Lumen Boundaries Research Articles

Exploring hemodynamic mechanisms and re-intervention strategies for partial false lumen thrombosis in Stanford type B aortic dissection after thoracic aortic endovascular repair

ObjectivesFalse lumen (FL) thrombosis status for Stanford type B aortic dissection (TBAD) after thoracic endovascular aortic repair (TEVAR) is critical for evaluating aortic remodeling and long-term prognosis. This study aimed to monitor the morphology evolution of partial FL thrombosis (PFLT) and its hemodynamic conditions through an innovative approach, providing a re-intervention strategy from both morphologic and hemodynamic perspectives. MethodsThree-dimensional geometries are extracted from a five-year follow-up of CTA images for TBAD after TEVAR. The morphology and hemodynamics of PFLT are comprehensively analyzed based on patient-specific reconstructions and computational fluid dynamics (CFD). The impact of various strategies treating risk factors of PFLT, including proximal entry closure, left renal artery stenting, or accessory renal artery embolism on hemodynamics is assessed. ResultsThe introduced morphologic approaches appropriately reflected the evolution of PFLT. Gradual dilation of FL (surface area from 82.63cm2 to 98.84cm2, volume from 45.12 mL to 63.40 mL, increase in distal tear (from 3.72 cm to 4.32 cm), and fluctuation of thrombosis-blood lumen boundary are observed. For further surgical preparation in the absence of unanimously recognized re-intervention indicators, velocity and wall shear stress distributions reveal different simulated re-interventions have distinctly suppressive effects on hemodynamic conditions within the PFLT, providing valuable insights for further surgical preparation. ConclusionsThe present study demonstrates a re-intervention strategy for PFLT in TBAD patients after TEVAR utilizing morphologic and hemodynamic analyses. Acknowledging the deterioration of PFLT may result in adverse long-term outcomes, this strategy might offer an alternative approach for clinical monitoring and management of related patients.

Efficient and Accurate 3D Thickness Measurement in Vessel Wall Imaging: Overcoming Limitations of 2D Approaches Using the Laplacian Method.

The clinical significance of measuring vessel wall thickness is widely acknowledged. Recent advancements have enabled high-resolution 3D scans of arteries and precise segmentation of their lumens and outer walls; however, most existing methods for assessing vessel wall thickness are 2D. Despite being valuable, reproducibility and accuracy of 2D techniques depend on the extracted 2D slices. Additionally, these methods fail to fully account for variations in wall thickness in all dimensions. Furthermore, most existing approaches are difficult to be extended into 3D and their measurements lack spatial localization and are primarily confined to lumen boundaries. We advocate for a shift in perspective towards recognizing vessel wall thickness measurement as inherently a 3D challenge and propose adapting the Laplacian method as an outstanding alternative. The Laplacian method is implemented using convolutions, ensuring its efficient and rapid execution on deep learning platforms. Experiments using digital phantoms and vessel wall imaging data are conducted to showcase the accuracy, reproducibility, and localization capabilities of the proposed approach. The proposed method produce consistent outcomes that remain independent of centerlines and 2D slices. Notably, this approach is applicable in both 2D and 3D scenarios. It allows for voxel-wise quantification of wall thickness, enabling precise identification of wall volumes exhibiting abnormal wall thickness. Our research highlights the urgency of transitioning to 3D methodologies for vessel wall thickness measurement. Such a transition not only acknowledges the intricate spatial variations of vessel walls, but also opens doors to more accurate, localized, and insightful diagnostic insights.

Accurate Recognition of Vascular Lumen Region from 2D Ultrasound Cine Loops for Bubble Detection.

Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression sickness. To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble presence. This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this algorithm. Results A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82. It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.

Changes in aorta hemodynamics in Left-Right Type 1 bicuspid aortic valve patients after replacement with bioprosthetic valves: An in-silico study.

