As medical technologies advance with increasing speed, virtual imaging trials (VITs) are emerging as a crucial tool in the evaluation and optimization of new imaging techniques. Widely used in many VITs is the four-dimensional extended cardiac-torso (XCAT) phantom, a comprehensive computational model that accurately represents human anatomy and physiology. While the XCAT phantom offers a powerful tool for imaging research, it offers only a limited model of blood flow to compartmentalized organs, potentially limiting the realism and clinical applicability of contrast-enhanced scan simulations. This study bridges that gap by combining realistic CT simulation with an accurate model of blood flow dynamics to enable more realistic simulations of contrast-enhanced imaging. To achieve this, a validated one-dimensional blood flow simulator, HARVEY1D, was used to model flow throughout the vessels of the XCAT phantom. DukeSim, a validated CT simulation platform, was then modified to incorporate the resulting flow into its simulations, thus enabling the generaon of simulated CT scans reflective of real-world blood-based contrast-enhanced imaging scenarios. To demonstrate the utility of this pipeline in an initial application to cardiac imaging, three heart models were studied: a non-diseased model, a 50% stenosis model, and an 80% stenosis model. Three seconds of contrast propagation were tracked in each heart model, and CT scans corresponding to two timepoints were simulated. Results demonstrated that the presence of stenosis significantly impacted blood flow, with greater resistance to blood flow leading to altered flow patterns visible in the simulated CT images. This work showcases a pipeline that leverages both computational fluid dynamics and medical imaging simulations to enhance the realism of virtual imaging trials and facilitate the evaluation, optimization, and development of diagnostic tools for contrast-enhanced imaging.
The utility and accuracy of computational modeling often requires direct validation against experimental measurements. The work presented here is motivated by taking a combined experimental and computational approach to determine the ability of large-scale computational fluid dynamics (CFD) simulations to understand and predict the dynamics of circulating tumor cells in clinically relevant environments. We use stroboscopic light sheet fluorescence imaging to track the paths and measure the velocities of fluorescent microspheres throughout a human aorta model. Performed over complex physiologicallyrealistic 3D geometries, large data sets are acquired with microscopic resolution over macroscopic distances.
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