Using a Mock Circulatory Loop as a Regulatory Science Tool to Simulate Different Heart Failure Conditions.

Mechanical circulatory support (MCS) device therapy is one of the primary treatment options for end-stage heart failure (HF), whereby a mechanical pump is integrated with the failing heart to maintain adequate tissue perfusion. The ISO 14708-5:2020 standard prescribes generic guidelines for nonclinical device evaluation and system performance testing of MCS devices using a mock circulatory loop (MCL). However, the utility of MCLs in premarket regulatory submissions of MCS devices is ambiguous, and the specific disease states that the device is intended to treat are not usually simulated. Hence, we aim to outline the potential of MCLs as a valuable regulatory science tool for characterizing MCS device systems by adequately representing target clinical-use HF conditions on the bench. Target pathophysiologic hemodynamics of HF conditions (i.e., cardiogenic shock (CS), left ventricular (LV) hypertrophy secondary to hypertension, and coronary artery disease), along with a healthy adult at rest and a healthy adult during exercise are provided as recommended test conditions. The conditions are characterized based on LV, aorta, and left atrium pressures using recommended cardiac hemodynamic indices such as systolic, diastolic, and mean arterial pressure, mean cardiac output (CO), cardiac cycle time, and systemic vascular resistance. This study is a first step toward standardizing MCLs to generate well-defined target HF conditions used to evaluate MCS devices.

Evaluation of Hemodynamics in a Prestressed and Compliant Tapered Femoral Artery Using an Optimization-Based Inverse Algorithm.

The important factors that affect the arterial wall compliance are the tissue properties of the arterial wall, the in vivo pulsatile pressure, and the prestressed condition of the artery. It is necessary to obtain the load-free geometry for determining the physiological level of prestress in the arterial wall. The previously developed optimization-based inverse algorithm was improved to obtain the load-free geometry and the wall prestress of an idealized tapered femoral artery of a dog under varying arterial wall properties. The compliance of the artery was also evaluated over a range of systemic pressures (72.5-140.7 mmHg), associated blood flows, and artery wall properties using the prestressed arterial geometry. The results showed that the computed load-free outer diameter at the inlet of the tapered artery was 6.7%, 9.0%, and 12% smaller than the corresponding in vivo diameter for the 25% softer, baseline, and 25% stiffer arterial wall properties, respectively. In contrast, the variations in the prestressed geometry and circumferential wall prestress were less than 2% for variable arterial wall properties. The computed compliance at the inlet of the prestressed artery for the baseline arterial wall property was 0.34%, 0.19%, and 0.13% diameter change/mmHg for time-averaged pressures of 72.5, 104.1, and 140.7 mmHg, respectively. However, the variation in compliance due to the change in arterial wall property was less than 6%. The load-free and prestressed geometries of the idealized tapered femoral artery were accurately (error within 1.2% of the in vivo geometry) computed under variable arterial wall properties using the modified inverse algorithm. Based on the blood-arterial wall interaction results, the arterial wall compliance was influenced significantly by the change in average pressure. In contrast, the change in arterial wall property did not influence the arterial wall compliance.

Uncertainty Analysis of the Core Body Temperature Under Thermal and Physical Stress Using a Three-Dimensional Whole Body Model.

Heat stress experienced by firefighters is a common consequence of extreme firefighting activity. In order to avoid the adverse health conditions due to uncompensable heat stress, the prediction and monitoring of the thermal response of firefighters is critical. Tissue properties, among other parameters, are known to vary between individuals and influence the prediction of thermal response. Further, measurement of tissue properties of each firefighter is not practical. Therefore, in this study, we developed a whole body computational model to evaluate the effect of variability (uncertainty) in tissue parameters on the thermal response of a firefighter during firefighting. Modifications were made to an existing human whole body computational model, developed in our lab, for conducting transient thermal analysis for a firefighting scenario. In conjunction with nominal (baseline) tissue parameters obtained from literature, and physiologic conditions from a firefighting drill, the Pennes bioheat and energy balance equations were solved to obtain the core body temperature of a firefighter. Subsequently, the uncertainty in core body temperature due to variability in the tissue parameters (input parameters), metabolic rate, specific heat, density, and thermal conductivity was computed using the sensitivity coefficient method. On comparing the individual effect of tissue parameters on the uncertainty in core body temperature, the metabolic rate had the highest contribution (within ±0.20°C) followed by specific heat (within ±0.10°C), density (within ±0.07°C), and finally thermal conductivity (within ±0.01 °C). A maximum overall uncertainty of ±0.23 °C in the core body temperature was observed due to the combined uncertainty in the tissue parameters. Thus, the model results can be used to effectively predict a realistic range of thermal response of the firefighters during firefighting or similar activities.

