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Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario.

The multimodal and multidomain registration of medical images have gained increasing recognition in clinical practice as a powerful tool for fusing and leveraging useful information from different imaging techniques and in different medical fields such as cardiology and orthopedics. Image registration could be a challenging process, and it strongly depends on the correct tuning of registration parameters. In this paper, the robustness and accuracy of a landmarks-based approach have been presented for five cardiac multimodal image datasets. The study is based on 3D Slicer software and it is focused on the registration of a computed tomography (CT) and 3D ultrasound time-series of post-operative mitral valve repair. The accuracy of the method, as a function of the number of landmarks used, was performed by analysing root mean square error (RMSE) and fiducial registration error (FRE) metrics. The validation of the number of landmarks resulted in an optimal number of 10 landmarks. The mean RMSE and FRE values were 5.26 ± 3.17 and 2.98 ± 1.68 mm, respectively, showing comparable performances with respect to the literature. The developed registration process was also tested on a CT orthopaedic dataset to assess the possibility of reconstructing the damaged jaw portion for a pre-operative planning setting. Overall, the proposed work shows how 3D Slicer and registration by landmarks can provide a useful environment for multimodal/unimodal registration.

The veno-arterial extracorporeal membrane oxygenation (VA ECMO) system is a temporary support procedure to provide oxygenated blood in patients with cardiac or respiratory failure. Despite being widely used in clinical reality, the VA ECMO has some well-known flaws, like the formation of a 'watershed zone' as a consequence of the mixing of native blood from the left ventricle and retrograde oxygenated blood from the ECMO pump. A deeper knowledge of the watershed zone behavior could contribute to better defining the oxygenation balancing for the patient. From this concept, this study proposes a numerical approach combined with Statistical Shape Modeling (SSM) to evaluate the effect of aortic morphology variations on the different conditions of VA ECMO support. In particular, a new SSM approach was adopted to define 48 synthetic aortic morphologies starting from patient-specific computed tomography datasets. After assessing the morphological differences, the first 10 modes were selected to generate volumetric meshes for a Computational Fluid Dynamics (CFD) analysis. A total of 20 cases were simulated in the healthy conditions, while four cases were simulated with a 70% reduction of cardiac output and three different levels of ECMO support (0, 4, and 6 L/min). From the simulated results, it was possible to assess the behavior of the watershed zone as a function of aortic morphological parameters and ECMO level. The results confirmed the significant effect of ECMO level on the position and distribution of the zone.

Optical coherence tomography (OCT) is an established catheter-based imaging modality for the assessment of coronary artery disease and the guidance of stent placement during percutaneous coronary intervention. Manual analysis of large OCT datasets for vessel contours or stent struts detection is time-consuming and unsuitable for real-time applications. In this study, a fully automatic method was developed for detection of both vessel contours and stent struts. The method was applied to in vitro OCT scans of eight stented silicone bifurcation phantoms for validation purposes. The proposed algorithm comprised four main steps, namely pre-processing, lumen border detection, stent strut detection, and three-dimensional point cloud creation. The algorithm was validated against manual segmentation performed by two independent image readers. Linear regression showed good agreement between automatic and manual segmentations in terms of lumen area (r>0.99). No statistically significant differences in the number of detected struts were found between the segmentations. Mean values of similarity indexes were >95% and >85% for the lumen and stent detection, respectively. Stent point clouds of two selected cases, obtained after OCT image processing, were compared to the centerline points of the corresponding stent reconstructions from micro computed tomography, used as ground-truth. Quantitative comparison between the corresponding stent points resulted in median values of ~150 μm and ~40 μm for the total and radial distances of both cases, respectively. The repeatability of the detection method was investigated by calculating the lumen volume and the mean number of detected struts per frame for seven repeated OCT scans of one selected case. Results showed low deviation of values from the median for both analyzed quantities. In conclusion, this study presents a robust automatic method for detection of lumen contours and stent struts from OCT as supported by focused validation against both manual segmentation and micro computed tomography and by good repeatability.

