Here we demostrate how you can constrain a cardiac computational model using clinical measurements such as pressure, volume and regional strain. The problem is formulated as a PDE-constrained optimisation problem where the objective functional represents the misfit between measured and simulated data. There are two phases; passive and active. In the passive phase the material parameters are the control parameters, and in the active phase the contraction parameter is the control parameter. The control parameters can be scalar or spatially resolved. The problem is solved using a gradient based optimization algorithm where the gradient is provided by solving the adjoint system.
The code here is currently undergoing a major refactoring in order for
it to be compatible with more recent versions of fenics as well as the
standalone cardiac mechanics solver
pulse.
We expect the master
branch to be stable, as development will mainly
be done on other branches.
The original code, hosted at
Bitbucket is no longer
maintained.
pulse-adjoint
is written in pure python but based on fenics
and
dolfin-adjoint
(using libabjoint
). Therefore fenics
version 2017
and dolfin-adjoint
that uses libadjoint
(not pyadjoint
) is
currently the only supported versions. You also need to install
pulse
.
As pulse-adjoint
is a pure python package you can install it by
simply typing
python setup.py install
when you are in the same folder as the setup.py
.
Documentation is found at pulse-adjoint.readthedocs.io
In order to get starting with pulse-adjoint it is beneficial to get to know FEniCS as well as the pulse, which has a lot of demos
Next you can check out the demoes in this repository. They should give an idea on how you can incorporate your own data into the pipeline. If you need to generate microstructure you can checkout out the ldrb
You are welcomed to use this code for your own reaseach, but encourage you to cite the following paper:
Finsberg, H., Xi, C., Tan, J.L., Zhong, L., Genet, M., Sundnes, J., Lee, L.C. and Wall, S.T., 2018. Efficient estimation of personalized biventricular mechanical function employing gradient‐based optimization. International journal for numerical methods in biomedical engineering, p.e2982. DOI
GNU LGPL v3
- Henrik Finsberg ([email protected])