Accelerated first-pass perfusion MRI using BLOSM: Evaluation using dynamic simulations and patient datasets with prominent respiratory motion
Xiao Chen 1 , Yang Yang 1 , Michael Salerno 2,3 , and Frederick H. Epstein 1
Biomedical Engineering, University of
Virginia, Charlottesville, VA, United States,
University of Virginia, Charlottesville, VA, United
Cardiology, University of Virginia,
Charlottesville, VA, United States
We recently developed a motion-compensated compressed
sensing (CS) method to accelerate dynamic MRI of the
heart that exploits matrix low-rank sparsity within
motion-tracked regions of temporal image sequences
(Block LOw-rank Sparsity with Motion guidance, or BLOSM).
Initial results showed that BLOSM appears promising for
accelerating first-pass myocardial perfusion imaging,
even when substantial respiratory motion occurs.
Presently, we implemented improved motion tracking for
BLOSM and compared the improved BLOSM method to other CS
methods using computer-simulated motions and using
first-pass perfusion datasets from patients with
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