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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