@article{taylor-lapole_colebank_weigand_olufsen_puelz_2022, title={A computational study of aortic reconstruction in single ventricle patients}, volume={11}, ISSN={["1617-7940"]}, url={https://doi.org/10.1007/s10237-022-01650-w}, DOI={10.1007/s10237-022-01650-w}, journal={BIOMECHANICS AND MODELING IN MECHANOBIOLOGY}, author={Taylor-LaPole, Alyssa M. and Colebank, Mitchel J. and Weigand, Justin D. and Olufsen, Mette S. and Puelz, Charles}, year={2022}, month={Nov} }
 @article{colebank_chesler_2022, title={An in-silico analysis of experimental designs to study right ventricular function and pulmonary hypertension}, volume={3}, url={https://doi.org/10.1101/2022.03.22.485347}, DOI={10.1101/2022.03.22.485347}, abstractNote={In-vivo studies of pulmonary hypertension (PH) have provided key insight into the progression of the disease and right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct an identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for identifiability using profile likelihood based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure volume data reveals that even basic, functional data from the LV remedies identifiability issues and provides substantial insight into biventricular interactions. Author Summary Computational models of cardiac dynamics are becoming increasingly useful in understanding the underlying mechanisms of disease. In-silico analyses are especially insightful in understanding PH and eventual RV dysfunction, as these conditions are diagnosed months to years after disease onset. Many researchers couple computational models with in-vivo experimental models of PH, yet few ever assess what data might be necessary or sufficient for parameter inference prior to designing their experiments. Here, we considered a multiscale computational model including sarcomere dynamics, biventricular interactions, and vascular hemodynamics, and assessed whether parameters could be inferred accurately given limited cardiac data. We utilized sensitivity analyses, profile likelihood confidence intervals, and MCMC to quantify parameter influence and uncertainty. We observed that RV pressure alone is not sufficient to infer the influential parameters in the model, whereas combined pressure and volume data in both the RV and LV reduced uncertainty in model parameters and in model forecasts. We conclude that synergistic PH studies utilizing computational modeling include these data to reduce issues with parameter identifiability and minimize uncertainty.}, publisher={Cold Spring Harbor Laboratory}, author={Colebank, M. J. and Chesler, N.C.}, year={2022}, month={Mar} }
 @article{colebank_chesler_2022, title={An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle}, url={https://doi.org/10.1371/journal.pcbi.1010017}, DOI={10.1371/journal.pcbi.1010017}, abstractNote={In-vivo studies of pulmonary vascular disease and pulmonary hypertension (PH) have provided key insight into the progression of right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are practically identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct a practical identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for practical identifiability using profile likelihood-based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from practical identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure-volume data reveals that at least some quantitative data from both ventricles should be included for future experimental studies.}, journal={PLOS Computational Biology}, author={Colebank, Mitchel J. and Chesler, Naomi C.}, editor={Marsden, Alison L.Editor}, year={2022}, month={Sep} }
 @article{bartolo_qureshi_colebank_chesler_olufsen_2022, title={Numerical predictions of shear stress and cyclic stretch in pulmonary hypertension due to left heart failure}, volume={1}, ISSN={["1617-7940"]}, url={https://doi.org/10.1007/s10237-021-01538-1}, DOI={10.1007/s10237-021-01538-1}, abstractNote={Isolated post-capillary pulmonary hypertension (Ipc-PH) occurs due to left heart failure, which contributes to 1 out of every 9 deaths in the United States. In some patients, through unknown mechanisms, Ipc-PH transitions to combined pre-/post-capillary PH (Cpc-PH) and is associated with a dramatic increase in mortality. Altered mechanical forces and subsequent biological signaling in the pulmonary vascular bed likely contribute to the transition from Ipc-PH to Cpc-PH. However, even in a healthy pulmonary circulation, the mechanical forces in the smallest vessels (the arterioles, capillary bed, and venules) have not been quantitatively defined. This study is the first to examine this question via a computational fluid dynamics model of the human pulmonary arteries, arterioles, venules, and veins. Using this model, we predict temporal and spatial dynamics of cyclic stretch and wall shear stress with healthy and diseased hemodynamics. In the normotensive case for large vessels, numerical simulations show that large arteries have higher pressure and flow than large veins, as well as more pronounced changes in area throughout the cardiac cycle. In the microvasculature, shear stress increases and cyclic stretch decreases as vessel radius decreases. When we impose an increase in left atrial pressure to simulate Ipc-PH, shear stress decreases and cyclic stretch increases as compared to the healthy case. Overall, this model predicts pressure, flow, shear stress, and cyclic stretch that providing a way to analyze and investigate hypotheses related to disease progression in the pulmonary circulation.}, journal={BIOMECHANICS AND MODELING IN MECHANOBIOLOGY}, author={Bartolo, Michelle A. and Qureshi, M. Umar and Colebank, Mitchel J. and Chesler, Naomi C. and Olufsen, Mette S.}, year={2022}, month={Jan} }
