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Background: Hemocompatibility‐related adverse events (HRAE) occur commonly in patients with left ventricular assist devices (LVADs) and add to morbidity and mortality. It is unclear whether the outflow graft orientation can impact flow conditions leading to HRAE. This study presents a simulation‐based approach using exact patient anatomy from medical images to investigate the influence of outflow cannula orientation in modulating flow conditions leading to HRAEs. Methods: A 3D model of a proximal aorta and outflow graft was reconstructed from a computed tomography (CT) scan of an LVAD patient and virtually modified to model multiple cannula orientations (n = 10) by varying polar (cranio‐caudal) (n = 5) and off‐set (anterior–posterior) (n = 2) angles. Time‐dependent computational flow simulations were then performed for each anatomical orientation. Qualitative and quantitative hemodynamics metrics of thrombogenicity including time‐averaged wall shear stress (TAWSS), oscillatory shear index (OSI), endothelial cell platelet activation potential (ECAP), particle residence time (PRT), and platelet activation potential (PLAP) were analyzed. Results: Within the simulations performed, endothelial cell activation potential (ECAP) and particle residence time (PRT) were found to be lowest with a polar angle of 85°, regardless of offset angle. However, polar angles that produced parameters at levels least associated with thrombosis varied when the offset angle was changed from 0° to 12°. For offset angles of 0° and 12° respectively, flow shear was lowest at 65° and 75°, time averaged wall shear stress (TAWSS) was highest at 85° and 35°, and platelet activation potential (PLAP) was lowest at 65° and 45°. Conclusion: This study suggests that computational fluid dynamic modeling based on patient‐specific anatomy can be a powerful analytical tool when identifying optimal positioning of an LVAD. Contrary to previous work, our findings suggest that there may be an "ideal" outflow cannula for each individual patient based on a CFD‐based hemocompatibility profile. [ABSTRACT FROM AUTHOR]

The intraluminal thrombus (ILT) has been shown to negatively impact the progression of the abdominal aortic aneurysms (AAAs). The formation of this thrombus layer has been connected to the local flow environment within AAAs, but the specific mechanisms leading to thrombus formation are still not fully understood. Our study investigated the association between vortical structures, near‐wall hemodynamic metrics (e.g., time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI)), and ILT accumulation in a longitudinal cohort of 14 AAAs (53 scans total). Vortices and hemodynamic parameters were estimated using hemodynamic simulations performed to each scan of each patient and compared to local 3D changes of ILT thickness between two consecutive scans (ΔILT). Results showed that vortices formed and remained strong and close to the lumen surface in AAAs without an ILT, while in AAAs with ILTs these detached from the lumen surface and dissipated nearby wall region where an increase in ILT thickness was observed. Although low TAWSS was observed in regions with and without ILT accumulation, an inverse correlation between ∆ILT and TAWSS was observed within the regions that experienced a thrombus growth. Our results support the idea that vortical structures might be playing a role modulating ILT accumulation into specific wall regions. Also, it submits the idea that the low TAWSS will be modulating the growth of thrombus within these preferred ILT accumulated regions. [ABSTRACT FROM AUTHOR]

Pulmonary arterial hypertension (PAH) is a disease characterized by an elevated pulmonary arterial (PA) pressure. While several computational hemodynamic models of the pulmonary vasculature have been developed to understand PAH, they are lacking in some aspects, such as the vessel wall deformation and its lack of calibration against measurements in humans. Here, we describe a computational modeling framework that addresses these limitations. Specifically, computational models describing the coupling of hemodynamics and vessel wall mechanics in the pulmonary vasculature of a PAH patient and a normal subject were developed. Model parameters, consisting of linearized stiffness E of the large vessels and Windkessel parameters for each outflow branch, were calibrated against in vivo measurements of pressure, flow and vessel wall deformation obtained, respectively, from right-heart catheterization, phase-contrast and cine magnetic resonance images. Calibrated stiffness E of the proximal PA was 2.0 and 0.5 MPa for the PAH and normal models, respectively. Calibrated total compliance C T and resistance R T of the distal vessels were, respectively, 0.32 ml/mmHg and 11.3 mmHg∗min/l for the PAH model, and 2.93 ml/mmHg and 2.6 mmHg∗min/l for the normal model. These results were consistent with previous findings that the pulmonary vasculature is stiffer with more constricted distal vessels in PAH patients. Individual effects on PA pressure due to remodeling of the distal and proximal compartments of the pulmonary vasculature were also investigated in a sensitivity analysis. The analysis suggests that the remodeling of distal vasculature contributes more to the increase in PA pressure than the remodeling of proximal vasculature. [ABSTRACT FROM AUTHOR]