Bicuspid aortic valve (BAV) is the most common cardiac congenital abnormality with a high rate of concomitant aortic valve and ascending aorta (AAo) pathologic changes throughout the patient's lifetime. The etiology of BAV-related aortopathy was historically believed to be genetic. However, recent studies theorize that adverse hemodynamics secondary to BAVs also contribute to aortopathy, but their precise role, specifically, that of wall shear stress (WSS) magnitude and directionality remains controversial. Moreover, the primary therapeutic option for BAV patients is aortic valve replacement (AVR), but the role of improved post-AVR hemodynamics on aortopathy progression is also not well-understood. To address these issues, this study employs a computational fluid dynamics model to simulate personalized AAo hemodynamics before and after TAVR for a small cohort of 6 Left-Right fused BAV patients. Regional distributions of five hemodynamic metrics, namely, time-averaged wall shear stress (TAWSS) and oscillating shear index (OSI), divergence of wall shear (DWSS), helicity flux integral & endothelial cell activation potential (ECAP), which are hypothesized to be associated with potential aortic injury are computed in the root, proximal and distal ascending aorta. BAVs are characterized by strong, eccentric jets, with peak velocities exceeding 4 m/s and axially circulating flow away from the jets. Such conditions result in focused WSS loading along jet attachment regions on the lumen boundary and weaker, oscillating WSS on other regions. The jet attachment regions also show alternating streaks of positive and negative DWSS, which may increase risk for local tissue stretching. Large WSS magnitudes, strong helical flows and circumferential WSS have been previously implicated in the progression of BAV aortopathy. Post-intervention hemodynamics exhibit weaker, less eccentric jets. Significant reductions are observed in flow helicity, TAWSS and DWSS in localized regions of the proximal AAo. On the other hand, OSI increases post-intervention and ECAP is observed to be low in both pre- and post-intervention scenarios, although significant increases are also observed in this ECAP. These results indicate a significant alleviation of pathological hemodynamics post AVR.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning

BackgroundQuantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. MethodsAI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). ResultsAI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. ConclusionAI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Automatic classification and segmentation of atherosclerotic plaques in the intravascular optical coherence tomography (IVOCT)

Background and objectivesIntravascular optical coherence tomography (IVOCT) is capable of delineating peri-luminal region, including thin fibrous cap, calcium, lipid and thrombus. The segmentation result of these plaques is needed when calculating some useful diagnostic indicators, such as the minimum fiber cap thickness or the maximum Plaque Structural Stress (PSS), to help the diagnosis of vulnerable plaque. Since only some images contain plaques, in order to simplify the network architecture, we designed a three-step framework with single task for each step in this paper to realize the machine learning based pixel-level semantic segmentation of plaque in IVOCT. MethodsA three-step framework is designed: lumen segmentation, image classification and plaque semantic segmentation. Firstly, the lumen of IVOCT is segmented using U-Net. Then the patches are cropped along the lumen boundary, and the plaques in the patches are classified and merged to get whether the original image contains calcium or lipid plaque. In the classification procedure, a self-attention module is introduced into ResNet to form the improved self-attention ResNet. Finally, the selected images with plaque are segmented to get the pixel-level segmentation results of each plaque component, which is realized by an combined network composed by convolutional auto encoder and U-Net. Results and conclusionsIn the lumen segmentation step, the Dice coefficient is greater than 95%. In the classification step, the classification precision, sensitivity and specificity of calcium plaque are all 100%; and that of lipid plaque are 97%, 100% and 94% respectively. In the final segmentation step, the Dice coefficient of calcium plaque is 71.8% and that of lipid plaque is 60.5%; the sensitivity of calcium plaque is 78.4% and that of lipid plaque is 80.2%. The experiments show that compared to some commonly used networks the proposed method achieves better performance.

Detecting Cardio-Vascular Acoustics in an Incipient Turbulence

Cardiovascular flow maintains the integrity of animated life, especially of humans. This flow goes fuzzy in the event of turbulence. In pathological states, vascular stenosis is usually the culprit in turbulence. Whether turbulence ensues from a physiological state or else, there must be a concomitant sound that is at variance with the normative flow acoustics. This sound emanates from a source point which is more or less within the inception of stenosis. This paper aims to identify the vascular sound field that emanates from a source point, maybe, due to stenosis. As a prelude, it furnished the windowed equations of motion for a fluid (blood) occupying a vascular region to derive the fluid density form of the acoustic analogy. However, the parameter of higher interest in vascular acoustics is turbulent pressure. So, it was well considered, and its relationship with acceleration at the monitor points on the aortic surface was supplied. An aspect of this work is the analysis of heart murmur. The heart was presumed here to admit Kelvin-Voigt’s viscoelastic model and, therefore, the equations governing the model hold well in the present case. Details here suggest that the hemodynamic pressure fluctuation on the aortic lumen boundary and the concomitant turbulent flow precipitates aortic stenosis murmur.

Defective Thyroglobulin: Cell Biology of Disease.

