Intravascular optical coherence tomography (IVOCT) is an intravascular imaging modality which enables the visualization arterial structures at the micro-structural level. The interpretations of these structures is mainly on the basis of relative image intensities. However, even for homogeneous tissue light intensities can differ. In this study the incident light intensity is modeled to be related to the catheter position. Two factors, the distance between catheter and inner lumen wall as well as the incident angle of the light upon the lumen wall, are considered. A three-level hierarchical model is constructed to statistically validate this model to include the potential effect of different pullbacks and/or frame numbers. The model is solved using 169 images out of 9 pull-backs recorded with a St.Jude Medical IVOCT system. F-tests results indicate that both the distance and the incident angle contribute to the model statistically significantly with p < 0.001. Based on the results from the statistical analysis, a potential compensation method is introduced to normalize the IVOCT intensities for the catheter position effects and small shadows

Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.