To assess the progression of coronary artery disease, Optical Coherence Tomography (OCT) pullbacks acquired at different timepoints should be compared. However, the assessment of temporal sequences is a difficult task, as motion artifacts in the longitudinal and axial plane can decrease the quality of the manual inspection. To address this challenge, the current study presents a two-stage computational framework for the longitudinal and axial registration of two OCT pullbacks. During the first stage of the process, we focus on the accurate detection of the matching image pairs from the respective series, while during the second stage we focus on the axial registration of the matched pairs so that their common features are aligned. The dataset used includes 19 patients from two clinical centers, with two OCT pullbacks per patient: one before the stent implantation procedure and one after it. We applied our method on a synthetic dataset of OCT pullbacks, which was generated based on the in-vivo OCT pullbacks to reproduce the motion artifacts across the planes. In addition, the proposed method was validated on the 19 pairs of in-vivo OCT pullbacks with annotated pre/post stent deployment data. The method was able to reduce the alignment error from 32.17±26.14 to 5.6±6.6 frames, the rotational error from 11.59°±11.22° to 1.18°±0.81° and the distance error from 3.07mm±1.52mm to 0.46mm±0.44mm. In addition, the mean Mutual Information similarity increased by 13.47% after the longitudinal registration and an additional 123.33% after the axial registration on top of the previous one.
Bibliographical noteFunding Information:
This work is funded by the European Commission : Project InSilc: In-silico trials for drug-eluting BVS design, development and evaluation (GA number: 777119 ).
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