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Abstract #4152

Automatic Fiducial Detection in T2 Weighted MRI in a Manifold Learning and Gaussian Mixture Modeling Framework

S. Ghose 1 , J. Mitra 1 , D. Rivest Henault 1 , A. Fazlollahi 1 , P. Stanwell 2 , P. Greer 3 , P. Pichler 3 , J. Fripp 1 , and J. Dowling 1

1 Australian e-Health Research Centre, CSIRO Digital Productivity Flagship, Herston, QLD, Australia, 2 University of Newcastle, NSW, Australia, 3 Department of Radiation Oncology, Calvary Mater Newcastle Hospital, NSW, Australia

Gold seeds or fiducials implanted in the prostate prior to radiation treatment are frequently used to enable the rigid registration of the two modalities required for the transfer of the prostate contours from MRI to CT. An automatic efficient detection method for the fiducials from MRI is necessary to automate the procedure. This work proposes Gaussian mixture modeling (GMM) and spectral clustering based methods for fiducial candidate selection and a similarity score based fiducial detection. The proposed approach detects fiducials with an accuracy of 95% when compared to the manual detection.

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