Abstract #2108

Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry

Yuxin Zhang¹, Kexin Deng¹, Shuo Chen², Bingyao Chen³, Xing Wei³, Jiafei Yang³, Shi Wang², and Kui Ying²

¹Biomedical Engineering, Tsinghua University, Beijing, China, People's Republic of, ²Key Laboratory of Particle and Radiation Imaging, Ministry of Education, Department of Engineering Physics, Tsinghua University, Beijing, China, People's Republic of, ³Department of Orthopedics, First Affiliated Hospital of PLA General Hospital, Beijing, China, People's Republic of

The proposed Kalman ﬁltered Bio Heat Transfer Model Based Self-adaptive Hybrid MR Thermometry, abbreviated as KalBHT hybrid algorithm, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman ﬁlter is utilized to update the window at each iteration time. Besides, the BHTE model is able to interpolate temperature maps during the acquisition and reconstruction of the next MR image to make real time temperature monitoring possible. To investigate the effect of the proposed model, phantom microwave heating experiment and in-vivo experiment with heating simulation were conducted in this study.

This abstract and the presentation materials are available to members only; a login is required.

Join Here