It has recently become possible to simulate aneurysmal blood flow dynamics in a patient-specific manner via the coupling of three-dimensional (3-D) X-ray angiography and cmputational fluid dynamics (CFD). Before such image-based CFD models can be used in a predictive capacity, however, it must be shown that they indeed reproduce the in vivo hemodynamic environment. Motivated by the fact that there are currently no techniques for adequately measuring complex blood velocity fields in vivo, in this paper we describe how cine X-ray angiograms may be simulated for the purpose of indirectly validating patient-sperific CFD models. Mimicking the radiological procedure, a virtual angiogram is constructed by first simulating the time-varying injection of contrast agent into a precomputed, patient-specific CFD model. A time-series of images is then constructed by simulating the attenuation of X-rays through the computed 3-D contrast-agent flow dynamics. Virtual angiographic images and residence time maps, here derived from an image-based CFD model of a giant aneurysm, are shown to be in excellent agreement wiith the corresponding clinical images and residence time maps, but only when the interaction between the quasisteady contrast agent injection and the pulsatile flow are properly accounted for. These virtual angiographic techniques pave the way for validating image-based CFD models against routinely available clinical data, and provide a means of visualizing complex, 3-D blood flow dynamics in a clinically relevant manner. They also clearly show how the contrast agent injection perturbs the noraml blood flow patterns, further highlighting the potential utility of image-based CFD as a window into the true aneurysmal hemodynamics.
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http://dx.doi.org/10.1109/TMI.2005.859204 | DOI Listing |
Comput Biol Med
January 2025
LaBS, Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy. Electronic address:
In the context of dynamic image-based computational fluid dynamics (DIB-CFD) modeling of cardiac system, the role of sub-valvular apparatus (chordae tendineae and papillary muscles) and the effects of different mitral valve (MV) opening/closure dynamics, have not been systemically determined. To provide a partial filling of this gap, in this study we performed DIB-CFD numerical experiments in the left ventricle, left atrium and aortic root, with the aim of highlighting the influence on the numerical results of two specific modeling scenarios: (i) the presence of the sub-valvular apparatus, consisting of chordae tendineae and papillary muscles; (ii) different MV dynamics models accounting for different use of leaflet reconstruction from imaging. This is performed for one healthy subject and one patient with mitral valve regurgitation.
View Article and Find Full Text PDFSci Rep
January 2025
School of Aerospace Engineering, Gyeongsang National University, Jinju-si, 52828, Gyeongsangnam-do, Republic of Korea.
This study introduces a novel deep learning-based technique for predicting pressure distribution images, aimed at application in image-based approximate optimal design. The proposed approach integrates both unsupervised and supervised learning paradigms, employing autoencoders (AE) for the unsupervised component and fully connected neural networks (FNN) for the supervised component. A surrogate model based on 2D image data was developed, enabling a comparative analysis of three distinct methods: the conventional AE, the convolutional autoencoder (CAE), and a hybrid CAE, which combines the CAE with a conventional AE.
View Article and Find Full Text PDFJ Vis Exp
November 2024
Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri-Kansas City;
Osteocytes are the bone cells that are thought to respond to mechanical strains and fluid flow shear stress (FFSS) by activating various biological pathways in a process known as mechanotransduction. Confocal image-derived models of osteocyte networks are a valuable tool for conducting Computational Fluid Dynamics (CFD) analysis to evaluate shear stresses on the osteocyte membrane, which cannot be determined by direct measurement. Computational modeling using these high-resolution images of the microstructural architecture of bone was used to numerically simulate the mechanical loading exerted on bone and understand the load-induced stimulation of osteocytes.
View Article and Find Full Text PDFFront Bioeng Biotechnol
November 2024
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
Purpose: To investigate local hemodynamic changes resulting from elevated intraocular pressure (IOP) in different vasculature networks using a computational fluid dynamics model based on 3D reconstructed confocal microscopic images.
Methods: Three-dimensional rat retinal vasculature was reconstructed from confocal microscopy images using a 3D U-Net-based labeling technique, followed by manual correction. We conducted a computational fluid dynamics (CFD) analysis on different retinal vasculature networks derived from a single rat.
Int J Comput Assist Radiol Surg
January 2025
INSIGNEO Institute for in Silico Medicine, University of Sheffield, Sheffield, UK.
Purpose: Accurately quantifying the rupture risk of unruptured intracranial aneurysms (UIAs) is crucial for guiding treatment decisions and remains an unmet clinical challenge. Computational Flow Dynamics and morphological measurements have been shown to differ between ruptured and unruptured aneurysms. It is not clear if these provide any additional information above routinely available clinical observations or not.
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