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Management of aortic dissections (AD) is still challenging, with no universally approved guideline among possible surgical, endovascular, or medical therapies. Approximately 25% of patients with AD suffer postintervention malperfusion syndrome or hemodynamic instability, with the risk of sudden death if left untreated. Part of the issue is that vascular implants may themselves induce flow disturbances that critically impact vital organs. A multilayer mesh construct might obviate the induced flow disturbances, and it is this concept we investigated. We used preintervention and post-multilayer flow modulator implantation (PM) geometries from clinical cases of type B AD. In-house semiautomatic segmentation routines were applied to computed tomography images to reconstruct the lumen. The device was numerically reconstructed and adapted to the PM geometry concentrically fit to the true lumen centerline. We also numerically designed a pseudohealthy case, where the geometry of the aorta was extracted interpolating geometric features of preintervention, postimplantation, and published representative healthy volunteers. Computational fluid dynamics methods were used to study the time-dependent flow patterns, shear stress metrics, and perfusion to vital organs. A three-element Windkessel lumped parameter module was coupled to a finite-volume solver to assign dynamic outlet boundary conditions. Multilayer flow modulator not only significantly reduced false lumen blood flow, eliminated local flow disturbances, and globally regulated wall shear stress distribution but also maintained physiological perfusion to peripheral vital organs. We propose further investigation to focus the management of AD on both modulation of blood flow and restoration of physiologic end-organ perfusion rather than mere restoration of vascular lamina morphology. NEW & NOTEWORTHY The majority of aortic dissection modeling efforts have focused on the maintenance of physiological flow using minimally invasive placed grafts. The multilayer flow modulator is a complex mesh construct of wires, designed to eliminate flow disruptions in the lumen, regulate the physiological wall stresses, and enhance endothelial function and offering the promise of improved perfusion of vital organs. This has never been fully proved or modeled, and these issues we confirmed using a dynamic framework of time-varying arterial waveforms.
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Aneurisma de la Aorta/cirugía , Disección Aórtica/cirugía , Implantación de Prótesis Vascular/instrumentación , Prótesis Vascular , Hemodinámica , Disección Aórtica/diagnóstico por imagen , Disección Aórtica/fisiopatología , Aneurisma de la Aorta/diagnóstico por imagen , Aneurisma de la Aorta/fisiopatología , Aortografía/métodos , Velocidad del Flujo Sanguíneo , Angiografía por Tomografía Computarizada , Humanos , Hidrodinámica , Modelos Cardiovasculares , Modelación Específica para el Paciente , Diseño de Prótesis , Interpretación de Imagen Radiográfica Asistida por Computador , Flujo Sanguíneo Regional , Factores de Tiempo , Resultado del TratamientoRESUMEN
BACKGROUND: The aim of this study is to present a new methodology for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography Angiography (CTA). METHODS: The methodology is summarized in six stages: 1) pre-processing of the initial raw images, 2) rough estimation of the lumen and outer vessel wall borders and approximation of the vessel's centerline, 3) manual adaptation of plaque parameters, 4) accurate extraction of the luminal centerline, 5) detection of the lumen - outer vessel wall borders and calcium plaque region, and 6) finally 3D surface construction. RESULTS: The methodology was compared to the estimations of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method. The correlation coefficients for calcium volume, surface area, length and angle vessel were 0.79, 0.86, 0.95 and 0.88, respectively. Additionally, when comparing the inner and outer vessel wall volumes of the reconstructed arteries produced by IVUS and CTA the observed correlation was 0.87 and 0.83, respectively. CONCLUSIONS: The results indicated that the proposed methodology is fast and accurate and thus it is likely in the future to have applications in research and clinical arena.
