherSPIE
ISBN (Electronic)9781510634015
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 18 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11317
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States
CityHouston
Period2/18/202/20/20

Keywords

  • Coronary calcification
  • Deep learning
  • Finite element model (FEM)
  • Image registration
  • Intravascular optical coherence tomography (IVOCT)
  • Stent deployment results
  • Vascular imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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