Measurement of in vivo strain patterns of musculoskeletal soft tissues (MSTs) during functional activities reveals their biomechanical function, supports the identification and understanding of pathologies, and quantifies tissue adaptation during healing. These scientific and clinical insights have motivated the development and application of various strain sensors to quantify MST strains in either intraoperative or dynamic in vivo conditions. In this study, a strain sensor system is developed based on stretchable electronics and radio frequency identification technologies. In this system, a flexible inductor‐capacitor‐resistor sensor is fabricated such that it can be wirelessly excited by a custom‐designed readout box through electronic resonance. The resonant frequency of the sensor changes when the capacitor is stretched, which is then also recorded by the readout box at a sampling rate of 1024 Hz. Suturing the stretchable capacitor onto the MST allows it to be stretched in line with musculoskeletal deformations, hence providing an indirect method to assess strain patterns in vivo. Application of the system ex vivo indicates that the signal remains linear between 0 and 25% strain and is electronically stable in a simulated in vivo environment for one week and over 100 000 cycles of fatigue loadings. The strain sensor exhibits excellent resolution (0.1% strain, ≈9 µm) during wireless strain measurement. Finally, sensor implantation and strain measurement onto the medial gastrocnemius tendon of a sheep indicate that the sensor is able to record repetitive strain patterns in vivo during dynamic movements. This study indicates the potential scientific and clinical applicability in vivo. [ABSTRACT FROM AUTHOR]

Objective: We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way. Methods: We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time. Results: A dataset of 7 subjects (4–7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach. Conclusion: We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion. Significance: The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs. [ABSTRACT FROM AUTHOR]

Abstract: Sensing mechanical tissue deformation in vivo can provide detailed information on organ functionality and tissue states. To bridge the huge mechanical mismatch between conventional electronics and biological tissues, stretchable electronic systems have recently been developed for interfacing tissues in healthcare applications. A major challenge for wireless electronic implants is that they typically require microchips, which adds complexity and may compromise long‐term stability. Here, a chipless wireless strain sensor technology based on a novel soft conductor with high cyclic stability is reported. The composite material consists of gold‐coated titanium dioxide nanowires embedded in a soft silicone elastomer. The implantable strain sensor is based on an resonant circuit which consists of a stretchable plate capacitor and a coil for inductive readout of its resonance frequency. Successful continuous wireless readout during 50% strain cycles is demonstrated. The sensor element has a Young's modulus of 260 kPa, similar to that of the bladder in order to not impair physiological bladder expansion. A proof‐of‐principle measurement on an ex vivo porcine bladder is presented, which shows the feasibility of the presented materials and devices for continuous, wireless strain monitoring of various tissues and organs in vivo. [ABSTRACT FROM AUTHOR]

• A total of 21 morphological parameters obtained longitudinal CT scan images was computed • The growth rate of all geometrical measurements and their correlation analysis were investigated in terms of non-surgical management of AAA. • AAA volume expansion rates were highly correlated with thrombus accumulation rates. • Among all geometrical parameters, the master curve of spherical diameter performed best, predicting the diameter. • This measure provides useful information about the evolution of aneurysm size and may be clinically helpful. The maximum diameter measurement of an abdominal aortic aneurysm (AAA), which depends on orthogonal and axial cross-sections or maximally inscribed spheres within the AAA, plays a significant role in the clinical decision making process. This study aims to build a total of 21 morphological parameters from longitudinal CT scans and analyze their correlations. Furthermore, this work explores the existence of a "master curve" of AAA growth, and tests which parameters serve to enhance its predictability for clinical use. 106 CT scan images from 25 Korean AAA patients were retrospectively obtained. We subsequently computed morphological parameters, growth rates, and pair-wise correlations, and attempted to enhance the predictability of the growth for high-risk aneurysms using non-linear curve fitting and least-square minimization. An exponential AAA growth model was fitted to the maximum spherical diameter, as the best representative of the growth among all parameters (r-square: 0.94) and correctly predicted to 15 of 16 validation scans based on a 95% confidence interval. AAA volume expansion rates were highly correlated (r=0.75) with thrombus accumulation rates. The exponential growth model using spherical diameter provides useful information about progression of aneurysm size and enables AAA growth rate extrapolation during a given surveillance period. [ABSTRACT FROM AUTHOR]