Biomedical Engineering Reference
In-Depth Information
International concern about escalating healthcare costs drives develop-
ment of methods that make the best possible use of medical images and, once
again, image registration can help. However, medical image registration does
not just enable better use of images that would be acquired anyway, it also
opens up new applications for medical images. These include serial imaging
to monitor subtle changes due to disease progression or treatment; perfusion
or other functional studies when the subject cannot be relied upon to remain
in a fixed position during the dynamic acquisition; and image-guided inter-
ventions, in which images acquired prior to the intervention are registered
with the treatment device, enabling the surgeon or interventionalist to use
the preintervention images to guide his or her work. Image registration has
also become a valuable technique for biomedical research, especially in neu-
roscience, where imaging studies are making substantial contributions to our
understanding of the way the brain works. Image registration can be used to
align multiple images from the same individual (intrasubject registration) and
to compare images acquired from different subjects (intersubject registration).
All the images that we wish to register or manipulate in any other way on
a computer must be available in digital form. This means that most medical
images are made up of a rectangular array of small square or rectangular ele-
ments called
); each pixel has an
associated image intensity value. This array provides the coordinate system
of the image, and an element in the image can be accessed by its two-dimen-
sional position within this array. A typical CT slice will be formed of 512
pixels
(an abbreviation of
picture elements
512
pixels, and each will correspond to an element of the cut through the patient
of about 0.5
2
. This dimension determines the limiting spatial reso-
lution of the image. 2D images are often stacked together to form a 3D vol-
ume, and many images are now acquired directly as 3D volumes. Each pixel
will now correspond to a small volume element of tissue, or
0.5 mm
. If the slice
spacing in high resolution CT is, say, 1.5 mm, the voxel size will be 0.5
voxel
0.5
3
. The number stored in each voxel—the voxel image intensity—will
be some average of a physical attribute measured over this volume. In MR,
voxels are generally slightly larger, typically 1
1.5 mm
3
in size.
Radiologists have traditionally reviewed medical images by viewing them
as film transparencies on a back-illuminated light box. Most imaging modal-
ities involve some digital manipulation and computation, and so these
images are now often stored in digital form and displayed on a workstation.
Digital storage greatly facilitates further digital manipulation, such as regis-
tration of the images and fusion of the information from the different modal-
ities. Subjective judgments of the relative size, shape, and spatial
relationships of visible structures and physiology inferred from intensity dis-
tributions are used for developing a diagnosis, planning therapy, and moni-
toring disease progression or response to therapy. A key process when
interpreting these images together is the explicit or implicit establishment of
correspondence between different points in the images. The spatial integrity
of the images can allow very accurate correspondence to be determined.
Once correspondence has been established in a verifiable way, multiple
1
3-5 mm
