Deep learning segmentation of coronary calcified plaque from intravascular optical coherence tomography (IVOCT) images with application to finite element modeling of stent deployment

Because coronary artery calcified plaques can hinder or eliminate stent deployment, interventional cardiologists need a better way to plan interventions, which might include one of the many methods for calcification modification (e.g., atherectomy). We are imaging calcifications with intravascular optical coherence tomography (IVOCT), which is the lone intravascular imaging technique with the ability to image the extent of a calcification, and using results to build vessel-specific finite element models for stent deployment. We applied methods to a large set of image data (\u3e45 lesions and \u3e 2,600 image frames) of calcified plaques, manually segmented by experts into calcified, lumen and “other” tissue classes. In optimization experiments, we evaluated anatomical (x, y) versus acquisition (r,θ) views, augmentation methods, and classification noise cleaning. Noisy semantic segmentations are cleaned by applying a conditional random field (CRF). We achieve an accuracy of 0.85 ± 0.04, 0.99 ± 0.01, and 0.97 ± 0.01, and F-score of 0.88 ± 0.07, 0.97 ± 0.01, and 0.91 ± 0.04 for calcified, lumen, and other tissues classes respectively across all folds following CRF noise cleaning. As a proof of concept, we applied our methods to cadaver heart experiments on highly calcified plaques. Following limited manual correction, we used our calcification segmentations to create a lesion-specific finite element model (FEM) and used it to predict direct stenting deployment at multiple pressure steps. FEM modeling of stent deployment captured many features found in the actual stent deployment (e.g., lumen shape, lumen area, and location and number of apposed stent struts)

Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries

Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360 IVOCT image frames of 77 lesions among 41 patients. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, theta) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7+/-17 degree; mean 196 degree). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2+/-14.6 micron; mean 175 micron), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.Comment: 18 pages, 9 figure

Should Research Thesis be a Prerequisite for Doctor of Medicine Degree? A Cross-sectional Study at Jordan University of Science and Technology

<p><strong>Background: </strong>University based research is an integral part of many prestigious medical schools worldwide. The benefits of student-conducted research have long been highlighted in the literature. This article aims to identify the insights of medical students concerning research training, including perceived hurdles in the way of conducting research, and the utility of a research thesis in acquiring a Doctor of Medicine degree.</p><p><strong>Methods:</strong> A total of 808 medical students at Jordan University of Science and Technology were selected by random sampling with a confidence level of 95%. A survey was constructed by a group of students through literature review and group discussions. The survey utilized polar and Likert scale questions to collect data from the students. Statistical inferences were then obtained through analysis of means and one sample t-test of the hypothesis.</p><p><strong>Results:</strong> A total of 687 students filled out the survey (85%). Analysis shows that respondents have a strong and positive attitude towards research. The respondents with past research experience constituted 14.3% of those surveyed. Those respondents identified the barriers faced by them during their experience. The students showed high degree of agreement that a research thesis should be a prerequisite for graduation with statistical significance of p-value ≤0.05.</p><p><strong>Conclusion: </strong>Modifying the curriculum to include research methodology is recommended, and developing it to incorporate a thesis as a requirement for graduation may be advised upon further review.</p

Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches

The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning

Degradation modeling of poly-l-lactide acid (PLLA) bioresorbable vascular scaffold within a coronary artery

In this work, a strain-based degradation model was implemented and validated to better understand the dynamic interactions between the bioresorbable vascular scaffold (BVS) and the artery during the degradation process. Integrating the strain-modulated degradation equation into commercial finite element codes allows a better control and visualization of local mechanical parameters. Both strut thinning and discontinuity of the stent struts within an artery were captured and visualized. The predicted results in terms of mass loss and fracture locations were validated by the documented experimental observations. In addition, results suggested that the heterogeneous degradation of the stent depends on its strain distribution following deployment. Degradation is faster at the locations with higher strains and resulted in the strut thinning and discontinuity, which contributes to the continuous mass loss, and the reduced contact force between the BVS and artery. A nonlinear relationship between the maximum principal strain of the stent and the fracture time was obtained, which could be transformed to predict the degradation process of the BVS in different mechanical environments. The developed computational model provided more insights into the degradation process, which could complement the discrete experimental data for improving the design and clinical management of the BVS

Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,&theta;) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 &plusmn; 0.10 and pixel-wise sensitivity/specificity of 87.7 &plusmn; 6.6%/99.8 &plusmn; 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 &plusmn; 0.3%, specificity of 98.8 &plusmn; 1.0%, and accuracy of 99.1 &plusmn; 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning

Racial and Ethnic Disparities in Peripheral Vascular Disease Admissions Using a Nationally Representative Sample

Our study aimed to identify clinical outcomes and resource utilization associated with race and ethnicity in patients admitted with peripheral vascular disease (PVD) across the United States. We queried the National Inpatient Sample database from 2015 to 2019 and identified 622,820 patients admitted with PVD. Patients across 3 major race and ethnic categories were compared in terms of baseline characteristics, inpatient outcomes, and resource utilization. Black and Hispanic patients were more likely to be younger and of the lowest median income but incur higher total hospital costs. Black race predicted higher rates of acute kidney injury, need for blood transfusion, and need for vasopressor but lower rates of circulatory shock, and mortality. Black and Hispanic patients were less likely to undergo limb-salvaging procedures and more likely to undergo amputation than White patients. In conclusion, our findings indicate that Black and Hispanic patients experience health disparities in resource utilization and inpatient outcomes for PVD admissions