Alexander D. Kaiser is a computational scientist and applied mathematician who researches modeling and simulation of heart mechanics. His doctoral work focused on the mitral valve. He currently works in the Stanford Cardiovascular Biomechanics Computation Laboratory, led by Alison Marsden, on modeling cardiac disease.
Member (Staff), Cardiovascular Institute
Honors & Awards
Benchmark Capital Fellowship in Congenital Cardiovascular Bioengineering, The Wall Center, Stanford University (7/2020)
Mechanisms and Innovation in Cardiovascular Disease, T32 training fellowship, National Heart Lung and Blood Institute, National Institutes of Health via Stanford CVI (6/2018)
Kurt O. Friedrichs Prize for Outstanding Dissertation in Mathematics, Courant Institute of Mathematical Sciences, New York University (4/2018)
Thomas Tyler Bringley Fellowship, Courant Institute of Mathematical Sciences, New York University (4/2016)
Math Master’s Thesis Prize, Courant Institute of Mathematical Sciences, New York University (4/2014)
NSF Graduate Research Fellowship, National Science Foundation (4/2013)
Education & Certifications
Doctor of Philosophy, New York University, Mathematics (2017)
Master of Science, New York University, Mathematics (2013)
Bachelor of Arts, University of California, Berkeley, Mathematics (2009)
Professional Affiliations and Activities
Postdoctoral scholar, Institute for Computational & Mathematical Engineering, Stanford University (2017 - 2022)
Controlled Comparison of Simulated Hemodynamics Across Tricuspid and Bicuspid Aortic Valves.
Annals of biomedical engineering
Bicuspid aortic valve is the most common congenital heart defect, affecting 1-2% of the global population. Patients with bicuspid valves frequently develop dilation and aneurysms of the ascending aorta. Both hemodynamic and genetic factors are believed to contribute to dilation, yet the precise mechanism underlying this progression remains under debate. Controlled comparisons of hemodynamics in patients with different forms of bicuspid valve disease are challenging because of confounding factors, and simulations offer the opportunity for direct and systematic comparisons. Using fluid-structure interaction simulations, we simulate flows through multiple aortic valve models in a patient-specific geometry. The aortic geometry is based on a healthy patient with no known aortic or valvular disease, which allows us to isolate the hemodynamic consequences of changes to the valve alone. Four fully-passive, elastic model valves are studied: a tricuspid valve and bicuspid valves with fusion of the left- and right-, right- and non-, and non- and left-coronary cusps. The resulting tricuspid flow is relatively uniform, with little secondary or reverse flow, and little to no pressure gradient across the valve. The bicuspid cases show localized jets of forward flow, excess streamwise momentum, elevated secondary and reverse flow, and clinically significant levels of stenosis. Localized high flow rates correspond to locations of dilation observed in patients, with the location related to which valve cusps are fused. Thus, the simulations support the hypothesis that chronic exposure to high local flow contributes to localized dilation and aneurysm formation.
View details for DOI 10.1007/s10439-022-02983-4
View details for PubMedID 35748961
Modeling the mitral valve.
International journal for numerical methods in biomedical engineering
This work is concerned with modeling and simulation of the mitral valve, one of the four valves in the human heart. The valve is composed of leaflets, the free edges of which are supported by a system of chordae, which themselves are anchored to the papillary muscles inside the left ventricle. First, we examine valve anatomy and present the results of original dissections. These display the gross anatomy and information on fiber structure of the mitral valve. Next, we build a model valve following a design-based methodology, meaning that we derive the model geometry and the forces that are needed to support a given load, and construct the model accordingly. We incorporate information from the dissections to specify the fiber topology of this model. We assume the valve achieves mechanical equilibrium while supporting a static pressure load. The solution to the resulting differential equations determines the pressurized configuration of the valve model. To complete the model we then specify a constitutive law based on a stress-strain relation consistent with experimental data that achieves the necessary forces computed in previous steps. Finally, using the immersed boundary method, we simulate the model valve in fluid in a computer test chamber. The model opens easily and closes without leak when driven by physiological pressures over multiple beats. Further, its closure is robust to driving pressures that lack atrial systole or are much lower or higher than normal.
View details for DOI 10.1002/cnm.3240
View details for PubMedID 31330567
Comparison of Immersed Boundary Simulations of Heart Valve Hemodynamics Against In Vitro 4D Flow MRI Data.
