Transformer-Based Prediction of Coronary Artery Lumen
Expansion Post Angioplasty Using Optical Coherence
Tomography
Name
gupta-shreyag-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
7.18 MB
Format
Adobe PDF
Checksum (MD5)
d7d27b14ae601e4bc5efefe8b8da44f1
Author(s)
Gupta, Shreya
Advisor(s)
Edelman, Elazer R.
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Coronary artery disease is the leading cause of mortality globally, resulting in an urgent and critical need to better understand both vessel morphology and the processes of intervention. Angioplasty is an intervention which causes a previously constricted vessel to expand via placement of a stent, and is affected by numerous characteristics of the vessel such as calcium eccentricity and size, wall thickness, and prior lumen size. Being able to accurately assess whether a stent will properly expand allows cardiologists to pursue pre-stenting calcium lesion modification strategies that help avoid dangerous complications of improper stenting. This work introduces a pipeline for post-stenting lumen area prediction from pre-stenting optical coherence tomography (OCT) images. This pipeline includes morphological correction of OCT image segmentations, explainable feature extraction from OCT segmentations, and a predictive transformer network that combines morphological features with injected stent information. The aim is for such a pipeline to be used to support clinical decision making.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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