Constraining OCT with Knowledge of Device Design Enables High Accuracy Hemodynamic Assessment of Endovascular Implants

Abstract

BackgroundStacking cross-sectional intravascular images permits three-dimensional rendering of endovascular implants, yet introduces between-frame uncertainties that limit characterization of device placement and the hemodynamic microenvironment. In a porcine coronary stent model, we demonstrate enhanced OCT reconstruction with preservation of between-frame features through fusion with angiography and a priori knowledge of stent design.Methods and ResultsStrut positions were extracted from sequential OCT frames. Reconstruction with standard interpolation generated discontinuous stent structures. By computationally constraining interpolation to known stent skeletons fitted to 3D ‘clouds’ of OCT-Angio-derived struts, implant anatomy was resolved, accurately rendering features from implant diameter and curvature (n = 1 vessels, r2 = 0.91, 0.90, respectively) to individual strut-wall configurations (average displacement error ~15 μm). This framework facilitated hemodynamic simulation (n = 1 vessel), showing the critical importance of accurate anatomic rendering in characterizing both quantitative and basic qualitative flow patterns. Discontinuities with standard approaches systematically introduced noise and bias, poorly capturing regional flow effects. In contrast, the enhanced method preserved multi-scale (local strut to regional stent) flow interactions, demonstrating the impact of regional contexts in defining the hemodynamic consequence of local deployment errors.ConclusionFusion of planar angiography and knowledge of device design permits enhanced OCT image analysis of in situ tissue-device interactions. Given emerging interests in simulation-derived hemodynamic assessment as surrogate measures of biological risk, such fused modalities offer a new window into patient-specific implant environments.

Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

“…Detection of struts and lumen border is crucial for studying the efficacy of endovascular devices by building patient-specific 3-D models and performing biomechanical simulations. 35 The validation results of the proposed methodology show that it can be used as a foundation for creating such models to mechanistically study and understand the effect of polymeric scaffolds on cardiovascular function. A holistic insight into cardiovascular function will allow us to move toward a continuum of more reliable and effective patient-specific treatment strategies.…”

“…Given the high PPV of the strut (0.93) and lumen detection (0.94) methodology, our proposed algorithm in conjunction with manual calibration holds promise of producing realistic 3-D geometric models of the scaffolded coronary artery. 35 We also applied our algorithm to images with excess noise and blood artifacts in the lumen and found accurate detection results [Figs. 13(a) and 13(b)].…”

Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images

“…To achieve this, the three-dimensional (3D) configuration of BVS and arterial lumen is needed. Using the proposed method, the BVS struts can be detected in OCT frames and then translated in 3D using well-known 3D OCT reconstruction algorithms [21], [22]. Building patient-specific 3D models will provide clinicians with accurate quantitative metrics to assess patient cardiovascular function post BVS implantation.…”

Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses

“…Table 1 reports a list as comprehensive as possible of the studies published on this topic. While several studies presented a detection algorithm of the lumen contours [ 13 , 15 , 18 , 19 , 22 , 26 , 30 , 31 , 35 , 36 , 39 , 40 ] or the stent struts [ 20 , 21 , 23 , 24 , 27 , 29 , 32 ] only, in other works both detection algorithms were developed [ 14 , 16 , 17 , 25 , 28 , 33 , 34 , 37 , 38 , 41 , 42 ].…”

“…Nam et al [ 37 ] extracted the lumen contour by detecting and modifying a temporary lumen contour, which was obtained by connecting inward points at 20% of the A-scans’ maximum intensity. O’Brien et al [ 38 ] first classified the A-scans of each OCT frame as belonging to a lumen using features extracted from the “scale-space signature” of the A-scan signal. This signature was generated by convolving the A-scan signal with a specific mother wavelet.…”