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Abstract #4302

Predictive Magnetic Resonance Temperature Imaging with Machine Learning

Joshua P. Yung1, 2, Christopher J. MacLellan1, 2, Anil Shetty3, Roger J. McNichols3, John D. Hazle1, 2, R. Jason Stafford1, 2, David T. A. Fuentes1, 2

1Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States; 2The University of Texas Graduate School of Biomedical Sciences, Houston, TX, United States; 3Visualase, Inc, Houston, TX, United States

In previous studies, the Pennes bioheat transfer equation was used to predict temperature heating during thermal therapy. In this work, a non-physical model that takes a priori temperature measurements to predict future heating is used. The method was tested on in vivo data of laser induced interstitial thermal therapy of human brain. The prediction was run using two a priori time steps and three a priori time steps. In conclusion, the proposed method predicted future temperature values with uncertainty values allowing for confidence intervals. The mean and standard deviation values can offer additional information for the procedure.