 @article{colebank_qureshi_rajagopal_krasuski_olufsen_2021, title={A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension}, volume={321}, ISSN={["1522-1539"]}, url={https://doi.org/10.1152/ajpheart.00086.2021}, DOI={10.1152/ajpheart.00086.2021}, abstractNote={Chronic thromboembolic pulmonary hypertension (CTEPH) is caused by recurrent or unresolved pulmonary thromboemboli, leading to perfusion defects and increased arterial wave reflections. CTEPH treatment aims to reduce pulmonary arterial pressure and reestablish adequate lung perfusion, yet patients with distal lesions are inoperable by standard surgical intervention. Instead, these patients undergo balloon pulmonary angioplasty (BPA), a multi-session, minimally invasive surgery that disrupts the thromboembolic material within the vessel lumen using a catheter balloon. However, there still lacks an integrative, holistic tool for identifying optimal target lesions for treatment. To address this insufficiency, we simulate CTEPH hemodynamics and BPA therapy using a multiscale fluid dynamics model. The large pulmonary arterial geometry is derived from a computed tomography (CT) image, whereas a fractal tree represents the small vessels. We model ring- and web-like lesions, common in CTEPH, and simulate normotensive conditions and four CTEPH disease scenarios; the latter includes both large artery lesions and vascular remodeling. BPA therapy is simulated by simultaneously reducing lesion severity in three locations. Our predictions mimic severe CTEPH, manifested by an increase in mean proximal pulmonary arterial pressure above 20 mmHg and prominent wave reflections. Both flow and pressure decrease in vessels distal to the lesions and increase in unobstructed vascular regions. We use the main pulmonary artery (MPA) pressure, a wave reflection index, and a measure of flow heterogeneity to select optimal target lesions for BPA. In summary, this study provides a multiscale, image-to-hemodynamics pipeline for BPA therapy planning for inoperable CTEPH patients.}, number={2}, journal={AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY}, publisher={American Physiological Society}, author={Colebank, Mitchel J. and Qureshi, M. Umar and Rajagopal, Sudarshan and Krasuski, Richard A. and Olufsen, Mette S.}, year={2021}, month={Aug}, pages={H318–H338} }
 @article{colebank_qureshi_olufsen_2021, title={Sensitivity analysis and uncertainty quantification of 1-D models of pulmonary hemodynamics in mice under control and hypertensive conditions}, volume={37}, ISSN={["2040-7947"]}, url={https://doi.org/10.1002/cnm.3242}, DOI={10.1002/cnm.3242}, abstractNote={Pulmonary hypertension (PH), defined as an elevated mean blood pressure in the main pulmonary artery (MPA) at rest, is associated with vascular remodeling of both large and small arteries. PH has several sub‐types that are all linked to high mortality rates. In this study, we use a one‐dimensional (1‐D) fluid dynamics model driven by in vivo measurements of MPA flow to understand how model parameters and network size influence MPA pressure predictions in the presence of PH. We compare model predictions with in vivo MPA pressure measurements from a control and a hypertensive mouse and analyze results in three networks of increasing complexity, extracted from micro‐computed tomography (micro‐CT) images. We introduce global scaling factors for boundary condition parameters and perform local and global sensitivity analysis to calculate parameter influence on model predictions of MPA pressure and correlation analysis to determine a subset of identifiable parameters. These are inferred using frequentist optimization and Bayesian inference via the Delayed Rejection Adaptive Metropolis (DRAM) algorithm. Frequentist and Bayesian uncertainty is computed for model parameters and MPA pressure predictions. Results show that MPA pressure predictions are most sensitive to distal vascular resistance and that parameter influence changes with increasing network complexity. Our outcomes suggest that PH leads to increased vascular stiffness and decreased peripheral compliance, congruent with clinical observations.}, number={11}, journal={INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING}, publisher={Wiley}, author={Colebank, Mitchel J. and Qureshi, M. Umar and Olufsen, Mette S.}, year={2021}, month={Nov} }
