ArticleJournal of medical imaging (Bellingham, Wash.)2016
Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images.Biomedical optics express · 2019Article
- Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets.Journal of biomedical optics · 2019Article
- Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment.Journal of biomedical optics · 2018Article
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Authors and funding
14 authors.
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Abstract
Evidence suggests high-resolution, high-contrast, [Formula: see text] intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and three-dimensional (3-D) registration methods to provide validation of IVOCT pullback volumes using microscopic, color, and fluorescent cryo-image volumes with optional registered cryo-histology. A specialized registration method matched IVOCT pullback images acquired in the catheter reference frame to a true 3-D cryo-image volume. Briefly, an 11-parameter registration model including a polynomial virtual catheter was initialized within the cryo-image volume, and perpendicular images were extracted, mimicking IVOCT image acquisition. Virtual catheter parameters were optimized to maximize cryo and IVOCT lumen overlap. Multiple assessments suggested that the registration error was better than the [Formula: see text] spacing between IVOCT image frames. Tests on a digital synthetic phantom gave a registration error of only [Formula: see text] (signed distance). Visual assessment of randomly presented nearby frames suggested registration accuracy within 1 IVOCT frame interval ([Formula: see text]). This would eliminate potential misinterpretations confronted by the typical histological approaches to validation, with estimated 1-mm errors. The method can be used to create annotated datasets and automated plaque classification methods and can be extended to other intravascular imaging modalities.
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