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Vasos Coronarios/diagnóstico por imagen , Imagenología Tridimensional/métodos , Placa Aterosclerótica/diagnóstico por imagen , Ultrasonografía Intervencional/métodos , Algoritmos , Angiografía Coronaria/métodos , Humanos , Tomografía Computarizada por Rayos X/métodosRESUMEN
Atherosclerosis is a systemic disease with local manifestations. Low-density lipoprotein (LDL) accumulation in the subendothelial layer is one of the hallmarks of atherosclerosis onset and ignites plaque development and progression. Blood flow-induced endothelial shear stress (ESS) is causally related to the heterogenic distribution of atherosclerotic lesions and critically affects LDL deposition in the vessel wall. In this work we modeled blood flow and LDL transport in the coronary arterial wall and investigated the influence of several hemodynamic and biological factors that may regulate LDL accumulation. We used a three-dimensional model of a stenosed right coronary artery reconstructed from angiographic and intravascular ultrasound patient data. We also reconstructed a second model after restoring the patency of the stenosed lumen to its nondiseased state to assess the effect of the stenosis on LDL accumulation. Furthermore, we implemented a new model for LDL penetration across the endothelial membrane, assuming that endothelial permeability depends on the local lumen LDL concentration. The results showed that the presence of the stenosis had a dramatic effect on the local ESS distribution and LDL accumulation along the artery, and areas of increased LDL accumulation were observed in the downstream region where flow recirculation and low ESS were present. Of the studied factors influencing LDL accumulation, 1) hypertension, 2) increased endothelial permeability (a surrogate of endothelial dysfunction), and 3) increased serum LDL levels, especially when the new model of variable endothelial permeability was applied, had the largest effects, thereby supporting their role as major cardiovascular risk factors.
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Estenosis Coronaria/metabolismo , Vasos Coronarios/metabolismo , Endotelio Vascular/metabolismo , Lipoproteínas LDL/metabolismo , Anciano , Algoritmos , Aterosclerosis/patología , Viscosidad Sanguínea , Permeabilidad Capilar/fisiología , Enfermedades Cardiovasculares/epidemiología , Simulación por Computador , Angiografía Coronaria , Frecuencia Cardíaca/fisiología , Hemodinámica/fisiología , Humanos , Hipertensión/fisiopatología , Procesamiento de Imagen Asistido por Computador , Lipoproteínas LDL/sangre , Angiografía por Resonancia Magnética , Masculino , Modelos Biológicos , Medición de RiesgoRESUMEN
Artificial intelligence (AI) offers great promise in cardiology, and medicine broadly, for its ability to tirelessly integrate vast amounts of data. Applications in medical imaging are particularly attractive, as images are a powerful means to convey rich information and are extensively utilized in cardiology practice. Departing from other AI approaches in cardiology focused on task automation and pattern recognition, we describe a digital health platform to synthesize enhanced, yet familiar, clinical images to augment the cardiologist's visual clinical workflow. In this article, we present the framework, technical fundamentals, and functional applications of the methodology, especially as it pertains to intravascular imaging. A conditional generative adversarial network was trained with annotated images of atherosclerotic diseased arteries to generate synthetic optical coherence tomography and intravascular ultrasound images on the basis of specified plaque morphology. Systems leveraging this unique and flexible construct, whereby a pair of neural networks is competitively trained in tandem, can rapidly generate useful images. These synthetic images replicate the style, and in several ways exceed the content and function, of normally acquired images. By using this technique and employing AI in such applications, one can ameliorate challenges in image quality, interpretability, coherence, completeness, and granularity, thereby enhancing medical education and clinical decision-making.
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Intravascular ultrasound (IVUS) imaging is widely used for diagnostic imaging in interventional cardiology. The detection and quantification of atherosclerosis from acquired images is typically performed manually by medical experts or by virtual histology IVUS (VH-IVUS) software. VH-IVUS analyzes backscattered radio frequency (RF) signals to provide a color-coded tissue map, and is the method of choice for assessing atherosclerotic plaque in situ. However, a significant amount of tissue cannot be analyzed in reasonable time because the method can be applied just once per cardiac cycle. Furthermore, only hardware and software compatible with RF signal acquisition and processing may be used. We present an image-based tissue characterization method that can be applied to entire acquisition sequences post hoc for the assessment of diseased vessels. The pixel-based method utilizes domain knowledge of arterial pathology and physiology, and leverages technological advances of convolutional neural networks to segment diseased vessel walls into the same tissue classes as virtual histology using only grayscale IVUS images. The method was trained and tested on patches extracted from VH-IVUS images acquired from several patients, and achieved overall accuracy of 93.5% for all segmented tissue. Imposing physically-relevant spatial constraints driven by domain knowledge was key to achieving such strong performance. This enriched approach offers capabilities akin to VH-IVUS without the constraints of RF signals or limited once-per-cycle analysis, offering superior potential information acquisition speed, reduced hardware and software requirements, and more widespread applicability. Such an approach may well yield promise for future clinical and research applications.