Verification Benchmarks to Assess the Implementation of Computational Fluid Dynamics Based Hemolysis Prediction Models.

As part of an ongoing effort to develop verification and validation (V&V) standards for using computational fluid dynamics (CFD) in the evaluation of medical devices, we have developed idealized flow-based verification benchmarks to assess the implementation of commonly cited power-law based hemolysis models in CFD. Verification process ensures that all governing equations are solved correctly and the model is free of user and numerical errors. To perform verification for power-law based hemolysis modeling, analytical solutions for the Eulerian power-law blood damage model (which estimates hemolysis index (HI) as a function of shear stress and exposure time) were obtained for Couette and inclined Couette flow models, and for Newtonian and non-Newtonian pipe flow models. Subsequently, CFD simulations of fluid flow and HI were performed using Eulerian and three different Lagrangian-based hemolysis models and compared with the analytical solutions. For all the geometries, the blood damage results from the Eulerian-based CFD simulations matched the Eulerian analytical solutions within ∼1%, which indicates successful implementation of the Eulerian hemolysis model. Agreement between the Lagrangian and Eulerian models depended upon the choice of the hemolysis power-law constants. For the commonly used values of power-law constants (α = 1.9-2.42 and ß = 0.65-0.80), in the absence of flow acceleration, most of the Lagrangian models matched the Eulerian results within 5%. In the presence of flow acceleration (inclined Couette flow), moderate differences (∼10%) were observed between the Lagrangian and Eulerian models. This difference increased to greater than 100% as the beta exponent decreased. These simplified flow problems can be used as standard benchmarks for verifying the implementation of blood damage predictive models in commercial and open-source CFD codes. The current study only used power-law model as an illustrative example to emphasize the need for model verification. Similar verification problems could be developed for other types of hemolysis models (such as strain-based and energy dissipation-based methods). However, since the current study did not include experimental validation, the results from the verified models do not guarantee accurate hemolysis predictions. This verification step must be followed by experimental validation before the hemolysis models can be used for actual device safety evaluations.

Diagnostic uncertainties during assessment of serial coronary stenoses: an in vitro study.

Currently, the diagnosis of coronary stenosis is primarily based on the well-established functional diagnostic parameter, fractional flow reserve (FFR: ratio of pressures distal and proximal to a stenosis). The threshold of FFR has a "gray" zone of 0.75-0.80, below which further clinical intervention is recommended. An alternate diagnostic parameter, pressure drop coefficient (CDP: ratio of trans-stenotic pressure drop to the proximal dynamic pressure), developed based on fundamental fluid dynamics principles, has been suggested by our group. Additional serial stenosis, present downstream in a single vessel, reduces the hyperemic flow, Q˜h, and pressure drop, Δp˜, across an upstream stenosis. Such hemodynamic variations may alter the values of FFR and CDP of the upstream stenosis. Thus, in the presence of serial stenoses, there is a need to evaluate the possibility of misinterpretation of FFR and test the efficacy of CDP of individual stenoses. In-vitro experiments simulating physiologic conditions, along with human data, were used to evaluate nine combinations of serial stenoses. Different cases of upstream stenosis (mild: 64% area stenosis (AS) or 40% diameter stenosis (DS); intermediate: 80% AS or 55% DS; and severe: 90% AS or 68% DS) were tested under varying degrees of downstream stenosis (mild, intermediate, and severe). The pressure drop-flow rate characteristics of the serial stenoses combinations were evaluated for determining the effect of the downstream stenosis on the upstream stenosis. In general, Q˜h and Δp˜ across the upstream stenosis decreased when the downstream stenosis severity was increased. The FFR of the upstream mild, intermediate, and severe stenosis increased by a maximum of 3%, 13%, and 19%, respectively, when the downstream stenosis severity increased from mild to severe. The FFR of a stand-alone intermediate stenosis under a clinical setting is reported to be ∼0.72. In the presence of a downstream stenosis, the FFR values of the upstream intermediate stenosis were either within (0.77 for 80%-64% AS and 0.79 for 80%-80% AS) or above (0.88 for 80%-90% AS) the "gray" zone (0.75-0.80). This artificial increase in the FFR value within or above the "gray" zone for an upstream intermediate stenosis when in series with a clinically relevant downstream stenosis could lead to misinterpretation of functional stenosis severity. In contrast, a distinct range of CDP values was observed for each case of upstream stenosis (mild: 8-10; intermediate: 47-54; and severe: 130-155). The nonoverlapping range of CDP could better delineate the effect of the downstream stenosis from the upstream stenosis and allow for the accurate diagnosis of the functional severity of the upstream stenosis.

