Abstract #5540

Real-Time Motion Prediction for Feedback Control of MRI-Guided Interventions

Xinzhou Li^1,2, Samantha Mikaiel^1,3, James Simonelli⁴, Yu-Hsiu Lee⁴, Tsu-Chin Tsao⁴, and Holden H. Wu^1,2,3

¹Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Physics and Biology in Medicine, University of California, Los Angeles, Los Angeles, CA, United States, ⁴Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, United States

MRI is capable of providing flexible soft tissue contrast and real-time guidance of interventions. Real-time information about the motion of tissues and devices is essential to provide feedback for physician and robotic control of MRI-guided interventions. In this work, a new motion prediction algorithm using MRI-based motion tracking and multi-rate Kalman filtering is proposed to provide accurate and real-time motion information. Experiments and simulations show that Kalman filtering with expectation maximization training and multi-rate data fusion is able to achieve low motion prediction error. This new algorithm has potential in providing real-time feedback information for MRI-guided interventions.

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