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Near infrared autofluorescence (NIRAF) optical coherence tomography (OCT) is an intravascular imaging modality, based on a catheter which emits light at two different wavelengths through an optical fiber. Since OCT is becoming the method of choice in interventional cardiology and NIRAF is proven to be higher in plaque lesions having higher risk morphologic phenotypes, the NIRAF-OCT can become powerful and promising technology. However, there is NIRAF- distance dependence which has to be addressed before the technology can be applied in clinical practice. The present paper aims at presenting a method which calibrates the distance dependent NIRAF signal and ensures that similar NIRAF values are depicted when targeting the same lesion. Towards this purpose, autofluorescence phantoms were constructed, accurate distance measurements were conducted and the NIRAF-distance relationship was quantified. Finally, a calibration function was proposed which is able to accurately calibrate the NIRAF signal in any NIRAF-OCT pullback.
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Enfermedad de la Arteria Coronaria , Placa Aterosclerótica , Calibración , Humanos , Tomografía de Coherencia ÓpticaRESUMEN
Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment.
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Técnicas de Imagen Cardíaca , Biología Computacional , Informática Médica , Cardiología/organización & administración , Cardiología/estadística & datos numéricos , Enfermedades Cardiovasculares/diagnóstico por imagen , Humanos , Aprendizaje Automático , Publicaciones/estadística & datos numéricosRESUMEN
Aortic dissections are challenging for it remains perplexing to determine when surgical, endovascular, or medical therapies are optimal. We studied the effect of the multilayer flow modulator (MFM) device in patients with different forms of type-B aortic dissections. CT scans were performed pre-, immediately post-MFM implantation, and multiple times within a 24-month follow-up. Three-dimensional reconstructions were created from these scans and the multilayer or single-layer mesh device placed virtually into the true lumen. We observed that MFM device can sufficiently restore flow perfusion, reduce the false lumen, eliminate local flow recirculation, and reduce wall shear stress distribution globally. Single-layer devices can reduce false lumen dimensions; however, they generate local disturbance and recirculation zones in selected areas at specific time points. Moreover, in polar extremes of dissection, the MFM device restored flow to vital organs perfusing vessels independent of effects on luminal patency. Management of aortic dissections should focus on modulation of blood flow, suppression of local recirculation, and restoration of vital organ perfusion rather than primarily restoring vascular lumen morphology. While the latter restores the geometry of the true lumen, only the former restores homeostasis. Graphical abstract.
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Disección Aórtica , Prótesis Vascular , Modelos Cardiovasculares , Adulto , Disección Aórtica/sangre , Disección Aórtica/cirugía , Velocidad del Flujo Sanguíneo , Procedimientos Endovasculares/instrumentación , Femenino , Hemodinámica , Humanos , Masculino , Persona de Mediana Edad , Tomografía Computarizada por Rayos XRESUMEN
Automated analysis of vascular imaging techniques is limited by the inability to precisely determine arterial borders. Intravascular optical coherence tomography (OCT) offers unprecedented detail of artery wall structure and composition, but does not provide consistent visibility of the outer border of the vessel due to the limited penetration depth. Existing interpolation and surface fitting methods prove insufficient to accurately fill the gaps between the irregularly spaced and sometimes unreliably identified visible segments of the vessel outer border. This paper describes an intuitive, efficient, and flexible new method of 3D surface fitting and smoothing suitable for this task. An anisotropic linear-elastic mesh is fit to irregularly spaced and uncertain data points corresponding to visible segments of vessel borders, enabling the fully automated delineation of the entire inner and outer borders of diseased vessels in OCT images for the first time. In a clinical dataset, the proposed smooth surface fitting approach had great agreement when compared with human annotations: areas differed by just 11 ± 11% (0.93 ± 0.84 mm2), with a coefficient of determination of 0.89. Overlapping and non-overlapping area ratios were 0.91 and 0.18, respectively, with a sensitivity of 90.8 and specificity of 99.0. This spring mesh method of contour fitting significantly outperformed all alternative surface fitting and interpolation approaches tested. The application of this promising proposed method is expected to enhance clinical intervention and translational research using OCT.
