Abstract #2569
Estimating 3D deformable motion from a series of fast 2D MRI images with CLARET
Jason Brown 1 , Cihat Eldeniz 1 , Wolfgang Rehwald 2 , Brian Dale 3 , Hongyu An 1 , and David Lalush 1
1
Joint Department of Biomedical Engineering,
The University of North Carolina at Chapel Hill and
North Carolina State University, Chapel Hill, NC, United
States,
2
Siemens
Healthcare, Malvern, PA, United States,
3
Siemens
Healthcare, Cary, NC, United States
In this application, we effectively estimated
patient-specific 3D deformable motion from fast 2D MRI
images. CLARET is an image registration method that has
been used to relate a set of 2D images to a
corresponding set of 3D images. Using CLARET to predict
the 3D motion of a subject from a set of 2D projection
images has the potential to be used in MRI imaging of
dynamic processes. The results of the registration give
a motion estimate that reduced alignment error mean and
variance in 2D frames. We concluded that CLARET can be
used effectively in an MRI setting.
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