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Abstract #1915

Coil Sensitivity Estimation and Complex Image Combination for 96-Channel Receive Array at 7T

Hannah Kempfert1, Jingjia Chen2,3, and Chunlei Liu1,4
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 4Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States

Synopsis

Keywords: Image Reconstruction, Data Processing

Motivation: High density receive arrays can improve SNR and parallel imaging capability; however, they also introduce significant image reconstruction challenges.

Goal(s): We aim to find a reconstruction method that will produce consistent and high-quality complex images for high-channel-count receive arrays at 7T.

Approach: Several existing sensitivity map estimation methods and coil combination methods were tested for 8-channel and 32-channel datasets, and an ultrahigh resolution 96-channel dataset acquired at 7T.

Results: Existing reconstruction methods did not produce consistent results for the 96-channel dataset. Compression of high-quality sensitivity maps reduced data size by a factor of 100 while maintaining image quality.

Impact: This work explores the unique reconstruction challenges in high-channel-count receive arrays by assessing performance of existing reconstruction techniques on an ultrahigh resolution dataset acquired with a 96-channel receive array, establishing a need for more research in effective reconstruction methods.

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Keywords