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Vasos Coronarios/diagnóstico por imagen , Imagenología Tridimensional/métodos , Tomografía de Coherencia Óptica/métodos , Algoritmos , Humanos , Sensibilidad y Especificidad , Ultrasonografía IntervencionalRESUMEN
The detection, quantification and characterization of coronary atherosclerotic plaques has a major effect on the diagnosis and treatment of coronary artery disease (CAD). Different studies have reported and evaluated the noninvasive ability of Computed Tomography Coronary Angiography (CTCA) to identify coronary plaque features. The identification of calcified plaques (CP) and non-calcified plaques (NCP) using CTCA has been extensively studied in cardiovascular research. However, NCP detection remains a challenging problem in CTCA imaging, due to the similar intensity values of NCP compared to the perivascular tissue, which surrounds the vasculature. In this work, we present a novel methodology for the identification of the plaque burden of the coronary artery and the volumetric quantification of CP and NCP utilizing CTCA images and we compare the findings with virtual histology intravascular ultrasound (VH-IVUS) and manual expert's annotations. Bland-Altman analyses were employed to assess the agreement between the presented methodology and VH-IVUS. The assessment of the plaque volume, the lesion length and the plaque area in 18 coronary lesions indicated excellent correlation with VH-IVUS. More specifically, for the CP lesions the correlation of plaque volume, lesion length and plaque area was 0.93, 0.84 and 0.85, respectively, whereas the correlation of plaque volume, lesion length and plaque area for the NCP lesions was 0.92, 0.95 and 0.81, respectively. In addition to this, the segmentation of the lumen, CP and NCP in 1350 CTCA slices indicated that the mean value of DICE coefficient is 0.72, 0.7 and 0.62, whereas the mean HD value is 1.95, 1.74 and 1.95, for the lumen, CP and NCP, respectively.
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Angiografía por Tomografía Computarizada , Angiografía Coronaria , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Vasos Coronarios/diagnóstico por imagen , Imagenología Tridimensional , Ultrasonografía Intervencional , Calcificación Vascular/diagnóstico por imagen , Anciano , Femenino , Humanos , Masculino , Persona de Mediana EdadRESUMEN
BACKGROUND: Due to the incremental increase of clinical interest in the development of software that allows the 3-dimensional (3D) reconstruction and the functional assessment of the coronary vasculature, several software packages have been developed and are available today. OBJECTIVE: Taking this into consideration, we have developed an innovative suite of software modules that perform 3D reconstruction of coronary arterial segments using different coronary imaging modalities such as IntraVascular UltraSound (IVUS) and invasive coronary angiography images (ICA), Optical Coherence Tomography (OCT) and ICA images, or plain ICA images and can safely and accurately assess the hemodynamic status of the artery of interest. METHODS: The user can perform automated or manual segmentation of the IVUS or OCT images, visualize in 3D the reconstructed vessel and export it to formats, which are compatible with other Computer Aided Design (CAD) software systems. We employ finite elements to provide the capability to assess the hemodynamic functionality of the reconstructed vessels by calculating the virtual functional assessment index (vFAI), an index that corresponds and has been shown to correlate well to the actual fractional flow reserve (FFR) value. RESULTS: All the modules of the proposed system have been thoroughly validated. In brief, the 3D-QCA module, compared to a successful commercial software of the same genre, presented very good correlation using several validation metrics, with a Pearson's correlation coefficient (R) for the calculated volumes, vFAI, length and minimum lumen diameter of 0.99, 0.99, 0.99 and 0.88, respectively. Moreover, the automatic lumen detection modules for IVUS and OCT presented very high accuracy compared to the annotations by medical experts with the Pearson's correlation coefficient reaching the values of 0.94 and 0.99, respectively. CONCLUSIONS: In this study, we have presented a user-friendly software for the 3D reconstruction of coronary arterial segments and the accurate hemodynamic assessment of the severity of existing stenosis.
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Vasos Coronarios/diagnóstico por imagen , Hemodinámica/fisiología , Imagenología Tridimensional/métodos , Modelos Cardiovasculares , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Diseño Asistido por Computadora , Angiografía Coronaria/métodos , Humanos , Diseño de Software , Tomografía de Coherencia ÓpticaRESUMEN
We present a novel and time-efficient method for intracoronary lumen detection, which produces three-dimensional (3-D) coronary arteries using optical coherence tomographic (OCT) images. OCT images are acquired for multiple patients and longitudinal cross-section (LOCS) images are reconstructed using different acquisition angles. The lumen contours for each LOCS image are extracted and translated to 2-D cross-sectional images. Using two angiographic projections, the centerline of the coronary vessel is reconstructed in 3-D, and the detected 2-D contours are transformed to 3-D and placed perpendicular to the centerline. To validate the proposed method, 613 manual annotations from medical experts were used as gold standard. The 2-D detected contours were compared with the annotated contours, and the 3-D reconstructed models produced using the detected contours were compared to the models produced by the annotated contours. Wall shear stress (WSS), as dominant hemodynamics factor, was calculated using computational fluid dynamics and 844 consecutive 2-mm segments of the 3-D models were extracted and compared with each other. High Pearson's correlation coefficients were obtained for the lumen area (r = 0.98) and local WSS (r = 0.97) measurements, while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping and nonoverlapping areas ratio between experts' annotations and presented method was 0.92 and 0.14, respectively. The proposed computer-aided lumen extraction and 3-D vessel reconstruction method is fast, accurate, and likely to assist in a number of research and clinical applications.
