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Abstract #2275

Interpolated Compressed Sensing MR Image Reconstruction Using Neighboring Slice K-Space Data

Yong Pang1, Xiaoliang Zhang1, 2

1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States; 2UCSF/UC Berkeley Joint Graduate Group in Bioengineering, San Francisco & Berkeley, CA, United States

Sparse MRI has been introduced to reduce the acquisition time and raw data size by significantly undersampling the k-space. We propose an interpolation method to improve the signal to noise ratio or imaging speed for multi-slice two-dimensional MRI. The raw data of each slice is multiplied by a weighting function and then used to estimate the missed k-space data of the neighboring slice, which helps improve the SNR of the neighboring slice. In-vivo MR of human feet has been used to investigate the feasibility of the proposed method, showing obvious improved SNR of the neighboring slice.