Annals of biomedical engineering
The immersed boundary (IB) method is a mathematical framework for fluid-structure interaction problems (FSI) that was originally developed to simulate flows around heart valves. Direct comparison of FSI simulations around heart valves against experimental data is challenging, however, due to the difficulty of performing robust and effective simulations, the complications of modeling a specific physical experiment, and the need to acquire experimental data that is directly comparable to simulation data. Such comparators are a necessary precursor for further formal validation studies of FSI simulations involving heart valves. In this work, we performed physical experiments of flow through a pulmonary valve in an in vitro pulse duplicator, and measured the corresponding velocity field using 4D flow MRI (4-dimensional flow magnetic resonance imaging). We constructed a computer model of this pulmonary artery setup, including modeling valve geometry and material properties via a technique called design-based elasticity, and simulated flow through it with the IB method. The simulated flow fields showed excellent qualitative agreement with experiments, excellent agreement on integral metrics, and reasonable relative error in the entire flow domain and on slices of interest. These results illustrate how to construct a computational model of a physical experiment for use as a comparator.
View details for DOI 10.1007/s10439-023-03266-2
View details for PubMedID 37378877
View details for PubMedCentralID 6328065
DynaRing: A Patient-Specific Mitral Annuloplasty Ring With Selective Stiffness Segments.
Journal of medical devices
2022; 16 (3): 031009
Annuloplasty ring choice and design are critical to the long-term efficacy of mitral valve (MV) repair. DynaRing is a selectively compliant annuloplasty ring composed of varying stiffness elastomer segments, a shape-set nitinol core, and a cross diameter filament. The ring provides sufficient stiffness to stabilize a diseased annulus while allowing physiological annular dynamics. Moreover, adjusting elastomer properties provides a mechanism for effectively tuning key MV metrics to specific patients. We evaluate the ring embedded in porcine valves with an ex-vivo left heart simulator and perform a 150 million cycle fatigue test via a custom oscillatory system. We present a patient-specific design approach for determining ring parameters using a finite element model optimization and patient MRI data. Ex-vivo experiment results demonstrate that motion of DynaRing closely matches literature values for healthy annuli. Findings from the patient-specific optimization establish DynaRing's ability to adjust the anterior-posterior and intercommissural diameters and saddle height by up to 8.8%, 5.6%, 19.8%, respectively, and match a wide range of patient data.
View details for DOI 10.1115/1.4054445
View details for PubMedID 35646225
A design-based model of the aortic valve for fluid-structure interaction.
Biomechanics and modeling in mechanobiology
This paper presents a new method for modeling the mechanics of the aortic valve and simulates its interaction with blood. As much as possible, the model construction is based on first principles, but such that the model is consistent with experimental observations. We require that tension in the leaflets must support a pressure, then derive a system of partial differential equations governing its mechanical equilibrium. The solution to these differential equations is referred to as the predicted loaded configuration; it includes the loaded leaflet geometry, fiber orientations and tensions needed to support the prescribed load. From this configuration, we derive a reference configuration and constitutive law. In fluid-structure interaction simulations with the immersed boundary method, the model seals reliably under physiological pressures and opens freely over multiple cardiac cycles. Further, model closure is robust to extreme hypo- and hypertensive pressures. Then, exploiting the unique features of this model construction, we conduct experiments on reference configurations, constitutive laws and gross morphology. These experiments suggest the following conclusions: (1) The loaded geometry, tensions and tangent moduli primarily determine model function. (2) Alterations to the reference configuration have little effect if the predicted loaded configuration is identical. (3) The leaflets must have sufficiently nonlinear material response to function over a variety of pressures. (4) Valve performance is highly sensitive to free edge length and leaflet height. These conclusions suggest appropriate gross morphology and material properties for the design of prosthetic aortic valves. In future studies, our aortic valve modeling framework can be used with patient-specific models of vascular or cardiac flow.
View details for DOI 10.1007/s10237-021-01516-7
View details for PubMedID 34549354
Patient-Specific Computational Fluid Dynamics Reveal Localized Flow Patterns Predictive of Post-Left Ventricular Assist Device Aortic Incompetence.