A review of biotransport education in the 21st century: lessons learned from experts.

The field of bioengineering is relatively new and complex including multiple disciplines encompassing areas in science and engineering. Efforts including the National Science Foundation (NSF) sponsored Integrative Graduate Education and Research Traineeship (IGERT) and VaNTH Engineering Research Center in Bioengineering Educational Technologies have been made to establish and disseminate knowledge and proven methods for teaching bioengineering concepts. Further, the summer bioengineering conference (SBC), sponsored by the American Society of Mechanical Engineers' (ASME) Bioengineering Division, was established to provide a meeting place for engineering educators and students having common interests in biological systems. Of the many subdisciplines of bioengineering, biotransport is a key subject that has wide applicability to many issues in engineering, biology, medicine, pharmacology, and environmental science, among others. The absence of standard content, guidelines, and texts needed for teaching biotransport courses to students motivated the Biotransport committee of ASME's Bioengineering Division to establish a biotransport education initiative. Biotransport education workshop sessions were conducted during the SBC 2011, 2012, and 2013 as part of this initiative. The workshop sessions included presentations from experienced faculty covering a spectrum of information from general descriptions of undergraduate biotransport courses to very detailed outlines of graduate courses to successful teaching techniques. A list of texts and references available for teaching biotransport courses at undergraduate and graduate levels has been collated and documented based on the workshop presentations. Further, based on individual teaching experiences and methodologies shared by the presenters, it was noted that active learning techniques, including cooperative and collaborative learning, can be useful for teaching undergraduate courses while problem based learning (PBL) can be a beneficial method for graduate courses. The outcomes of the education initiative will help produce students who are knowledgeable in the subject of biotransport, facile in applying biotransport concepts for solving problems in various application areas, and comfortable with their own abilities as life-long learners.

AlterNet-K: a small and compact model for the detection of glaucoma.

Glaucoma is one of the leading causes of permanent blindness in the world. It is caused due to an increase in the intraocular pressure within the eye that harms the optic nerve. People suffering from Glaucoma often do not notice any changes in their vision in the early stages. However, as it progresses, Glaucoma usually leads to vision loss that is irreversible in many cases. Thus, early diagnosis of this eye disease is of critical importance. The fundus image is one of the most used diagnostic tools for glaucoma detection. However, drawing accurate insights from these images requires them to be manually analyzed by medical experts, which is a time-consuming process. In this work, we propose a parameter-efficient AlterNet-K model based on an alternating design pattern, which combines ResNets and multi-head self-attention (MSA) to leverage their complementary properties to improve the generalizability of the overall model. The model was trained on the Rotterdam EyePACS AIROGS dataset, comprising 113,893 colour fundus images from 60,357 subjects. The AlterNet-K model outperformed transformer models such as ViT, DeiT-S, and Swin transformer, standard DCNN models including ResNet, EfficientNet, MobileNet and VGG with an accuracy of 0.916, AUROC of 0.968 and F1 score of 0.915. The results indicate that smaller CNN models combined with self-attention mechanisms can achieve high classification accuracies. Small and compact Resnet models combined with MSA outperform their larger counterparts. The models in this work can be extended to handle classification tasks in other medical imaging domains.

