  Interest in the application of engineering methods to problems in congenital
heart disease has gained increased popularity over the past decade. The use of
computational simulation to examine common clinical problems including single
ventricle physiology and the associated surgical approaches, the effects of
pacemaker implantation on vascular occlusion, or delineation of the
biomechanical effects of implanted medical devices is now routinely appearing
in clinical journals within all pediatric cardiovascular subspecialties. In
practice, such collaboration can only work if both communities understand each
other's methods and their limitations. This paper is intended to facilitate
this communication by presenting in the context of congenital heart disease
(CHD) the main steps involved in performing computational simulation-from the
selection of an appropriate clinical question/problem to understanding the
computational results, and all of the "black boxes" in between. We examine the
current state of the art and areas in need of continued development. For
example, medical image-based model-building software has been developed based
on numerous different methods. However, none of them can be used to construct a
model with a simple "click of a button". The creation of a faithful,
representative anatomic model, especially in pediatric subjects, often requires
skilled manual intervention. In addition, information from a second imaging
modality is often required to facilitate this process. We describe the
technical aspects of model building, provide a definition of some of the most
commonly used terms and techniques (e.g. meshes, mesh convergence,
Navier-Stokes equations, and boundary conditions), and the assumptions used in
running the simulations. Particular attention is paid to the assignment of
boundary conditions as this point is of critical importance in the current
areas of research within the realm of congenital heart disease. Finally,
examples are provided demonstrating how computer simulations can provide an
opportunity to "acquire" data currently unobtainable by other modalities, with
essentially no risk to patients. To illustrate these points, novel simulation
examples of virtual Fontan conversion (from preoperative data to predicted
postoperative state) and outcomes of different surgical designs are presented.
The need for validation of the currently employed techniques and predicted
results are required and the methods remain in their infancy. While the daily
application of these technologies to patient-specific clinical scenarios likely
remains years away, the ever increasing interest in this area among both
clinicians and engineers makes its eventual use far more likely than ever
before and, some could argue, only a matter of [computing] time.
