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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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