Comparison of homemade mask designs based on calculated infection risk, using actual COVID-19 infection scenarios.

During pandemics such as COVID-19, shortages of approved respirators necessitate the use of alternative masks, including homemade designs. The effectiveness of the masks is often quantified in terms of the ability to filter particles. However, to formulate public policy the efficacy of the mask in reducing the risk of infection for a given population is considerably more useful than its filtration efficiency (FE). The effect of the mask on the infection profile is complicated to estimate as it depends strongly upon the behavior of the affected population. A recently introduced tool known as the dynamic-spread model is well suited for performing population-specific risk assessment. The dynamic-spread model was used to simulate the performance of a variety of mask designs (all used for source control only) in different COVID-19 scenarios. The efficacy of different masks was found to be highly scenario dependent. Switching from a cotton T-shirt of 8% FE to a 3-layer cotton-gauze-cotton mask of 44% FE resulted in a decrease in number of new infections of about 30% in the New York State scenario and 60% in the Harris County, Texas scenario. The results are valuable to policy makers for quantifying the impact upon the infection rate for different intervention strategies, e.g., investing resources to provide the community with higher-filtration masks.

Localization of lung abnormalities on chest X-rays using self-supervised equivariant attention.

Chest X-Ray (CXR) images provide most anatomical details and the abnormalities on a 2D plane. Therefore, a 2D view of the 3D anatomy is sometimes sufficient for the initial diagnosis. However, close to fourteen commonly occurring diseases are sometimes difficult to identify by visually inspecting the images. Therefore, there is a drift toward developing computer-aided assistive systems to help radiologists. This paper proposes a deep learning model for the classification and localization of chest diseases by using image-level annotations. The model consists of a modified Resnet50 backbone for extracting feature corpus from the images, a classifier, and a pixel correlation module (PCM). During PCM training, the network is a weight-shared siamese architecture where the first branch applies the affine transform to the image before feeding to the network, while the second applies the same transform to the network output. The method was evaluated on CXR from the clinical center in the ratio of 70:20 for training and testing. The model was developed and tested using the cloud computing platform Google Colaboratory (NVidia Tesla P100 GPU, 16 GB of RAM). A radiologist subjectively validated the results. Our model trained with the configurations mentioned in this paper outperformed benchmark results. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-022-00249-5.

Validation of a Multiscale Computational Model Using a Mock Circulatory Loop to Simulate Cardiogenic Shock.

The objectives of this study are to characterize the hemodynamics of cardiogenic shock (CS) through a computational model validated using a mock circulatory loop (MCL) and to perform sensitivity analysis and uncertainty propagation studies after the American Society of Mechanical Engineers (ASME) Validation and Verification (V&V) guidelines. The uncertainties in cardiac cycle time ( ), total resistance ( ), and total volume ( ) were quantified in the MCL and propagated in the computational model. Both models were used to quantify the pressure in the left atrium, aorta (Ao), and left ventricle (LV), along with the flow through the aortic valve, reaching a good agreement. The results suggest that 1) is the main source of uncertainty in the variables under study, 2) showed its greatest impact on the uncertainty of Ao hemodynamics, and 3) mostly affected the uncertainty of LV pressure and Ao flow at the late-systolic phase. Comparison of uncertainty levels in the computational and experimental results was used to infer the presence of additional contributing factors that were not captured and propagated during a first analysis. Future work will expand upon this study to analyze the impact of mechanical circulatory support devices, such as ventricular assist devices, under CS conditions.

In Silico Fit Evaluation of Additively Manufactured Face Coverings.

In response to the respiratory protection device shortage during the COVID-19 pandemic, the additive manufacturing (AM) community designed and disseminated numerous AM face masks. Questions regarding the effectiveness of AM masks arose because these masks were often designed with limited (if any) functional performance evaluation. In this study, we present a fit evaluation methodology in which AM face masks are virtually donned on a standard digital headform using finite element-based numerical simulations. We then extract contour plots to visualize the contact patches and gaps and quantify the leakage surface area for each mask frame. We also use the methodology to evaluate the effects of adding a foam gasket and variable face mask sizing, and finally propose a series of best practices. Herein, the methodology is focused only on characterizing the fit of AM mask frames and does not considering filter material or overall performance. We found that AM face masks may provide a sufficiently good fit if the sizing is appropriate and if a sealing gasket material is present to fill the gaps between the mask and face. Without these precautions, the rigid nature of AM materials combined with the wide variation in facial morphology likely results in large gaps and insufficient adaptability to varying user conditions which may render the AM face masks ineffective.