The primary functional units of the thyroid gland are follicles of various sizes comprised of a monolayer of epithelial cells (thyrocytes) surrounding an apical extracellular cavity known as the follicle lumen. In the normal thyroid gland, the follicle lumen is filled with secreted protein (referred to as colloid), comprised nearly exclusively of thyroglobulin with a half-life ranging from days to weeks. At the cellular boundary of the follicle lumen, secreted thyroglobulin becomes iodinated, resulting from the coordinated activities of enzymes localized to the thyrocyte apical plasma membrane. Thyroglobulin appearance in evolution is essentially synchronous with the appearance of the follicular architecture of the vertebrate thyroid gland. Thyroglobulin is the most highly expressed thyroid gene and represents the most abundantly expressed thyroid protein. Wildtype thyroglobulin protein is a large and complex glycoprotein that folds in the endoplasmic reticulum, leading to homodimerization and export via the classical secretory pathway to the follicle lumen. However, of the hundreds of human thyroglobulin genetic variants, most exhibit increased susceptibility to misfolding with defective export from the endoplasmic reticulum, triggering hypothyroidism as well as thyroidal endoplasmic reticulum stress. The human disease of hypothyroidism with defective thyroglobulin (either homozygous, or compound heterozygous) can be experimentally modeled in thyrocyte cell culture, or in whole animals, such as mice that are readily amenable to genetic manipulation. From a combination of approaches, it can be demonstrated that in the setting of thyroglobulin misfolding, thyrocytes under chronic continuous ER stress exhibit increased susceptibility to cell death, with interesting cell biological and pathophysiological consequences.

Current research and future prospects of IVOCT imaging‐based detection of the vascular lumen and vulnerable plaque

Intravascular optical coherence tomography (IVOCT) is an imaging method that has developed rapidly in recent years and is useful in coronary atherosclerosis diagnosis. It is widely used in the assessment of vulnerable plaque. This review summarizes the main research methods used in recent years for blood vessel lumen boundary detection and segmentation and vulnerable plaque segmentation and classification. This article aims to comprehensively and systematically introduce the research progress on internal tissues of blood vessels based on IVOCT images. The characteristics and advantages of various methods have been summarized to provide theoretical ideas and methods for the reference of relevant researchers and scholars. This article is protected by copyright. All rights reserved.

A mid-fidelity numerical method for blood flow in deformable vessels

In this work, a novel fluid–structure interaction algorithm for the simulation of blood flow in three-dimensional deformable vessels is addressed. The method extends the mid-fidelity strategy named as Transversally Enriched Pipe Element Method, extensively tested as an efficient approach to simulate the blood flow under rigid wall hypothesis, by taking into account the distensibility of the lumen boundary by means of an independent ring structural model. The Navier–Stokes equations, in Arbitrary Lagrangian–Eulerian framework, are used as the governing equations for the blood flow dynamics, the vessel wall mechanics is represented through an elastic constitutive law, and the fluid domain deformation problem is explicitly solved by exploiting the layered structure of the geometry discretization associated to the mid-fidelity model. The result is an approximation strategy able to take into account the wall deformation at nearly zero added cost when compared with a rigid wall model. An extensive numerical validation and verification of the proposed methodology is reported employing simple domains and complex patient-specific geometries to highlight the potential for real applications.

Dynamic tracking of a magnetic micro-roller using ultrasound phase analysis

Microrobots (MRs) have attracted significant interest for their potentialities in diagnosis and non-invasive intervention in hard-to-reach body areas. Fine control of biomedical MRs requires real-time feedback on their position and configuration. Ultrasound (US) imaging stands as a mature and advantageous technology for MRs tracking, but it suffers from disturbances due to low contrast resolution. To overcome these limitations and make US imaging suitable for monitoring and tracking MRs, we propose a US contrast enhancement mechanism for MR visualization in echogenic backgrounds (e.g., tissue). Our technique exploits the specific acoustic phase modulation produced by the MR characteristic motions. By applying this principle, we performed real-time visualization and position tracking of a magnetic MR rolling on a lumen boundary, both in static flow and opposing flow conditions, with an average error of 0.25 body-lengths. Overall, the reported results unveil countless possibilities to exploit the proposed approach as a robust feedback strategy for monitoring and tracking biomedical MRs in-vivo.

Fractional flow reserve for coronary stenosis assessment derived from fusion of intravascular ultrasound and X-ray angiography.

