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Abstract #2247

On Non-Cartesian Reconstruction by Prior-Data-Driven K-T PCA

Mei-Lan Chu1, Ping-Huei Tsai2, 3, Hsiao-Wen Chung1, Hsu-Hsia Peng4, Cheng-Wen Ko5

1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; 2Imaging Research Center, Taipei Medical University, Taipei, Taiwan; 3Department of Radiology, WanFang Hospital, Taipei, Taiwan; 4Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan; 5Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan

Previous research of reconstructing dynamic MR imaging with arbitrary sampling trajectory was mainly rely on the traning data whcih extracted from central k-space. However, the training data is proned to streaking artifact from substantially under-sampled data. We propose a prior-data-driven method to address this problem. The method mainly rely on x-f principal components extracted from existing images with similar anatomical position, which make the prior information free from streaking artifact. We demonstrate the feasibility of the proposed method wtih simulation of radial imaging with simulation of radial imaging, and the results show that the method robustly reconstuct under-sampled dynamic images of arbitrary trajectory.