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Abstract #3232

Improving iMoCo through Group-wise Registration and Motion State Weighted Reconstruction

Zekang Ding1, Huajun She1, and Yiping Du1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

In original iMoCo algorithm, a single frame image was reconstructed by solving the iMoCo reconstruction model including the estimated motion fields and TGV sparse constraint. Since motion fields is critical in iMoCo algorithm, errors in motion estimation would deteriorate the final reconstructed image. In this study, we improved the performance of iMoCo through (1) reconstructing the full resolution dynamic images for motion estimation, (2) estimating motion fields through nonrigid group-wise registration, and (3) using a motion state weighted iMoCo reconstruction model. Residual streaking artifacts and certain image blurring were suppressed using the proposed algorithm in comparison with the original iMoCo.

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