@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{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{lee_carlson_chesler_olufsen_qureshi_smith_sochi_beard_2016, title={Heterogeneous mechanics of the mouse pulmonary arterial network}, volume={15}, ISSN={["1617-7940"]}, DOI={10.1007/s10237-015-0757-y}, abstractNote={Individualized modeling and simulation of blood flow mechanics find applications in both animal research and patient care. Individual animal or patient models for blood vessel mechanics are based on combining measured vascular geometry with a fluid structure model coupling formulations describing dynamics of the fluid and mechanics of the wall. For example, one-dimensional fluid flow modeling requires a constitutive law relating vessel cross-sectional deformation to pressure in the lumen. To investigate means of identifying appropriate constitutive relationships, an automated segmentation algorithm was applied to micro-computerized tomography images from a mouse lung obtained at four different static pressures to identify the static pressure-radius relationship for four generations of vessels in the pulmonary arterial network. A shape-fitting function was parameterized for each vessel in the network to characterize the nonlinear and heterogeneous nature of vessel distensibility in the pulmonary arteries. These data on morphometric and mechanical properties were used to simulate pressure and flow velocity propagation in the network using one-dimensional representations of fluid and vessel wall mechanics. Moreover, wave intensity analysis was used to study effects of wall mechanics on generation and propagation of pressure wave reflections. Simulations were conducted to investigate the role of linear versus nonlinear formulations of wall elasticity and homogeneous versus heterogeneous treatments of vessel wall properties. Accounting for heterogeneity, by parameterizing the pressure/distention equation of state individually for each vessel segment, was found to have little effect on the predicted pressure profiles and wave propagation compared to a homogeneous parameterization based on average behavior. However, substantially different results were obtained using a linear elastic thin-shell model than were obtained using a nonlinear model that has a more physiologically realistic pressure versus radius relationship.}, number={5}, journal={BIOMECHANICS AND MODELING IN MECHANOBIOLOGY}, author={Lee, Pilhwa and Carlson, Brian E. and Chesler, Naomi and Olufsen, Mette S. and Qureshi, M. Umar and Smith, Nicolas P. and Sochi, Taha and Beard, Daniel A.}, year={2016}, month={Oct}, pages={1245–1261} }