Three-Dimensional Intravascular Optical Coherence Tomography Rendering Assessment of Spontaneous Coronary Artery Dissection Concomitant With Left Main Ostial Critical Stenosis

Three-dimensional imaging of pulmonary arterial vasa vasorum using optical coherence tomography in patients after bidirectional Glenn and Fontan procedures

AIMS

We evaluated pulmonary arterial (PA) vasa vasorum (VV) in Fontan candidate patients with a novel three-dimensional (3D) imaging technique using optical coherence tomography (OCT).

METHODS AND RESULTS

This prospective study assessed the development of adventitial VV in the distal PA of 10 patients with bidirectional Glenn circulation (BDG group, 1.6 ± 0.3 years) and Fontan circulation (Fontan group, 3.3 ± 0.3 years), and in 20 children with normal PA haemodynamics and morphology (Control group, 1.5 ± 0.3 years). We assessed the PA VV with two-dimensional (2D) cross-sectional, multi-planar reconstruction (MPR), and volume rendering (VR) imaging. VV development was evaluated by the VV area/volume ratio, defined as the VV area/volume divided by the adventitial area/volume. Compared to the control group, the observed VV number and diameter on 3D images of MPR and VR were significantly higher, and curved and torturous-shaped VV were more frequently observed in the BDG and Fontan groups (P < 0.001, all). The median VV volume ratio was significantly greater in the BDG than in the control group (3.38% vs. 0.61%; P < 0.001). Although the VV volume ratio decreased significantly after the Fontan procedure (2.64%, P = 0.005 vs. BDG), the ratio remained higher than in the control group (P < 0.001 vs. control).

CONCLUSION

3D OCT imaging is a novel method that can be used to evaluate adventitial PA VV and may provide pathophysiological insight into the role of the PA VV in these patients.

A novel automated lumen segmentation and classification algorithm for detection of irregular protrusion after stents deployment

BACKGROUND

Clinically, irregular protrusions and blockages after stent deployment can lead to significant adverse outcomes such as thrombotic reocclusion or restenosis. In this study, we propose a novel fully automated method for irregular lumen segmentation and normal/abnormal lumen classification.

METHODS

The proposed method consists of a lumen segmentation, feature extraction, and lumen classification. In total, 92 features were extracted to classify normal/abnormal lumen. The lumen classification method is a combination of supervised learning algorithm and feature selection that is a partition-membership filter method.

RESULTS

As the results, our proposed lumen segmentation method obtained the average of dice similarity coefficient (DSC) and the accuracy of proposed features and the random forest (RF) for normal/abnormal lumen classification as 97.6% and 98.2%, respectively.

CONCLUSIONS

Therefore, we can lead to better understanding of the overall vascular status and help to determine cardiovascular diagnosis.

Comprehensive intravascular imaging of atherosclerotic plaque in vivo using optical coherence tomography and fluorescence lifetime imaging

Comprehensive imaging of both the structural and biochemical characteristics of atherosclerotic plaque is essential for the diagnosis and study of coronary artery disease because both a plaque's morphology and its biochemical composition affect the level of risk it poses. Optical coherence tomography (OCT) and fluorescence lifetime imaging (FLIm) are promising optical imaging methods for characterizing coronary artery plaques morphologically and biochemically, respectively. In this study, we present a hybrid intravascular imaging device, including a custom-built OCT/FLIm system, a hybrid optical rotary joint, and an imaging catheter, to visualize the structure and biochemical composition of the plaque in an atherosclerotic rabbit artery in vivo. Especially, the autofluorescence lifetime of the endogenous tissue molecules can be used to characterize the biochemical composition; thus no exogenous contrast agent is required. Also, the physical properties of the imaging catheter and the imaging procedures are similar to those already used clinically, facilitating rapid translation into clinical use. This new intravascular imaging catheter can open up new opportunities for clinicians and researchers to investigate and diagnose coronary artery disease by simultaneously providing tissue microstructure and biochemical composition data in vivo without the use of exogenous contrast agent.

Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage

PURPOSE

Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the microstructure of arterial walls in vivo. IV-OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in-stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV-OCT images.

METHODS

The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV-OCT readers.

RESULTS

Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case-by-case validation also showed comparable accuracy for most cases. High correlation coefficients (R > 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 μm, respectively) and a narrow range of limits of agreement (36 and 54 μm, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage.

CONCLUSIONS

The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent-related side effects, including in-stent thrombosis and in-stent restenosis.