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Abstract #1410

Study on regularization paremeter tuning in compressed sensing using no-reference image quality assessment

Kihun Bang1, Jinseong Jang1, and Dosik Hwang1

1School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of

In Magnetic Resonance Imaging system, acquiring fewer measurements is required to reduce scan time, but it leads the aliasing artifact. Compressed Sensing is exploited to reconstruct image from undersampled data without artifacts by solving the optimization problem. However, It has some difficulites in selecting regularization parameters and this abstract propose the way to select regularization parameters by evaluating image quality. The quality of reconstructed image from proposed method is much better than the image from manual parameters. This study also has potential to be helpful in fast MR imaging.

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