 @article{paun_colebank_olufsen_hill_husmeier_2020, title={Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation}, volume={17}, url={https://doi.org/10.1098/rsif.2020.0886}, DOI={10.1098/rsif.2020.0886}, abstractNote={This study uses Bayesian inference to quantify the uncertainty of model parameters and haemodynamic predictions in a one-dimensional pulmonary circulation model based on an integration of mouse haemodynamic and micro-computed tomography imaging data. We emphasize an often neglected, though important source of uncertainty: in the mathematical model form due to the discrepancy between the model and the reality, and in the measurements due to the wrong noise model (jointly called ‘model mismatch’). We demonstrate that minimizing the mean squared error between the measured and the predicted data (the conventional method) in the presence of model mismatch leads to biased and overly confident parameter estimates and haemodynamic predictions. We show that our proposed method allowing for model mismatch, which we represent with Gaussian processes, corrects the bias. Additionally, we compare a linear and a nonlinear wall model, as well as models with different vessel stiffness relations. We use formal model selection analysis based on the Watanabe Akaike information criterion to select the model that best predicts the pulmonary haemodynamics. Results show that the nonlinear pressure–area relationship with stiffness dependent on the unstressed radius predicts best the data measured in a control mouse.}, number={173}, journal={Journal of The Royal Society Interface}, publisher={The Royal Society}, author={Paun, L. Mihaela and Colebank, Mitchel J. and Olufsen, Mette S. and Hill, Nicholas A. and Husmeier, Dirk}, year={2020}, month={Dec}, pages={20200886} }
 @article{chambers_colebank_qureshi_clipp_olufsen_2020, title={Structural and hemodynamic properties of murine pulmonary arterial networks under hypoxia-induced pulmonary hypertension}, volume={234}, ISSN={["2041-3033"]}, url={https://doi.org/10.1177/0954411920944110}, DOI={10.1177/0954411920944110}, abstractNote={Detection and monitoring of patients with pulmonary hypertension, defined as a mean blood pressure in the main pulmonary artery above 25 mmHg, requires a combination of imaging and hemodynamic measurements. This study demonstrates how to combine imaging data from microcomputed tomography images with hemodynamic pressure and flow waveforms from control and hypertensive mice. Specific attention is devoted to developing a tool that processes computed tomography images, generating subject-specific arterial networks in which one-dimensional fluid dynamics modeling is used to predict blood pressure and flow. Each arterial network is modeled as a directed graph representing vessels along the principal pathway to ensure perfusion of all lobes. The one-dimensional model couples these networks with structured tree boundary conditions representing the small arteries and arterioles. Fluid dynamics equations are solved in this network and compared to measurements of pressure in the main pulmonary artery. Analysis of microcomputed tomography images reveals that the branching ratio is the same in the control and hypertensive animals, but that the vessel length-to-radius ratio is significantly lower in the hypertensive animals. Fluid dynamics predictions show that in addition to changed network geometry, vessel stiffness is higher in the hypertensive animal models than in the control models.}, number={11}, journal={PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE}, publisher={SAGE Publications}, author={Chambers, Megan J. and Colebank, Mitchel J. and Qureshi, M. Umar and Clipp, Rachel and Olufsen, Mette S.}, year={2020}, month={Nov}, pages={1312–1329} }
 @article{colebank_paun_qureshi_chesler_husmeier_olufsen_fix_2019, title={Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries}, volume={16}, ISSN={["1742-5662"]}, url={https://doi.org/10.1098/rsif.2019.0284}, DOI={10.1098/rsif.2019.0284}, abstractNote={Computational fluid dynamics (CFD) models are emerging tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation have made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension, requiring a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation propagates to CFD model predictions, making the quantification of segmentation-induced uncertainty crucial for subject-specific models. This study quantifies the variability of one-dimensional CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of a single, excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii and network connectivity for each segmented pulmonary network. Probability density functions are computed for vessel radius and length and then sampled to propagate uncertainties to haemodynamic predictions in a fixed network. In addition, we compute the uncertainty of model predictions to changes in network size and connectivity. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length.}, number={159}, journal={JOURNAL OF THE ROYAL SOCIETY INTERFACE}, publisher={The Royal Society}, author={Colebank, Mitchel J. and Paun, L. Mihaela and Qureshi, M. Umar and Chesler, Naomi and Husmeier, Dirk and Olufsen, Mette S. and Fix, Laura Ellwein}, year={2019}, month={Oct} }
 @article{qureshi_colebank_schreier_tabima_haider_chesler_olufsen_2018, title={Characteristic impedance: frequency or time domain approach?}, volume={39}, ISSN={1361-6579}, url={http://dx.doi.org/10.1088/1361-6579/aa9d60}, DOI={10.1088/1361-6579/aa9d60}, abstractNote={Objective: Characteristic impedance (Zc) is an important component in the theory of hemodynamics. It is a commonly used metric of proximal arterial stiffness and pulse wave velocity. Calculated using simultaneously measured dynamic pressure and flow data, estimates of characteristic impedance can be obtained using methods based on frequency or time domain analysis. Applications of these methods under different physiological and pathological conditions in species with different body sizes and heart rates show that the two approaches do not always agree. In this study, we have investigated the discrepancies between frequency and time domain estimates accounting for uncertainties associated with