CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation

Abstract

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization.

Fig 1. The CRIMSON workflow consists of Preprocessing, Flowsolver, and Postprocessing stages represented in three different columns.

Typical hardware architecture utilized at each stage is noted. Some of the steps within each stage are optional, depending upon the complexity of the simulation. Grey hashed boxes indicate areas where the GUI support is under development. A medical image volume is required as initial input, if not importing a discrete geometric model from elsewhere. The final output is, at minimum, time-resolved pressure and velocity fields throughout the domain.

Fig 2. Geometric modelling aspect of the CRIMSON GUI.

(1) The image data and segmentation is viewed in three adjustable orthogonal planes and a 3D projection. (2) Centerlines are created for each vessel of interest, editable via coordinates in Panel 2, or 3D interaction in Panel 1 (blue arrow shows relationship). (3) A “re-slice” of the image, consisting of a plane perpendicular to the vessel centerline; this is used to draw two-dimensional contour of the vessels (4). Data objects (centerlines, vessel lofts and trees, etc.) are shown in the data manager (5).

Fig 3. The CRIMSON Boundary Condition Toolbox.

Researchers can use this tool to design custom LPN boundary condition models by choosing and arranging components from the toolbox (1) on the workplane (2). Parameters and other component properties can be set in the Properties pane (3). Here, a depiction of a Windkessel model is being created.

Fig 4. A PC-MRI (phase & magnitude images) plane slicing through the aortic root can segmented and processed using CRIMSON, so that patient-specific velocity profiles can be imposed as boundary conditions on the model.

Fig 5. A complex, two-domain patient-specific 3D model with full closed-loop boundary condition system created in CRIMSON.

This was used in a study of cardiovascular complications during liver transplant [44]. Image used under CC BY 4.0 license.

Fig 6. The results of running a pure zero-dimensional simulation of the model shown in Fig 5.

Note the differing y-axis scales. Panel A: Case C1—the time evolution of the myocardial hunger in the left anterior descending (LAD) perfusion territory of the myocardium, in the patient-specific simulation under baseline resting conditions. The blood supply is such that the hunger is kept close to zero for the entire simulation. Panel B: Case C2—the model with parameters perturbed to simulate PRS. The hunger grows continually, indicating that the blood supply to this perfusion territory is insufficient, and thus, myocardial ischaemia.

Fig 7. Visualisation of the Netlist boundary condition circuits shown in Fig 5.

There are two connected components; during simulation, these two are joined by the two connected components of the 3D domain–the pulmonary arteries and the systemic arteries. Pressure nodes (between components) are shown as dots. Red dots indicate points of interface with the 3D domain. Lines indicate LPN components and are coloured according to type (resistor, capacitor, etc.).

Fig 8. Examples of advanced and upcoming CRIMSON features.

A: Custom aortic valve model implementation using the NEBCT and Python Control Systems Framework. B: Example of a Lagrangian particle tracking study of massless particle transport in the blood stream [46]. C: Time history of parameter convergence during Kalman filter data assimilation [12]. D: Arbitrary Lagrangian-Eulerian deformation of a vascular mesh between diastole and systole [17]. E: Support for arbitrary run-time Python code for modelling cardiovascular control systems and changes of state, by controlling component parameters during simulations [16]. F: Scalar reaction-advection-diffusion (RAD) problem with one species transported in the blood stream. Run-time specification of arbitrary reactions between tens of species is under current development [15].