Purpose: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. Methods: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. Results: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were 29±46μm for the intima–media interface, 30±50μm for the media–adventitia interface, and 50±64μm for the adventitia–periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78–0.98). Conclusion: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.

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Purpose: Identification of rupture-prone plaques in coronary arteries is a major clinical challenge. Fibrous cap thickness and wall shear stress are two relevant image-based risk factors, but these two parameters are generally computed and analyzed separately. Accordingly, combining these two parameters can potentially improve the identification of at-risk regions. Therefore, the purpose of this study is to investigate the feasibility of the fusion of wall shear stress and fibrous cap thickness of coronary arteries in patient data. Methods: Fourteen patients were included in this pilot study. Imaging of the coronary arteries was performed with optical coherence tomography and with angiography. Fibrous cap thickness was automatically quantified from optical coherence tomography pullbacks using a contour segmentation approach based on fast marching. Wall shear stress was computed by applying computational fluid dynamics on the 3D volume reconstructed from two angiograms. The two parameters then were co-registered using anatomical landmarks such as side branches. Results: The two image modalities were successfully co-registered, with a mean (±SD) error corresponding to 8.6±6.7% of the length of the analyzed region. For all the analyzed participants, the average thinnest portion of each fibrous cap was 129±69μm, and the average WSS value at the location of the fibrous cap was 1.46±1.16Pa. A unique index was finally generated for each patient via the fusion of fibrous cap thickness and wall shear stress measurements, to translate all the measured parameters into a single risk map. Conclusion: The introduced risk map integrates two complementary parameters and has potential to provide valuable information about plaque vulnerability.@en

Objectives: Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods: A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results: Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22±18μm,R=.73) and were similar to inter-observer reproducibility (21±19μm, R = .74), while being significantly faster and fully reproducible. Conclusion: The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.@en

Rationale and Objectives: Magnetic resonance angiography (MRA) is a well-established modality for the assessment of renal artery stenosis. Using dedicated quantitative analyses, MRA can become a useful tool for assessing renal artery dimensions in patients referred for renal sympathetic denervation (RDN) and for providing accurate measurements of vascular response after RDN. The purpose of this study was to test the reproducibility of a novel MRA quantitative imaging tool and to validate these measurements against intravascular ultrasound (IVUS). Materials and Methods: In nine patients referred for renal denervation, renal artery dimensions were measured. Bland-Altman analysis was used to assess the intraobserver and interobserver reproducibility. Results: Mean lumen diameter was 5.8 ± 0.7 mm, with a very good intraobserver and interobserver variability of 0.7% (reproducibility: bias, 0 mm; standard deviation [SD], 0.1 mm) and 1.2% (bias, 0 mm; SD, 0.1 mm), respectively. Mean total lumen volume was 1035.3 ± 403.6 mm3 with good intraobserver and interobserver variability of 2.9% (bias, -9.7 mm3; SD, 34.0 mm3) and 2.8% (bias, -11.4 mm3; SD, 42.4 mm3). The correlation (Pearson R) between mean lumen diameter measured with MRA and IVUS was 0.750 (P = .002). Conclusions: Using a novel MRA quantitative imaging tool, renal artery dimensions can be measured with good reproducibility and accuracy. MRA-derived diameters and volumes correlated well with IVUS measurements.@en