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Abstract #3510

Accelerated reconstruction for calibrationless parallel imaging using grouped joint nonlinear inversion and its application in myelin water imaging

Zhe Wu1, Hongjian He1, Yi Sun2, and Jianhui Zhong1,3

1Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, 2MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China, 3Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

The simultaneous estimation of images and coil sensitivities using joint nonlinear inversion (JNLINV) has been shown to be effective for calibrationless parallel imaging for multi-echo data. However, the number of unknowns grows with increasing number of echoes, so the reconstruction procedure could be lengthy. This study proposes an improved method called grouped JNLINV (gJNLINV) to enhance the reconstruction efficiency. Its reconstruction time is ~1/3 of that with JNLINV while preserving a similar root-mean-square error (RMSE) and increasing the fidelity of the coil sensitivities. We further demonstrate the application of gJNLINV on a 32-echo GRE data set for myelin water imaging.

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