experimental processes and physiological conditions. Approach: We have used published data measured in different species including humans, dogs, and mice to investigate: (a) the effects of time delay and signal noise in the pressure-flow data, (b) uncertainties about the blood flow conditions, (c) periodicity of the cardiac cycle versus the breathing cycle, on the frequency and time domain estimates of Zc, and (d) if discrepancies observed under different hemodynamic conditions can be eliminated. Main results and Significance: We have shown that the frequency and time domain estimates are not equally sensitive to certain characteristics of hemodynamic signals including phase lag between pressure and flow, signal to noise ratio and the end of systole retrograde flow. The discrepancies between two types of estimates are inherent due to their intrinsically different mathematical expressions and therefore it is impossible to define a criterion to resolve such discrepancies. Considering the interpretation and role of Zc as an important hemodynamic parameter, we suggest that the frequency and time domain estimates should be further assessed as two different hemodynamic parameters in a future study.}, number={1}, journal={Physiological Measurement}, publisher={IOP Publishing}, author={Qureshi, M Umar and Colebank, Mitchel J and Schreier, David A and Tabima, Diana M and Haider, Mansoor A and Chesler, Naomi C and Olufsen, Mette S}, year={2018}, month={Jan}, pages={014004} }
 @article{qureshi_colebank_paun_ellwein fix_chesler_haider_hill_husmeier_olufsen_2018, title={Hemodynamic assessment of pulmonary hypertension in mice: a model-based analysis of the disease mechanism}, volume={18}, ISSN={1617-7959 1617-7940}, url={http://dx.doi.org/10.1007/s10237-018-1078-8}, DOI={10.1007/s10237-018-1078-8}, abstractNote={This study uses a one-dimensional fluid dynamics arterial network model to infer changes in hemodynamic quantities associated with pulmonary hypertension in mice. Data for this study include blood flow and pressure measurements from the main pulmonary artery for 7 control mice with normal pulmonary function and 5 mice with hypoxia-induced pulmonary hypertension. Arterial dimensions for a 21-vessel network are extracted from micro-CT images of lungs from a representative control and hypertensive mouse. Each vessel is represented by its length and radius. Fluid dynamic computations are done assuming that the flow is Newtonian, viscous, laminar, and has no swirl. The system of equations is closed by a constitutive equation relating pressure and area, using a linear model derived from stress–strain deformation in the circumferential direction assuming that the arterial walls are thin, and also an empirical nonlinear model. For each dataset, an inflow waveform is extracted from the data, and nominal parameters specifying the outflow boundary conditions are computed from mean values and characteristic timescales extracted from the data. The model is calibrated for each mouse by estimating parameters that minimize the least squares error between measured and computed waveforms. Optimized parameters are compared across the control and the hypertensive groups to characterize vascular remodeling with disease. Results show that pulmonary hypertension is associated with stiffer and less compliant proximal and distal vasculature with augmented wave reflections, and that elastic nonlinearities are insignificant in the hypertensive animal.}, number={1}, journal={Biomechanics and Modeling in Mechanobiology}, publisher={Springer Nature}, author={Qureshi, M. Umar and Colebank, Mitchel J. and Paun, L. Mihaela and Ellwein Fix, Laura and Chesler, Naomi and Haider, Mansoor A. and Hill, Nicholas A. and Husmeier, Dirk and Olufsen, Mette S.}, year={2018}, month={Oct}, pages={219–243} }
 @article{păun_qureshi_colebank_hill_olufsen_haider_husmeier_2018, title={MCMC methods for inference in a mathematical model of pulmonary circulation}, volume={72}, ISSN={0039-0402}, url={http://dx.doi.org/10.1111/stan.12132}, DOI={10.1111/stan.12132}, abstractNote={This study performs parameter inference in a partial differential equations system of pulmonary circulation. We use a fluid dynamics network model that takes selected parameter values and mimics the behaviour of the pulmonary haemodynamics under normal physiological and pathological conditions. This is of medical interest as it enables tracking the progression of pulmonary hypertension. We show how we make the fluids model tractable by reducing the parameter dimension from a 55D to a 5D problem. The Delayed Rejection Adaptive Metropolis algorithm, coupled with constraint non‐linear optimization, is successfully used to learn the parameter values and quantify the uncertainty in the parameter estimates. To accommodate for different magnitudes of the parameter values, we introduce an improved parameter scaling technique in the Delayed Rejection Adaptive Metropolis algorithm. Formal convergence diagnostics are employed to check for convergence of the Markov chains. Additionally, we perform model selection using different information criteria, including Watanabe Akaike Information Criteria.}, number={3}, journal={Statistica Neerlandica}, publisher={Wiley}, author={Păun, L. Mihaela and Qureshi, M. Umar and Colebank, Mitchel and Hill, Nicholas A. and Olufsen, Mette S. and Haider, Mansoor A. and Husmeier, Dirk}, year={2018}, month={Apr}, pages={306–338} }