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Abstract #3730

Kalman Filtered MR Temperature Imaging

David Fuentes1, Joshua Yung1, Andrew Elliott1, John D. Hazle1, Roger Jason Stafford1

1Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States

The presented work critically evaluates the ability of a Kalman Filtered MR thermal image acquisition scheme to accurately monitor a LITT procedure in the presence of corrupt or missing data. Details of the finite element-based stochastic form of the Pennes bioheat transfer model needed to achieve real-time performance within the Kalman framework are discussed. The ability to provide a robust temperature estimate in presence of data corruption was quantitatively evaluated in terms of an L2 (RMS) norm of the error. Results indicate the developed algorithm may provide a useful model-based estimate of the temperature state during a LITT procedure.