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Angiografía Coronaria/métodos , Imagenología Tridimensional/métodos , Tomografía de Coherencia Óptica/métodos , Algoritmos , Vasos Coronarios/diagnóstico por imagen , HumanosRESUMEN
Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging-they are relatively invisible via angiography-and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.
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Vasos Coronarios/diagnóstico por imagen , Procedimientos Endovasculares/instrumentación , Tomografía de Coherencia Óptica/métodos , Algoritmos , Humanos , Polímeros/química , Diseño de Prótesis , Tomografía de Coherencia Óptica/instrumentaciónRESUMEN
Bioresorbable vascular scaffolds (BVS), the next step in the continuum of minimally invasive vascular interventions present new opportunities for patients and clinicians but challenges as well. As they are comprised of polymeric materials standard imaging is challenging. This is especially problematic as modalities like optical coherence tomography (OCT) become more prevalent in cardiology. OCT, a light-based intracoronary imaging technique, provides cross-sectional images of plaque and luminal morphology. Until recently segmentation of OCT images for BVS struts was performed manually by experts. However, this process is time consuming and not tractable for large amounts of patient data. Several automated methods exist to segment metallic stents, which do not apply to the newer BVS. Given this current limitation coupled with the emerging popularity of the BVS technology, it is crucial to develop an automated methodology to segment BVS struts in OCT images. The objective of this paper is to develop a novel BVS strut detection method in intracoronary OCT images. First, we preprocess the image to remove imaging artifacts. Then, we use a K-means clustering algorithm to automatically segment the image. Finally, we isolate the stent struts from the rest of the image. The accuracy of the proposed method was evaluated using expert estimations on 658 annotated images acquired from 7 patients at the time of coronary arterial interventions. Our proposed methodology has a positive predictive value of 0.93, a Pearson Correlation coefficient of 0.94, and a F1 score of 0.92. The proposed methodology allows for rapid, accurate, and fully automated segmentation of BVS struts in OCT images.
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Carotid atherosclerosis may lead to devastating clinical outcomes such as stroke. Data on the value of local factors in predicting progression in carotid atherosclerosis are limited. Our aim was to investigate the association of local endothelial shear stress (ESS) and low-density lipoprotein (LDL) accumulation with the natural history of atherosclerotic disease using a series of 3 time points of human magnetic resonance data. Three-dimensional lumen/wall reconstruction was performed in 12 carotids, and blood flow and LDL mass transport modeling were performed. Our results showed that an increase in plaque thickness and a decrease in lumen size were associated with low ESS and high LDL accumulation in the arterial wall. Low ESS (odds ratio [OR]: 2.99; 95% confidence interval [CI]: 2.31-3.88; P < .001 vs higher ESS) and high LDL concentration (OR: 3.26; 95% CI: 2.44-4.36; P < .001 vs higher LDL concentration) were significantly associated with substantial local plaque growth. Low ESS and high LDL accumulation both presented a diagnostic accuracy of 67% for predicting plaque growth regions. Modeling of blood flow and LDL mass transport show promise in predicting progression of carotid atherosclerosis.
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Enfermedades de las Arterias Carótidas/diagnóstico por imagen , Imagenología Tridimensional , Imagen por Resonancia Magnética/métodos , Anciano , Biomarcadores/sangre , Velocidad del Flujo Sanguíneo , Enfermedades de las Arterias Carótidas/fisiopatología , Progresión de la Enfermedad , Femenino , Hemodinámica/fisiología , Humanos , Interpretación de Imagen Asistida por Computador , Lipoproteínas LDL/sangre , Masculino , Persona de Mediana Edad , Factores de RiesgoRESUMEN
The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary bifurcations using biplane Coronary Angiographies and Optical Coherence Tomography (OCT) imaging. The method is based on a five step approach by improving a previous validated work in order to reconstruct coronary arterial bifurcations. In the first step the lumen borders are detected on the Frequency Domain (FD) OCT images. In the second step a semi-automated method is implemented on two angiographies for the extraction of the 2D bifurcation coronary artery centerline. In the third step the 3D path of the bifurcation artery is extracted based on a back projection algorithm. In the fourth step the lumen borders are placed onto the 3D catheter path. Finally, in the fifth step the intersection of the main and side branches produces the reconstructed model of the coronary bifurcation artery. Data from three patients are acquired for the validation of the proposed methodology and the results are compared against a reconstruction method using quantitative coronary angiography (QCA). The comparison between the two methods is achieved using morphological measures of the vessels as well as comparison of the wall shear stress (WSS) mean values.