Mock circulatory loop generated database for dynamic characterization of pressure-based cardiac output monitoring systems.

Pulse contour cardiac output monitoring systems allow real-time and continuous estimation of hemodynamic variables such as cardiac output (CO) and stroke volume variation (SVV) by analysis of arterial blood pressure waveforms. However, evaluating the performance of CO monitoring systems to measure the small variations in these variables sometimes used to guide fluid therapy is a challenge due to limitations in clinical reference methods. We developed a non-clinical database as a tool for assessing the dynamic attributes of pressure-based CO monitoring systems, including CO response time and CO and SVV resolutions. We developed a mock circulation loop (MCL) that can simulate rapid changes in different parameters, such as CO and SVV. The MCL was configured to simulate three different states (normovolemic, cardiogenic shock, and hyperdynamic) representing a range of flow and pressure conditions. For each state, we simulated stepwise changes in the MCL flow and collected datasets for characterizing pressure-based CO systems. Nine datasets were generated that contain hours of peripheral pressure, central flow and pressure waveforms. The MCL-generated database is provided open access as a tool for evaluating dynamic characteristics of pressure-based CO algorithms and systems in detecting variations in CO and SVV indices. In an example application of the database, a CO response time of 10 s, CO and SVV resolutions with lower and upper limits of (-9.1%, 8.4%) and (-5.0%, 3.8%), respectively, were determined for a pressure-based CO benchtop system. This tool will support a more comprehensive assessment of pressure-based CO monitoring systems and algorithms.

Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model.

When formulating countermeasures to epidemics such as those generated by COVID-19, estimates of the benefits of a given intervention for a specific population are highly beneficial to policy makers. A recently introduced tool, known as the "dynamic-spread" SIR model, can perform population-specific risk assessment. Behavior is quantified by the dynamic-spread function, which includes the mechanisms of droplet reduction using facemasks and transmission control due to social distancing. The spread function is calibrated using infection data from a previous wave of the infection, or other data felt to accurately represent the population behaviors. The model then computes the rate of spread of the infection for different hypothesized interventions, over the time window for the calibration data. The dynamic-spread model was used to assess the benefit of three enhanced intervention strategies - increased mask filtration efficiency, higher mask compliance, and elevated social distancing - in four COVID-19 scenarios occurring in 2020: the first wave (i.e. until the first peak in numbers of new infections) in New York City; the first wave in New York State; the spread aboard the Diamond Princess Cruise Liner; and the peak occurring after re-opening in Harris County, Texas. Differences in the efficacy of the same intervention in the different scenarios were estimated. As an example, when the average outward filtration efficiency for facemasks worn in New York City was increased from an assumed baseline of 67% to a hypothesized 90%, the calculated peak number of new infections per day decreased by 40%. For the same baseline and hypothesized filtration efficiencies aboard the Diamond Princess Cruise liner, the calculated peak number of new infections per day decreased by about 15%. An important factor contributing to the difference between the two scenarios is the lower mask compliance (derivable from the spread function) aboard the Diamond Princess.

Simulating Radial Pressure Waveforms with a Mock Circulatory Flow Loop to Characterize Hemodynamic Monitoring Systems.

