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Abstract #4375

Accelerated lung MRI using Low-Rank Decomposition: a prospective and simulation study

Manoj Kumar Sarma 1 , Stan Rapacchi 1 , Peng Hu 1 , Daniel B. Ennis 1 , M. Albert Thomas 1 , Percy Lee 2 , Patrick Kupelian 2 , and Ke Sheng 2

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

Respiratory motion has posed significant challenges in lung cancer radiotherapy. For patients presented with lung cancer, dynamic 2D lung MRI is a safe and robust method to characterize internal organ motion. Since the MR speed depends on the number of data points sampled in a given time, under-sampling of the k-space is a practical approach to shorten imaging time. Recently, various compressed sensing techniques have been utilized to accelerate imaging acquisition. In the study, the combination of transform domain sparsity with rank deficiency is used to reconstruct spatial-temporal lung dynamic MRI data and its ability to track lung tumor motion is examined.

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