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Tomografía de Coherencia Óptica , Algoritmos , Angiografía Coronaria , Enfermedad de la Arteria Coronaria , Vasos Coronarios , Humanos , Imagenología TridimensionalRESUMEN
OBJECTIVE: The aim of this study is to explore major mechanisms of atherosclerotic plaque growth, presenting a proof-of-concept numerical model. METHODS: To this aim, a human reconstructed left circumflex coronary artery is utilized for a multilevel modeling approach. More specifically, the first level consists of the modeling of blood flow and endothelial shear stress (ESS) computation. The second level includes the modeling of low-density lipoprotein (LDL) and high-density lipoprotein and monocytes transport through the endothelial membrane to vessel wall. The third level comprises of the modeling of LDL oxidation, macrophages differentiation, and foam cells formation. All modeling levels integrate experimental findings to describe the major mechanisms that occur in the arterial physiology. In order to validate the proposed approach, we utilize a patient specific scenario by comparing the baseline computational results with the changes in arterial wall thickness, lumen diameter, and plaque components using follow-up data. RESULTS: The results of this model show that ESS and LDL concentration have a good correlation with the changes in plaque area [R2 = 0.365 (P = 0.029, adjusted R2 = 0.307) and R2 = 0.368 (P = 0.015, adjusted R2 = 0.342), respectively], whereas the introduction of the variables of oxidized LDL, macrophages, and foam cells as independent predictors improves the accuracy in predicting regions potential for atherosclerotic plaque development [R2 = 0.847 (P = 0.009, adjusted R2 = 0.738)]. CONCLUSION: Advanced computational models can be used to increase the accuracy to predict regions which are prone to plaque development. SIGNIFICANCE: Atherosclerosis is one of leading causes of death worldwide. For this purpose computational models have to be implemented to predict disease progression.
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Enfermedad de la Arteria Coronaria/fisiopatología , Vasos Coronarios/fisiopatología , Diagnóstico por Computador/métodos , Endotelio Vascular/fisiopatología , Modelos Cardiovasculares , Placa Aterosclerótica/fisiopatología , Velocidad del Flujo Sanguíneo , Presión Sanguínea , Simulación por Computador , Humanos , Lipoproteínas LDL/sangre , Monocitos/metabolismo , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Resistencia al CorteRESUMEN
The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography (CT) Angiography. The method is summarized in three steps. In the first step, image filters are applied to CT images and an initial estimation of the vessel borders is extracted. In the second step, the 3D centerline is extracted using the center of gravity of each rough artery border. Finally in the third step, the borders and the plaque are detected and placed onto the 3D centerline constructing a 3D surface. By using as gold standard the results of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method, high correlation is observed for calcium objects detected by CT and IVUS. The correlation coefficients for objects' volume, surface area, length and angle are r=0.51, r=0.89, r=0.96 and r=0.93, respectively.
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Enfermedad de la Arteria Coronaria , Algoritmos , Angiografía por Tomografía Computarizada , Angiografía Coronaria , Vasos Coronarios , Humanos , Imagenología Tridimensional , Ultrasonografía IntervencionalRESUMEN
In this work, we present a computational model for plaque growth utilizing magnetic resonance data of a patient's carotid artery. More specifically, we model blood flow utilizing the Navier-Stokes equations, as well as LDL and HDL transport using the convection-diffusion equation in the arterial lumen. The accumulated LDL in the arterial wall is oxidized considering the protective effect of HDL. Macrophages recruitment and foam cells formation are the final step of the proposed multi-level modeling approach of the plaque growth. The simulated results of our model are compared with the follow-up MRI findings in 12 months regarding the change to the arterial wall thickness. WSS and LDL may indicate potential regions of plaque growth (R(2)=0.35), but the contribution of foam cells formation, macrophages and oxidized LDL increased the prediction significantly (R(2)=0.75).