CircOpt

CircOpt is an optimization framework for the tuning of patient/subject-specific circulatory models. It is implemented in Matlab and uses CircAdapt for model evaluations.

Here is a figure showing the geometric parameters and objective metrics used in the circulatory optimization. (A) A schematic of the CircAdapt model highlighting key geometric dimensions of major model components. Non-geometric parameter tuning is also available in CircOpt. (B) A typical simulated left ventricular pressure-volume loop showing all partial objective metrics.

Features

This project contains a number of features that support the automation of model tuning:

• Patient/subject-specific data are organized in a configuration file. Missing values are updated from the reference values as well as possible through a rules-based system.
• Models are tuned from the reference configuration over a variable number of steps for stability. Failures detection stops diverging models early.
• Model parameters to be tuned are listed in a struct. The initialization of reference values and scaling is automatically handled.
• Models are evaluated in parallel during the optimization
• Results are logged and queried to prevent duplicate model evaluations.
• Left ventricular pressure-volume loops can be easily plotted for visual inspection.

Dependencies

The core of the cardiovascular circulatory model is CircAdapt, which can be downloaded directly from their website.

This program also uses solvers from the Matlab Optimization Toolbox.

Installation

1. Clone the CircOpt repository
2. Download CircAdapt and place it in worker0/
3. Manually modify CircAdapt as described in diff.txt

Using CircOpt

1. Follow the example syntax in Run.m to tune a model
2. Change the values in 'data...m' (and make sure to reference that file in Run.m) to provide patient-specific data for tuning

File Organization

• 'worker0/': Contains the template that will be cloned for each cpu worker at optimization runtime
• 'params...Ref.m': Contain reference and initialization data for a specific patient or subject species
• 'data...m': Contain data for a specific patient or subject
• 'opt...m': Tune CircAdapt to a specific patient or subject
• 'eval...m': Evaluate a given patient or subject CircAdapt configuration
• 'step...m': Evaluate a single perturbation step within a given patient or subject CircAdapt configuration

Publications

1. Joshua Mineroff, Balaji Sesha Sarath Pokuri, Adarsh Krishnamurthy, and Baskar Ganapathysubramanian (In Review). Optimization Framework for Patient-Specific Modeling Under Uncertainty. International Journal for Numerical Methods in Biomedical Engineering
2. Joshua Mineroff, Andrew D. McCulloch, David Krummen, Baskar Ganapathysubramanian, and Adarsh Krishnamurthy (Accepted 2019). Optimization Framework for Patient-Specific Cardiac Modeling. Cardiovascular Engineering and Technology
3. Joshua Mineroff (2018). An Optimization and Uncertainty Quantification Framework for Patient-Specific Cardiac Modeling. Graduate Theses and Dissertations

Licensing

This code is licensed under MIT license.