2014 Poster Sessions : Image Reconstruction in Lower Extremities Perfusion Imaging: Combined Low-Rank Matrix-Completion and Image Segmentation

Student Name : Jieying Luo
Advisor : Dwight Nishimura
Research Areas: Information Systems
Abstract:
Perfusion imaging in the lower extremities remains challenging due to the requirements of large volumetric coverage and high temporal resolution. In this work, a 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 achieve highly accelerated dynamic contrast-enhanced perfusion imaging and recover perfusion dynamics with less temporal blurring. This method is promising for quantitative perfusion imaging in the lower extremities.

Bio:
Jieying Luo is a graduate student at Stanford University. She received M. Sc. degree in Electrical Engineering from Stanford University in 2011, and B.Sc. degree in Department of Physics from Peking University, China, in 2009. Her research interests include image reconstruction, pulse sequence design and motion correction in magnetic resonance imaging.