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Abstract #3817

Suppressing Mulit-Channel Diffusion Tensor Imaging Noise Using the Data Consistency Constraint

Ying-Hua Chu1, Shang-Yueh Tsai2, Yi-Cheng Hsu3, Wen-Jui Kuo4, Fa-Hsuan Lin1, 5

1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; 2Graduate Institute of Applied Physics, National Cheng-Chi University, Taipei, Taiwan; 3Department of Mathematics, Nnational Taiwan University, Taipei, Taiwan; 4Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan; 5Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland

We exploit the redundancy among channels of a receiver coil array to improve the SNR of DTI. Our method uses a universal kernel to enforce the data-consistency (DC) among k-space data across receiver coils. This DC constraint was then applied to all diffusion-weighted images to suppress noise disturbing the data consistency required by the parallel MRI theory. Experimental results at 3T with b = 4,000 s/mm2 demonstrate that the SNR can be improved by approximately 40% by applying this constraint to DTI reconstructions.