Biomedical Engineering Reference
In-Depth Information
important when studying very subtle changes in the intensities or sizes of
structures from images taken over a period of time. Interpolation errors can
easily exceed the original image noise and can swamp subtle changes that
would otherwise be detectable in subtraction images. The mathematics of these
issues are addressed in Chapter 3, with applications described in Chapter 7.
Unfortunately, accurate sinc interpolation can be extremely time consuming
even on very fast computers, and can, therefore, limit applicability. Recent
innovations in this area such as shear transformations are making high qual-
ity interpolation much faster.
Some consideration needs to be given to the spatial resolution and pixel or
voxel sizes of the two images. Transforming from a high resolution modality
such as CT or MR with voxel sizes of perhaps 1
1 mm or finer onto a
voxel grid from, for example, PET with a voxel size of 3
1
3 mm will
result, inevitably, in loss of information. On the other hand, transforming a
PET image onto the grid of an MR or CT image will dramatically increase the
memory required to store the PET image (by a factor of 27 in this example),
unless some form of data compression is used. The choice of the final trans-
formed image-sampling grid will depend on the specific application.
3
2.8
Validation
Complex software has to be verified and validated. This is particularly impor-
tant in medical applications, where erroneous results can risk a patient's health
or even life. Verification is the process by which the software is shown to do
what it is specified to do (e.g., maximize mutual information). The software
industry has developed standards, protocols, and quality procedures for veri-
fication. This is an important topic, but beyond the scope of this topic.
Validation is the process whereby the software is shown to satisfy the needs of
the application with accuracy and other performance criteria (e.g., register two
images within a certain tolerance, within a certain processing time, and with less
than a certain rate of failure). Validation of image registration algorithms will usu-
ally follow a sequence of measurements using computer-generated models
(software phantoms), images of physical phantoms of accurately known con-
struction and dimensions, and images of patients or volunteers. The process
must demonstrate both high robustness and high accuracy. Robustness
implies a very low failure rate and, if failure does occur, that this is commu-
nicated to the user. Assessment of accuracy requires knowledge of a “gold
standard” or “ground truth” registration. This is difficult to achieve with clin-
ical images, but several methods have recently been reported. These are
described in more detail in Chapter 6.
Finally, in any new technology applied to medicine we must evaluate
whether there is a clear benefit to the patient and, if so, that it is achieved
