Biomedical Engineering Reference
In-Depth Information
•
Modify the model based on this data coupled to a mathematical
description of tissue deformation
•
Update (i.e., deform) the preoperatively obtained high resolution
images according to the computed three-dimensional deformation
field
This approach exploits the wealth of high resolution preoperative data which
is routinely available on a case-by-case basis, while taking advantage of
incomplete intraoperative information and low-cost, high-performance com-
puting to reduce registration errors which develop concurrent with surgery.
If this form of preoperative image compensation can be developed, it may be
possible to address the problem of tissue motion during image-guided pro-
cedures in many instances without involving volumetric intraoperative
imaging, which, while intuitively appealing, can be both expensive and cum-
bersome within the traditional OR environment. Even in the setting where
high resolution intraoperative MR is available,
5,6
computational estimates of
volumetric tissue displacement are likely to be useful, for example, as an
intermediate update path between full intraoperative image acquisitions as
in the case of a twin operating theater (surgery + imaging), or when preoper-
ative information cannot be duplicated in the OR as in the case of functional
studies (e.g., fMRI). Hence, there is considerable rationale for and interest in
developing computational models of tissue deformation for improving
image guidance.
Interestingly, the potential of using model-based computations in the con-
text of image registration has been recognized for a number of years. There is
a significant body of literature associated with the matching of medical
images when deformation is involved, which are reviewed in Chapter 13.
Typically, pre- and postcondition images exist which need to be matched or a
patient-specific image is conformed to a reference atlas. Approaches for elas-
tically matching two existing images usually involve the optimization of a
prescribed cost function, and a variety of techniques have been developed
(e.g., see references 7 and 8, among others). In all of these efforts, the primary
task has been to transform one known image into the shape of another known
image without any knowledge of the physical driving forces involved. Intra-
operatively, one has the luxury of being able to model the physical events
which take place during surgery in order to account for tissue motion.
This chapter focuses on these later models. It provides an abbreviated
historical perspective on brain tissue mechanical modeling and some of
the mathematical options that are available for representing brain tissue
mechanical response to surgical interventions. Of these options, consolida-
tion theory is followed as an example framework for discussing computa-
tional implementation and validation. Discussion of the types of model
parameters and sources of data input needed for model computations is
highlighted. Examples of updated images from
modeling, including
in the human OR, are described. The focus here is modeling for neurosurgical
in vivo