PURPOSE: Mock circulatory loops (MCLs) can reproducibly generate physiologically relevant pressures and flows for cardiovascular device testing. These systems have been extensively used to characterize the performance of therapeutic cardiac devices, but historically MCLs have had limited use for assessing patient monitoring systems. Here, we adapted an MCL to include peripheral components and evaluated its utility for qualitative and quantitative benchtop testing of hemodynamic monitoring devices. METHODS: An MCL was designed to simulate three physiological hemodynamic states: normovolemia, cardiogenic shock, and hyperdynamic circulation. The system was assessed for stability in pressure and flow values over time, repeatability, waveform morphology, and systemic-peripheral pressure relationships. RESULTS: For each condition, cardiac output was controlled to the nearest 0.2 L/min, and flow rate and mean arterial pressure remained stable and repeatable over a 60-s period (n = 5, standard deviation of ± 0.1 L/min and ± 0.84 mmHg, respectively). Transfer function analyses showed that the systemic-peripheral relationships could be adequately manipulated. The results from this MCL were comparable to those from other published MCLs and computational simulations. However, resolving current limitations of the system would further improve its utility. Three pulse contour analysis algorithms were applied to the pressure and flow data from the MCL to demonstrate the potential role of MCLs in characterizing hemodynamic monitoring systems. CONCLUSION: Overall, the development of robust analysis methods in conjunction with modified MCLs can expand device testing applications to hemodynamic monitoring systems. Properly validated MCLs can create a stable and reproducible environment for testing patient monitoring systems over their entire operating ranges prior to clinical use.

Retrospective analysis of interventions to epidemics using dynamic simulation of population behavior.

Retrospective analyses of interventions to epidemics, in which the effectiveness of strategies implemented are compared to hypothetical alternatives, are valuable for performing the cost-benefit calculations necessary to optimize infection countermeasures. SIR (susceptible-infected-removed) models are useful in this regard but are limited by the challenge of deciding how and when to update the numerous parameters as the epidemic changes in response to population behaviors. Behaviors of particular interest include facemask adoption (at various levels) and social distancing. We present a method that uses a "dynamic spread function" to systematically capture the continuous variation in the population behavior and the gradual change in infection evolution, resulting from interventions. No parameter updates are made by the user. We use the tool to quantify the reduction in infection rate realizable from the population of New York City adopting different facemask strategies during COVID-19. Assuming a baseline facemask of 67% filtration efficiency, calculations show that increasing the efficiency to 80% could have reduced the roughly 5000 new infections per day occurring at the peak of the epidemic to around 4000. Population behavior that may not be varied as part of the retrospective analysis, such as social distancing in a facemask analysis, are automatically captured as part of the calibration of the dynamic spread function.

A computational model for predicting changes in infection dynamics due to leakage through N95 respirators.

In the absence of fit-testing, leakage of aerosolized pathogens through the gaps between the face and N95 respirators could compromise the effectiveness of the device and increase the risk of infection for the exposed population. To address this issue, we have developed a model to estimate the increase in risk of infection resulting from aerosols leaking through gaps between the face and N95 respirators. The gaps between anthropometric face-geometry and N95 respirators were scanned using computed tomography. The gap profiles were subsequently input into CFD models. The amount of aerosol leakage was predicted by the CFD simulations. Leakage levels were validated using experimental data obtained using manikins. The computed amounts of aerosol transmitted to the respiratory system, with and without leaks, were then linked to a risk-assessment model to predict the infection risk for a sample population. An influenza outbreak in which 50% of the population deployed respirators was considered for risk assessment. Our results showed that the leakage predicted by the CFD model matched the experimental data within about 13%. Depending upon the fit between the headform and the respirator, the inward leakage for the aerosols ranged between 30 and 95%. In addition, the non-fit-tested respirator lowered the infection rate from 97% (for no protection) to between 42 and 80%, but not to the same level as the fit-tested respirators (12%). The CFD-based leakage model, combined with the risk-assessment model, can be useful in optimizing protection strategies for a given population exposed to a pathogenic aerosol.

Evaluation of pulmonary artery stenosis in congenital heart disease patients using functional diagnostic parameters: An in vitro study.

