Biomedical Engineering Reference
In-Depth Information
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Developing novel display techniques that make it easy to relate
information in different images with very different resolutions, and
for nonrigid registration, to provide intuitive visualization of the
deformation field which may have scalar, vector, or tensor values,
depending on the application
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Developing and validating complete applications. Registration is
but one component in what may be a sophisticated chain of pro-
cessing tasks. Solving the whole image processing and analysis task
for specific clinical applications will be an important focus
In thinking about future image registration development, it is important to
return to the topic of correspondence introduced in Chapter 2. Image regis-
tration is about establishing point-by-point correspondence between two
images (or an image and physical space). While the basic definition of corre-
spondence is clear, its meaning in particular applications may not be. For
example, when tracking change in one subject over a short time interval, cor-
respondence refers to a specific element of tissue within the patient. This is
less clear when comparing one subject with another where correspondence
could be related to shape, histological characteristics, or metabolic function.
Correspondence between images of a patient who has changed position
(where no tissue is gained or lost) is different from studying images of a
patient over time, where tissue may grow, shrink, or be surgically removed.
It is not yet clear whether these different registration problems will be solved
by quite different algorithms, or whether an underlying unified approach
will be successful. The next few years will be very interesting in the field of
image registration as greater clinical use is made of techniques that have
become established in research literature, and as new techniques are devised,
in particular for different applications of nonrigid registration.
References
1. Dawant, B.M., Hartmann, S.L., Thirion, J.P., Maes, F., Vandermeulen, D., and
Demaerel, P., Automatic 3-D segmentation of internal structures of the head in
MR images using a combination of similarity and free-form transformations: Part I,
methodology and validation on normal subjects.
IEEE Trans. Med. Imaging
18:
909-916, 1999.
2. Thesen, S., Heid, O., Mueller, E., and Schad, L.R., Prospective acquisition correc-
tion for head motion with image-based tracking for real-time fMRI.
Mag. Reson.
Med.
44: 457-463, 2000.
