Implicit function-based phantoms for evaluation of registration algorithms
2005
Medical image fusion is increasingly enhancing diagnostic accuracy
by synergizing information from multiple images, obtained by the
same modality at different times or from complementary modalities
such as structural information from CT and functional from PET. An
active, crucial research topic in fusion is validation of the registration (point-to-point correspondence) used. Phantoms and
other simulated studies are useful in the absence of, or as a preliminary to, definitive clinical tests. Software phantoms in
specific have the added advantage of robustness, repeatability and
reproducibility. Our virtual-lung-phantom-based scheme can test
the accuracy of any registration algorithm and is flexible enough
for added levels of complexity (addition of blur/anti-alias, rotate/warp, and modality-associated noise) to help evaluate the
robustness of an image registration/fusion methodology. Such a
framework extends easily to different anatomies. The feature of
adding software-based fiducials both within and outside simulated
anatomies prove more beneficial when compared to experiments using
data from external fiducials on a patient. It would help the diagnosing clinician make a prudent choice of registration algorithm.
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