Intravascular ultrasound (IVUS) provides good insight into lumen boundary and plaques; however, it is still difficult to detect functionally significant stenosis from IVUS images for the guidance of coronary percutaneous intervention (PCI). This study aimed to develop a novel method to estimate fractional flow reserve (FFR) value for determining the functional significance of coronary artery disease through the fusion of IVUS and X-ray angiographic images. We developed a novel approach to 3D vessel reconstruction by integrating IVUS with X-ray angiographic images. Based on the reconstructed geometry and the inlet flow derived from the thrombolysis in myocardial infarction (TIMI) frame count, a simplified fluid dynamics equation was established to compute the pressure drop and IVUS-derived FFR (AccuFFRivus) was subsequently obtained. To validate the feasibility and performance of this IVUS-based FFR method, we performed AccuFFRivus calculations on 32 coronary vessels with invasive FFR as the reference standard. Great correlation (r=0.86, P<0.001) was observed between AccuFFRivus and FFR. The area under the receiver-operating characteristic curve (AUC) was higher for AccuFFRivus than minimal lumen area (MLA, <4 mm2) and diameter stenosis rate (DS% ≥50%) [0.98 (95% CI: 0.86 to 1.0) vs. 0.78 (95% CI: 0.60 to 0.91) and 0.66 (95% CI: 0.47 to 0.82)]. Bland-Altman plot showed a mean difference value of -0.011 (limits of agreement: -0.156 to 0.134). AccuFFRivus is a novel method for hybridizing IVUS and X-ray angiographic images to identify functionally significant stenosis with FFR ≤0.80. The good diagnostic performance from the initial validation study demonstrates the potential for clinical utilization of physiologically guided decision-making. Further validation is required in future studies with a large number of cases.

Detecting Aortic Valve Anomaly From Induced Murmurs: Insights From Computational Hemodynamic Models.

Patients who receive transcatheter aortic valve replacement are at risk for leaflet thrombosis-related complications, and can benefit from continuous, longitudinal monitoring of the prosthesis. Conventional angiography modalities are expensive, hospital-centric and either invasive or employ potentially nephrotoxic contrast agents, which preclude their routine use. Heart sounds have been long recognized to contain valuable information about individual valve function, but the skill of auscultation is in decline due to its heavy reliance on the physician’s proficiency leading to poor diagnostic repeatability. This subjectivity in diagnosis can be alleviated using machine learning techniques for anomaly detection. We present a computational and data-driven proof-of-concept analysis of a novel, auscultation-based technique for monitoring aortic valve, which is practical, non-invasive, and non-toxic. However, the underlying mechanisms leading to physiological and pathological heart sounds are not well-understood, which hinders development of such a technique. We first address this by performing direct numerical simulations of the complex interactions between turbulent blood flow in a canonical ascending aorta model and dynamic valve motion in 29 cases with healthy and stenotic valves. Using the turbulent pressure fluctuations on the aorta lumen boundary, we model the propagation of heart sounds, as elastic waves, through the patient’s thorax. The heart sound may be recorded on the epidermal surface using a stethoscope/phonocardiograph. This approach allows us to correlate instantaneous hemodynamic phenomena and valve motion with the acoustic response. From this dataset we extract “acoustic signatures” of healthy and stenotic valves based on principal components of the recorded sound. These signatures are used to train a linear discriminant classifier by maximizing correlation between recorded heart sounds and valve status. We demonstrate that this classifier is capable of accurate prospective detection of anomalous valve function and that the principal component-based signatures capture prominent audible features of heart sounds, which have been historically used by physicians for diagnosis. Further development of such technology can enable inexpensive, safe and patient-centric at-home monitoring, and can extend beyond transcatheter valves to surgical as well as native valves.

Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets.

Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets. First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts. Second, within a machine learning (ML) environment, we automatically segment the lumen boundary using a multi-frame (MF) convolutional neural network (MFCNN). Finally, we use the theory of Gaussian processes (GPs) to regress the final lumen boundary. The dataset consisted of 85 IVUS pullbacks (52 patients). The dataset was partitioned at the pullback-level using 73 pullbacks for training (20586 frames), 6 pullbacks for validation (1692 frames), and 6 for testing (1692 frames). The degree of overlapping, between the ground truth and ML contours, median (interquartile range, IQR) systematically increased from 0.896 (0.874-0.933) for MF1 to 0.925 (0.911-0.948) for MF11. The median (IQR) of the distance error was also reduced from 3.83 (2.94-4.98)% for MF1 to 3.02 (2.25-3.95)% for MF11-GP. The corresponding median (IQR) in the lumen area error remained between 5.49 (2.50-10.50)% for MF1 and 5.12 (2.15-9.00)% for MF11-GP. The dispersion in the relative distance and area errors consistently decreased as we increased the number of frames, and also when the GP regressor was coupled to the MFCNN output. These results demonstrate that the proposed ML approach is suitable to effectively segment the lumen boundary in IVUS scans, reducing the burden of costly and time-consuming manual delineation.

