Correction of metallic stent struts and guide wire shadows in intravascular optical coherence tomography images using conditional generative adversarial networks

Yazan Gharaibeh; Juhwan Lee; Chaitanya Kolluru; Vladislav N. Zimin; Luis A. P. Dallan; Hiram G. Bezerra; David L. Wilson

doi:10.1117/12.2582177

23 February 2021 Correction of metallic stent struts and guide wire shadows in intravascular optical coherence tomography images using conditional generative adversarial networks

Yazan Gharaibeh, Juhwan Lee, Chaitanya Kolluru, Vladislav N. Zimin, Luis A. P. Dallan, Hiram G. Bezerra, David L. Wilson

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115981U (2021) https://doi.org/10.1117/12.2582177
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Intravascular optical coherence tomography (IVOCT) images have the necessary resolution and contrast to assess atherosclerotic plaque, and our group has created machine/deep learning techniques to analyze plaques in pre-stent IVOCT images. Such software can be used to aid numerous research studies and clinical treatment planning. Here we extend our work to images obtained following stent implantation, where opaque metallic stent struts obstruct the view of portions of the plaque, making it difficult to visualize/analyze plaques. We created a generative adversarial network (GAN)-based method to fill in the missing image data. Applications include analysis of plaque progression/regression behind the stent to evaluate drugs, drug eluting stents, and the anatomical registration of pre- and post-stent image data for further post stent optimization. We used conditional GAN (cGAN) to correct the presence of the guide wire and stent struts shadows, creating full images for visualization and analyses. To train/test software, we created synthetic post-stent images by interjecting realistic stent strut shadows in images without a stent in place. Results show the capability of cGAN to generate plausible and realistic images. To assess results, we used deep learning segmentation models to segment calcifications in corrected, synthesized images and compared results to the original images with stent. DICE scores were typically above 0.79 ± 0.03 with correction and 0.71 ± 0.02 without correction. The co-registration errors improved to be ranged between 0.52 mm to 0.69 mm. Compared with images with shadows, the new method offers lower errors in both location and orientation registration.

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Yazan Gharaibeh, Juhwan Lee, Chaitanya Kolluru, Vladislav N. Zimin, Luis A. P. Dallan, Hiram G. Bezerra, and David L. Wilson "Correction of metallic stent struts and guide wire shadows in intravascular optical coherence tomography images using conditional generative adversarial networks", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115981U (23 February 2021); https://doi.org/10.1117/12.2582177

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