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Abstract #0608

Ultra-Fast Dynamic MRI for Lung Tumor Tracking Based on Compressed Sensing

Manoj K. Sarma1, M. Albert Thomas1, Peng Hu2, Daniel B. Ennis2, Ke K. Sheng3

1Radiological Sciences, UCLA School of Medicine, Los Angeles, CA, United States; 2Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; 3Radiation Oncology, UCLA School of Medicine, Los Angeles, CA, United States

Radiotherapy guided by MRI has afforded the hardware potential to treat a moving tumor more accurately but existing imaging speed is inadequate for 3D real-time lung and lung tumor imaging. By exploiting the intrinsic coherence of the patient anatomy during time, we adapted a k-t SLR compressed sensing method to dramatically reduce the amount of data that is needed to update a new dynamic imaging without losing details. We were able to accurately track moving tumors of nine patients based on images reconstructed with very high data under-sampling ratio up to 5% of the original data.