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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