title: Automatic three-dimensional registration of intravascular optical coherence tomography images.
abstract: Intravascular optical coherence tomography (IV-OCT) is a catheter-based high-resolution imaging technique able to visualize the inner wall of the coronary arteries and implanted devices in vivo with an axial resolution below 20 μm. IV-OCT is being used in several clinical trials aiming to quantify the vessel response to stent implantation over time. However, stent analysis is currently performed manually and corresponding images taken at different time points are matched through a very labor-intensive and subjective procedure. We present an automated method for the spatial registration of IV-OCT datasets. Stent struts are segmented through consecutive images and three-dimensional models of the stents are created for both datasets to be registered. The two models are initially roughly registered through an automatic initialization procedure and an iterative closest point algorithm is subsequently applied for a more precise registration. To correct for nonuniform rotational distortions (NURDs) and other potential acquisition artifacts, the registration is consecutively refined on a local level. The algorithm was first validated by using an in vitro experimental setup based on a polyvinyl-alcohol gel tubular phantom. Subsequently, an in vivo validation was obtained by exploiting stable vessel landmarks. The mean registration error in vitro was quantified to be 0.14 mm in the longitudinal axis and 7.3-deg mean rotation error. In vivo validation resulted in 0.23 mm in the longitudinal axis and 10.1-deg rotation error. These results indicate that the proposed methodology can be used for automatic registration of in vivo IV-OCT datasets. Such a tool will be indispensable for larger studies on vessel healing pathophysiology and reaction to stent implantation. As such, it will be valuable in testing the performance of new generations of intracoronary devices and new therapeutic drugs.
authors.affiliation: Catholic University Leuven, Department of Cardiovascular Sciences, Herestraat 49, 3000 Leuven, Belgium.
authors.forename: Giovanni J
authors.initials: GJ
authors.lastname: Ughi
authors.forename: Tom
authors.initials: T
authors.lastname: Adriaenssens
authors.forename: Matilda
authors.initials: M
authors.lastname: Larsson
authors.forename: Christophe
authors.initials: C
authors.lastname: Dubois
authors.forename: Peter R
authors.initials: PR
authors.lastname: Sinnaeve
authors.forename: Mark
authors.initials: M
authors.lastname: Coosemans
authors.forename: Walter
authors.initials: W
authors.lastname: Desmet
authors.forename: Jan
authors.initials: J
authors.lastname: D'hooge
mesh_terms: D000465:Algorithms; D003324:Coronary Artery Disease / Q000473:pathology* / Q000601:surgery*; D003331:Coronary Vessels / Q000473:pathology* / Q000601:surgery*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D041623:Tomography, Optical Coherence / Q000379:methods*