Image registration illustrative results
In many medical imaging problems
there is a need to align one image
For example if you acquire one X-ray CT scan
of a patient who is holding their breath
and another scan when they hold their breath
then you can register these two images
to each other
to help quantify motion that occurs
during respiration (breathing).
Understanding this motion
can help in applications
like radiation therapy
for treating patients with lung cancer.
Prof. Fessler and his graduate students
are developing improved image registration tools
using techniques from the field of image processing
(developing measures of image similarity)
and from optimization theory.
This research is supported
by the NIH National Cancer Institute
and is performed in collaboration with scientists
in the UM Department of Radiology
and the UM Department of Radiation Oncology.
One challenge in nonrigid image registration
is constraining the estimated deformation
to preserve topology.
Conventional 3D image registration using B-splines
leads to mathematical models where tissues
"fold" on themselves, which are
physically implausible deformations.
Prof. Fessler's graduate student Se Young Chun
has developed an improved image registration algorithm
(presented at ISBI 2008)
that constrains the deformation
to be locally invertible
by including a special regularization function
in the cost function that is optimized.
His method is faster than the Jacobian penalty functions
and yields improved image registration results
that are much more realistic for breathing motion.
Here are some links to page(s) that illustrate
Some tools for image registration
are available in the
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