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Abstract #4783

A Deep Learning Algorithm for Non-Cartesian Coil Sensitivity Map Estimation

Zihao Chen1,2, Yuhua Chen1,3, Debiao Li1,3, and Anthony G. Christodoulou1

1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Engineering Physics, Tsinghua University, Beijing, China, 3Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States

The use of parallel imaging (PI) to exploit the encoding power of multiple coil sensitivity patterns is essential for any modern method for accelerating MRI. In practice, the need to estimate sensitivity maps when using an image-space PI formulation delays the image reconstruction process, particularly for non-Cartesian acquisitions. This paper presents a deep learning method to estimate sensitivity maps from non-Cartesian dynamic imaging data. Results show that this algorithm provide a significant reduction in the time (from 42s to 2.5s for 12 coils) for generating high-quality coil sensitivity maps from non-Cartesian MR data compared to the conventional algorithms.

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