RESUMEN
BACKGROUND: Aortic-valve-stenosis (AS) is a frequent degenerative valvular-disease and carries dismal outcome under-medical-treatment. Transvalvular pressure gradient reflects severity of the valve-disease but is highly dependent on flow-conditions and on other valvular/aortic characteristics. Alternatively, aortic-valve-area (AVA) represents a measure of aortic-valve lesion severity conceptually essential and practically widely-recognized but exhibits multiple-limitations. METHODS: We analyzed the 4D multi-detector computed tomography(MDCT) of 20 randomly selected patients with severe AS. For each-patient, we generated the 3D-model of the valve and of its calcifications, and we computed the anatomical AVA accounting for the 3D-morphology of the leaflets in three-different-ways. Finally, we compared our results vs. Doppler-based AVAE measurements and vs. 2D-planimetric AVA-measurements. RESULTS: 3D-reconstruction and identification of the cusps were successful in 90% of the cases. The calcification patterns where highly-variable over patients, ranging from multiple small deposits to wide and c-shaped deposits running from commissure-to-commissure. AVAE was 82 ± 15 mm2. When segmenting 18 image planes, AVATight, AVAProj-Ann, AVAProj-Tip and their average AVAAve were equal to 80 ± 16, 88 ± 20, 93 ± 21 and 87 ± 19 mm2, respectively, while AVAPlan was equal to 143 ± 50 mm2. Linear-regression of the three measurements vs. AVAE yielded regression slopes equal to 1.26, 1.13 and 0.93 for AVAProj-Ann, AVAProj-Tip and AVATight, respectively. The respective Pearson-coefficients were 0.85,0.86 and 0.90. Conversely, when comparing AVAPlan vs. AVAE, linear regression yielded a slope of 1.73 and a Pearson coefficient of 0.53. CONCLUSIONS: We described a new-method to obtain a set of flow-independent quantifications that complement pressure gradient measurements and combine the advantages of previously proposed methods, while bypassing the corresponding-limitations.
Asunto(s)
Estenosis de la Válvula Aórtica , Válvula Aórtica , Imagenología Tridimensional , Tomografía Computarizada Multidetector , Humanos , Estenosis de la Válvula Aórtica/diagnóstico por imagen , Tomografía Computarizada Multidetector/métodos , Masculino , Femenino , Válvula Aórtica/diagnóstico por imagen , Válvula Aórtica/patología , Anciano , Proyectos Piloto , Imagenología Tridimensional/métodos , Anciano de 80 o más Años , Persona de Mediana EdadRESUMEN
The aortic valve (AV) is crucial for cardiovascular (CV) hemodynamic, impacting cardiac output (CO) and left ventricular volumetric flow rate (LVQ). Its nonlinear behavior challenges standard LVQ prediction methods as well as CO one. This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time-varying AV resistance (TV-AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS-related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real-time learning and transforming 1-D CV signals into 2-D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41 ± 4.84% for CO in healthy conditions and 2.83 ± 1.35% in AS cases-a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV-AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data.
Asunto(s)
Válvula Aórtica , Modelos Cardiovasculares , Válvula Aórtica/fisiología , Humanos , Redes Neurales de la Computación , Estenosis de la Válvula Aórtica/fisiopatología , Hemodinámica/fisiología , Gasto Cardíaco/fisiologíaRESUMEN
Cardiac valves simulation is one of the most complex tasks in cardiovascular modeling. Fluid-structure interaction is not only highly computationally demanding but also requires knowledge of the mechanical properties of the tissue. Therefore, an alternative is to include valves as resistive flow obstacles, prescribing the geometry (and its possible changes) in a simple way, but, at the same time, with a geometry complex enough to reproduce both healthy and pathological configurations. In this work, we present a generalized parametric model of the aortic valve to obtain patient-specific geometries that can be included into blood flow simulations using a resistive immersed implicit surface (RIIS) approach. Numerical tests are presented for geometry generation and flow simulations in aortic stenosis patients whose parameters are extracted from ECG-gated CT images.