Overcoming calcium blooming and improving the quantification accuracy of percent area luminal stenosis by material decomposition of multi-energy computed tomography datasets.

Purpose: Conventional stenosis quantification from single-energy computed tomography (SECT) images relies on segmentation of lumen boundaries, which suffers from partial volume averaging and calcium blooming effects. We present and evaluate a method for quantifying percent area stenosis using multienergy CT (MECT) images. Approach: We utilize material decomposition of MECT images to measure stenosis based on the ratio of iodine mass between vessel locations with and without a stenosis, thereby eliminating the requirement for segmentation of iodinated lumen. The method was first assessed using simulated MECT images created with different spatial resolutions. To experimentally assess this method, four phantoms with different stenosis severity (30% to 51%), vessel diameters (5.5 to 14mm), and calcification densities (700 to ) were fabricated. Conventional SECT images were acquired using a commercial CT system and were analyzed with commercial software. MECT images were acquired using a commercial dual-energy CT (DECT) system and also from a research photon-counting detector CT (PCD-CT) system. Three-material-decomposition was performed on MECT data, and iodine density maps were used to quantify stenosis. Clinical radiation doses were used for all data acquisitions. Results: Computer simulation verified that this method reduced partial volume and blooming effects, resulting in consistent stenosis measurements. Phantom experiments showed accurate and reproducible stenosis measurements from MECT images. For DECT and two-threshold PCD-CT images, the estimation errors were 4.0% to 7.0%, 2.0% to 9.0%, 10.0% to 18.0%, and to (ground truth: 51%, 51%, 51%, and 30%). For four-threshold PCD-CT images, the errors were 1.0% to 3.0%, 4.0% to 6.0%, to 9.0%, and 0.0% to 6.0%. Errors using SECT were much larger, ranging from 4.4% to 46%, and were especially worse in the presence of dense calcifications. Conclusions: The proposed approach was shown to be insensitive to acquisition parameters, demonstrating the potential to improve the accuracy and precision of stenosis measurements in clinical practice.

Automatic A-line coronary plaque classification using combined deep learning and textural features in intravascular OCT images.

We developed a fully automated method for classifying A-line coronary plaques in intravascular optical coherence tomography images using combined deep learning and textural features. The proposed method was trained on 4,292 images from 48 pullbacks giving 80 manually labeled, volumes of interest. Preprocessing steps including guidewire/shadow removal, lumen boundary detection, pixel shifting, and noise reduction were employed. We built a convolutional neural network to extract the deep learning features from the preprocessed image. Traditional textural features were also extracted and combined with deep learning features. Feature selection was performed using the minimum redundancy maximum relevance method. Combined features were utilized as inputs for a random forest classifier. After classification, conditional random field (CRF) method was used for classification noise cleaning. We determined a sub-feature set with the most predictive power. With CRF noise cleaning, sensitivities/specificities were 82.2%/90.8% and 82.4%/89.2% for fibrolipidic and fibrocalcific classes, respectively, with good Dice coefficients. The classification noise cleaning step improved performance metrics by nearly 10-15%. The predicted en face classification maps of entire pullbacks agreed favorably to the manually labeled counterparts. Both assessments suggested that our automated measurements gave clinically relevant results. The proposed method is very promising with regards to both clinical treatment planning and research applications.

Spatiotemporal remodeling of embryonic aortic arch: stress distribution, microstructure, and vascular growth in silico.

The microstructure for mature vessels has been investigated in detail, while there is limited information about the embryonic stages, in spite of their importance in the prognosis of congenital heart defects. It is hypothesized that the embryonic vasculature represents a disorganized but dynamic soft tissue, which rapidly evolves toward a specialized multi-cellular vascular structure under mechanical loading. Here the microstructural evolution process of the embryonic pharyngeal aortic arch structure was simulated using an in ovo validated long-term growth and remodeling computational model, implemented as an in-house FEBio plug-in. Optical coherence tomography-guided servo-null pressure measurements are assignedas boundary conditions through the critical embryonic stages. The accumulation of key microstructural constituents was recorded through zoom confocal microscopy for all six embryonic arch arteriessimultaneously. The total amount and the radial variation slope of the collagen along the arch wall thickness in different archtypes and for different embryonic times, with different dimension scales, were normalized and compared statistically. The arch growth model shows that the stress levels around the lumen boundary increase from [Formula: see text] (Stage 18) to a level higher than [Formula: see text] (Stage 24), depending on matrix constituent production rates, while the homeostatic strain level is kept constant. The statistical tests show that although the total collagen levels differentiate among bilateral positions of the same arch, the shape coefficient of the matrix microstructural gradient changes with embryonic time, proving radial localization, in accordance with numerical model results. In vivo cell number (DAPI) and vascular endothelial growth factor distributions followed similar trends.

Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography

Intravascular optical coherence tomography (IVOCT) is used to assess stent tissue coverage and malapposition in stent evaluation trials. We developed the OCT Image Visualization and Analysis Toolkit for Stent (OCTivat-Stent), for highly automated analysis of IVOCT pullbacks. Algorithms automatically detected the guidewire, lumen boundary, and stent struts; determined the presence of tissue coverage for each strut; and estimated the stent contour for comparison of stent and lumen area. Strut-level tissue thickness, tissue coverage area, and malapposition area were automatically quantified. The software was used to analyze 292 stent pullbacks. The concordance-correlation-coefficients of automatically measured stent and lumen areas and independent manual measurements were 0.97 and 0.99, respectively. Eleven percent of struts were missed by the software and some artifacts were miscalled as struts giving 1% false-positive strut detection. Eighty-two percent of uncovered struts and 99% of covered struts were labeled correctly, as compared to manual analysis. Using the highly automated software, analysis was harmonized, leading to a reduction of inter-observer variability by 30%. With software assistance, analysis time for a full stent analysis was reduced to less than 30 minutes. Application of this software to stent evaluation trials should enable faster, more reliable analysis with improved statistical power for comparing designs.

Automatic fibroatheroma identification in intravascular optical coherence tomography volumes

Coronary heart disease is the most common type of heart disease that leads to heart attacks. The identification of vulnerable plaques, especially the thin-cap fibroatheroma (TCFA), is crucial to the diagnosis of coronary artery disease. Intravascular optical coherence tomography (IVOCT), an emerging imaging modality, has been proven to be useful for the identification of vulnerable plaques. In this work, we propose an approach to identify the volumes with fibroatheroma frames automatically. In the proposed method, we first detect the lumen using a graph-search based method from unfolded images. Then a region of interest starting from the lumen boundary is cropped for feature extraction. We explore three texture features, Local Binary Patterns (LBP), Haar-like and Histograms of Oriented Gradients (HOG), for fibroatheroma identification. In order to reduce the amount of calculation, a bag of words (BoW) approach is utilized in the feature extraction. Finally, support vector machines are trained to classify the volumes with fibroatheroma frames from those without. A dataset with 41 volumes collected from 41 different subjects is used. Experimental results show that we can achieve a sensitivity of 0.88 and a specificity of 0.94, demonstrating the effectiveness of the proposed method.

On-site evaluation of CT-based fractional flow reserve using simple boundary conditions for computational fluid dynamics

Fractional flow reserve (FFR) is an established method for diagnosing physiological coronary artery stenosis. A method for computing FFR using coronary computed tomography (CT) images was recently developed. However, its calculation requires off-site supercomputer analysis. Here, we report the preliminary result of a method using simple estimation of boundary conditions. The lumen boundaries of the coronary arteries were semi-automatically delineated using full width at half maximum of CT number profiles. The computational fluid dynamics (CFD) of the blood flow was performed using the boundary conditions of a fixed pressure at the coronary ostium and flow rates at each outlet. The total inflow at the coronary ostium was estimated based on the uniform wall shear stress hypothesis and corrected using a hyperemic multiplier to gain a hyperemic flow rate. The flow distribution from a parent vessel to the downstream daughter vessels was determined according to Murray's law. FFR estimated by CFD was calculated as FFRCFD = Pd/Pa. We collected patients who underwent coronary CT and coronary angiography followed by invasively measured FFR and compared FFRCFD with FFR. Sensitivity, specificity, and correlations were assessed. A total of 48 patients and 72 arteries were assessed. The correlation coefficient of FFRCFD with FFR was 0.56. The cut-off value was ≤ 0.80, sensitivity was 59.1%, and specificity was 94.0%. CFD-based FFR using simple boundary conditions for on-site clinical computation provided FFRCFD values that were moderately correlated with invasively measured FFR.