In the context of ascending aorta hemodynamics, it is well established that both valve morphology and vessel geometry play a key role. However, the possibility of conducting systematic comparisons is limited by the challenges associated with acquiring patient-specific follow-up data. In this paper, we combined a novel definition for a parametric time-varying inlet velocity profile with a virtual aneurysm growth model to investigate the combined effects of valve morphology and aneurysm progression on aortic hemodynamics. We successfully modeled the reduced orifice area and eccentric inflow characteristic of bicuspid aortic valves and their consequent effects on hemodynamics. Controlled comparisons revealed that flow patterns and related biomarkers are primarily influenced by the presence of an eccentric inflow that induces disrupted hemodynamics, elevated wall shear stresses, and increased oscillatory indexes. While aneurysm growth exerts minimal influence on hemodynamic parameters for small diameter increases, its impact becomes more relevant with substantial aortic bulge enlargement, and it remains dependent on the specific valve phenotype. The current study underlines the pivotal role of aortic valve boundary conditions and the influence of eccentric inlet velocity on ascending aortic flow patterns in both healthy and aneurysmal conditions. Knowledge of valve morphology and the definition of corresponding inflow conditions are essential for patient-specific analyses when in vivo patient-specific boundary conditions are unavailable.

Photon-counting computed tomography (PCCT) is an emerging technology that can potentially transform clinical CT imaging. After a brief description of the PCCT technology, this review summarizes its main advantages over conventional CT: improved spatial resolution, improved signal and contrast behavior, reduced electronic noise and artifacts, decreased radiation dose, and multi-energy capability with improved material discrimination. Moreover, by providing an overview of the existing literature, this review highlights how the PCCT benefits have been harnessed to enhance and broaden the diagnostic capabilities of CT for cardiovascular applications, including the detection of coronary artery calcifications, evaluation of coronary plaque extent and composition, evaluation of coronary stents, and assessment of myocardial tissue characteristics and perfusion.

The photon-counting detector (PCD) is a new computed tomography detector technology (photon-counting computed tomography, PCCT) that provides substantial benefits for cardiac and coronary artery imaging. Compared with conventional CT, PCCT has multi-energy capability, increased spatial resolution and soft tissue contrast with near-null electronic noise, reduced radiation exposure, and optimization of the use of contrast agents. This new technology promises to overcome several limitations of traditional cardiac and coronary CT angiography (CCT/CCTA) including reduction in blooming artifacts in heavy calcified coronary plaques or beam-hardening artifacts in patients with coronary stents, and a more precise assessment of the degree of stenosis and plaque characteristic thanks to its better spatial resolution. Another potential application of PCCT is the use of a double-contrast agent to characterize myocardial tissue. In this current overview of the existing PCCT literature, we describe the strengths, limitations, recent applications, and promising developments of employing PCCT technology in CCT.

Cardiovascular diseases remain a leading cause of morbidity and mortality worldwide and require extensive investigation through studies. Mock Circulatory Loops (MCLs) are advanced platforms that accurately replicate physiological and pathological hemodynamic conditions, while also allowing for precise and patient-specific data collection. Particle Image Velocimetry (PIV) is the standard flow visualization technique for studies, but it is costly and requires strict safety measures. High-power Light Emitting Diode illuminated PIV (LED-PIV) offers a safer and cheaper alternative. In this study, we aim to demonstrate the feasibility of a Hybrid-MCL integrated with a LED-PIV system for the investigation of Abdominal Aortic Aneurysm (AAA) compliant phantoms. We considered two distinct AAA models, namely, an idealized model and a patient-specific one under different physiological flow and pressure conditions. The efficacy of the proposed setup for the investigation of AAA hemodynamics was confirmed by observing velocity and vorticity fields across multiple flow rate scenarios and regions of interest. The findings of this study underscore the potential impact of Hybrid-MCL integrated with a LED-PIV system on enhancing the affordability, accessibility, and safety of CVD investigations.

The left atrial appendage (LAA) is a complex cardiovascular structure which can yield to thrombi formation in patients with non-valvular atrial fibrillation (AF). The study of LAA fluid dynamics together with morphological features should be investigated in order to evaluate the possible connection of geometrical and hemodynamics indices with the stroke risk. To reach this goal, we conducted a morphological analysis of four different LAA shapes considering their variation during the cardiac cycle and computational fluid dynamics (CFD) simulations in AF conditions were carried out. The analysis of main geometrical LAA parameters showed a huger ostium and a reduced motility for the cauliflower and cactus shapes, as well as a lower velocity values from the CFD analysis. Such findings are in line with literature and highlight the importance of coupling dynamics imaging data with CFD calculations for providing information not available at clinical level.