Congenital pulmonary artery (PA) stenosis is often associated with abnormal PA hemodynamics including increased pressure drop (Δp) and reduced asymmetric flow (Q), which may result in right ventricular dysfunction. We propose functional diagnostic parameters, pressure drop coefficient (CDP), energy loss (Eloss), and normalized energy loss (E¯loss) to characterize pulmonary hemodynamics, and evaluate their efficacy in delineating stenosis severity using in vitro experiments. Subject-specific test sections including the main PA (MPA) bifurcating into left and right PAs (LPA, RPA) with a discrete LPA stenosis were manufactured from cross-sectional imaging and 3D printing. Three clinically-relevant stenosis severities, 90% area stenosis (AS), 80% AS, and 70% AS, were evaluated at different cardiac outputs (COs). A benchtop flow loop simulating pulmonary hemodynamics was used to measure Q and Δp within the test sections. The experimental Δp-Q characteristics along with clinical data were used to obtain pathophysiologic conditions and compute the diagnostic parameters. The pathophysiologic QLPA decreased as the stenosis severity increased at a fixed CO. CDPLPA, Eloss,LPA (absolute), and E¯loss,LPA (absolute) increased with an increase in LPA stenosis severity at a fixed CO. Importantly, CDPLPA and E¯loss,LPA had reduced variability with CO, and distinct values for each LPA stenosis severity. Under variable CO, a) CDPLPA values were 14.5-21.0 (70% AS), 60.7- 2.2 (80% AS), ≥ 261.6 (90% AS), and b) E¯loss,LPA values (in mJ per QLPA) were -501.9 to -1023.8 (70% AS), -1247.6 to -1773.0 (80% AS), -1934.5 (90% AS). Hence, CDPLPA and E¯loss,LPA are expected to assess the true functional severity of PA stenosis.

Three-dimensional printing and virtual surgery for congenital heart procedural planning.

Complex unrepaired congenital heart disease requires extensive planning to determine the optimal procedural approach. Conventional noninvasive diagnostic imaging initially provides only two-dimensional (2D) representations of the complex, three-dimensional cardiovascular anatomy. With the expansion of 3D visualization techniques in imaging, a paradigm shift has occurred in complex congenital heart disease surgical planning using digital and 3D printed heart models. There has been early success in demonstrating the benefit of these models in interdisciplinary communication and education. The future goal of this work is to demonstrate a clinical outcome benefit using digital and 3D printed models to plan both surgical and catheterization-based interventional procedures. Ultimately, the hope is that advanced procedural planning with virtual surgery and 3D printing will enhance decision-making in complex congenital heart disease cases resulting in improved perioperative performance by reducing operative times, complications, and reoperations.

Use of the FDA nozzle model to illustrate validation techniques in computational fluid dynamics (CFD) simulations.

A "credible" computational fluid dynamics (CFD) model has the potential to provide a meaningful evaluation of safety in medical devices. One major challenge in establishing "model credibility" is to determine the required degree of similarity between the model and experimental results for the model to be considered sufficiently validated. This study proposes a "threshold-based" validation approach that provides a well-defined acceptance criteria, which is a function of how close the simulation and experimental results are to the safety threshold, for establishing the model validity. The validation criteria developed following the threshold approach is not only a function of Comparison Error, E (which is the difference between experiments and simulations) but also takes in to account the risk to patient safety because of E. The method is applicable for scenarios in which a safety threshold can be clearly defined (e.g., the viscous shear-stress threshold for hemolysis in blood contacting devices). The applicability of the new validation approach was tested on the FDA nozzle geometry. The context of use (COU) was to evaluate if the instantaneous viscous shear stress in the nozzle geometry at Reynolds numbers (Re) of 3500 and 6500 was below the commonly accepted threshold for hemolysis. The CFD results ("S") of velocity and viscous shear stress were compared with inter-laboratory experimental measurements ("D"). The uncertainties in the CFD and experimental results due to input parameter uncertainties were quantified following the ASME V&V 20 standard. The CFD models for both Re = 3500 and 6500 could not be sufficiently validated by performing a direct comparison between CFD and experimental results using the Student's t-test. However, following the threshold-based approach, a Student's t-test comparing |S-D| and |Threshold-S| showed that relative to the threshold, the CFD and experimental datasets for Re = 3500 were statistically similar and the model could be considered sufficiently validated for the COU. However, for Re = 6500, at certain locations where the shear stress is close the hemolysis threshold, the CFD model could not be considered sufficiently validated for the COU. Our analysis showed that the model could be sufficiently validated either by reducing the uncertainties in experiments, simulations, and the threshold or by increasing the sample size for the experiments and simulations. The threshold approach can be applied to all types of computational models and provides an objective way of determining model credibility and for evaluating medical devices.