Circulation. Heart failure
BACKGROUND: Progressive aortic valve disease has remained a persistent cause of concern in patients with left ventricular assist devices. Aortic incompetence (AI) is a known predictor of both mortality and readmissions in this patient population and remains a challenging clinical problem.METHODS: Ten left ventricular assist device patients with de novo aortic regurgitation and 19 control left ventricular assist device patients were identified. Three-dimensional models of patients' aortas were created from their computed tomography scans, following which large-scale patient-specific computational fluid dynamics simulations were performed with physiologically accurate boundary conditions using the SimVascular flow solver.RESULTS: The spatial distributions of time-averaged wall shear stress and oscillatory shear index show no significant differences in the aortic root in patients with and without AI (mean difference, 0.67 dyne/cm2 [95% CI, -0.51 to 1.85]; P=0.23). Oscillatory shear index was also not significantly different between both groups of patients (mean difference, 0.03 [95% CI, -0.07 to 0.019]; P=0.22). The localized wall shear stress on the leaflet tips was significantly higher in the AI group than the non-AI group (1.62 versus 1.35 dyne/cm2; mean difference [95% CI, 0.15-0.39]; P<0.001), whereas oscillatory shear index was not significantly different between both groups (95% CI, -0.009 to 0.001; P=0.17).CONCLUSIONS: Computational fluid dynamics serves a unique role in studying the hemodynamic features in left ventricular assist device patients where 4-dimensional magnetic resonance imaging remains unfeasible. Contrary to the widely accepted notions of highly disturbed flow, in this study, we demonstrate that the aortic root is a region of relatively stagnant flow. We further identified localized hemodynamic features in the aortic root that challenge our understanding of how AI develops in this patient population.
View details for DOI 10.1161/CIRCHEARTFAILURE.120.008034
View details for PubMedID 34139862
Use of patient-specific computational models for optimization of aortic insufficiency after implantation of left ventricular assist device.
The Journal of thoracic and cardiovascular surgery
Aortic incompetence (AI) is observed to be accelerated in the continuous-flow left ventricular assist device (LVAD) population and is related to increased mortality. Using computational fluid dynamics (CFD), we investigated the hemodynamic conditions related to the orientation of the LVAD outflow in these patients.We identified 10 patients with new aortic regurgitation, and 20 who did not, after LVAD implantation between 2009 and 2018. Three-dimensional models of patients' aortas were created from their computed tomography scans. The geometry of the LVAD outflow graft in relation to the aorta was quantified using azimuth angles (AA), polar angles (PAs), and distance from aortic root. The models were used to run CFD simulations, which calculated the pressures and wall shear stress (rWSS) exerted on the aortic root.The AA and PA were found to be similar. However, for combinations of high values of AA and low values of PA, there were no patients with AI. The distance from aortic root to the outflow graft was also smaller in patients who developed AI (3.39 ± 0.7 vs 4.07 ± 0.77 cm, P = .04). There was no significant difference in aortic root pressures in the 2 groups. The rWSS was greater in AI patients (4.60 ± 5.70 vs 2.37 ± 1.20 dyne/cm2, P < .001). Qualitatively, we observed a trend of greater perturbations, regions of high rWSS, and flow eddies in the AI group.Using CFD simulations, we demonstrated that patients who developed de novo AI have greater rWSS at the aortic root, and their outflow grafts were placed closer to the aortic roots than those patients without de novo AI.
View details for DOI 10.1016/j.jtcvs.2020.04.164
View details for PubMedID 32653292
- Gaussian-Like Immersed Boundary Kernels with Three Continuous Derivatives and Improved Translational Invariance arXiv preprint. https://arxiv.org/abs/1505.07529v3. 2017
- Automated simplification of large symbolic expressions JOURNAL OF SYMBOLIC COMPUTATION 2014; 60: 120–36
- TORCH - Computational Reference Kernels: A Testbed for Computer Science Research Tech Report LBNL-4172E. https://escholarship.org/uc/item/8n36z5tn. 2010
- A Kernel Testbed for Parallel Architecture, Language, and Performance Research AMER INST PHYSICS. 2010: 1297–1300
- A Principled Kernel Testbed for Hardware/Software Co-Design Research USENIX Workshop on Hot Topics in Parallelism 2010
Undetected Errors in Quasi-cyclic LDPC Codes Caused by Receiver Symbol Slips
Proceedings of IEEE Global Conference on Communications
View details for DOI 10.1109/GLOCOM.2009.5425765