TY - GEN
T1 - Co-registration of pre- And post-stent intravascular OCT images for validation of finite element model simulation of stent expansion
AU - Gharaibeh, Yazan
AU - Lee, Juhwan
AU - Prabhu, David
AU - Dong, Pengfei
AU - Zimin, Vladislav N.
AU - Dallan, Luis A.
AU - Bezerra, Hiram
AU - Gu, Linxia
AU - Wilson, David
N1 - Funding Information:
This project was supported by the National Heart, Lung, and Blood Institute through U.S. National Institutes of Health (NIH) Grants R21HL108263, R01HL114406, and R01HL143484, by NIH construction Grant (C06 RR12463), and by the Choose Ohio First Scholarship. These grants were attained via collaboration between Case Western Reserve University and University Hospitals of Cleveland. The content of this report is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The grants were obtained via collaboration between Case Western Reserve University and University Hospitals of Cleveland. This work made use of the High-Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. The veracity guarantor, Juhwan Lee, affirms to the best of his knowledge that all aspects of this paper are accurate.
Publisher Copyright:
© 2020 SPIE
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Coronary calcification
KW - Deep learning
KW - Finite element model (FEM)
KW - Image registration
KW - Intravascular optical coherence tomography (IVOCT)
KW - Stent deployment results
KW - Vascular imaging
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U2 - 10.1117/12.2550212
DO - 10.1117/12.2550212
M3 - Conference contribution
C2 - 35291699
AN - SCOPUS:85105573451
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Krol, Andrzej
A2 - Gimi, Barjor S.
PB - SPIE
T2 - Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Y2 - 18 February 2020 through 20 February 2020
ER -