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Abstract #1554

Application of Low-Rank Matrix-Completion Reconstruction Combined with Segmentation and Parallel Imaging in Lower Extremities Perfusion Imaging

Jieying Luo 1 , Taehoon Shin 2 , Tao Zhang 1 , Joseph Y. Cheng 1 , Bob S. Hu 3 , and Dwight G. Nishimura 1

1 Electrical Engineering, Stanford University, Stanford, California, United States, 2 University of Maryland, Baltimore, Maryland, United States, 3 Palo Alto Medical Foundation, Palo Alto, California, United States

Perfusion imaging in the lower extremities remains challenging due to the requirements of large volumetric coverage and high temporal resolution. A low-rank matrix-completion reconstruction method has been proposed for highly accelerated dynamic contrast-enhanced perfusion imaging. In this work, an improved reconstruction method that combines low-rank matrix-completion reconstruction with image-based segmentation and parallel imaging is developed and tested in vivo. The proposed method can recover perfusion dynamics with less temporal blurring, and is promising for quantitative perfusion imaging in the lower extremities.

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