20 January 2015 Undersampling trajectory design for compressed sensing based dynamic contrast-enhanced magnetic resonance imaging
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Abstract
Compressed sensing has the potential to address the challenge of simultaneously requiring high temporal and spatial resolution in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), by randomly undersampling the k-space with a predesigned trajectory. However, the traditional variable density (VD) design scheme includes inherent randomness since many probability density functions (PDFs) correspond to a given acceleration factor and one fixed PDF can generate different trajectories. This randomness may translate to an uncertainty in kinetic parameter estimation. We first evaluate how the one-to-many mapping in trajectory design influences DCE parameter estimation when high reduction factors are used. Then we propose a robust design scheme by adaptively segmenting k-space into low- and high-frequency domains considering the specific characteristics for different subjects and only applying the VD scheme in the high-frequency domain. Simulation results demonstrate high accuracy and robustness compared to the VD design.
© 2015 SPIE and IS&T
Duan-Duan Liu, Dong Liang, Na Zhang, Xin Liu, Yuan-Ting Zhang, "Undersampling trajectory design for compressed sensing based dynamic contrast-enhanced magnetic resonance imaging," Journal of Electronic Imaging 24(1), 013017 (20 January 2015). https://doi.org/10.1117/1.JEI.24.1.013017 . Submission:
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