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Abstract #2496

Effects of Point Spread Function and Regularization Information on the MRSI with Compressed Sensing

Jung-Hsiang Chang1, Yi-Hsun Yang1, Tzu-Cheng Chao2,3, and Cheng-Wen Ko1

1Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, 2Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, Taiwan, 3MOST AI Biomedical Research Center, Tainan, Taiwan

Compressed Sensing can be very useful in accelerating Phase-encoded Proton MRSI. The sampling functions and the reconstruction settings have been known as critical factors in recovering the data of the accelerated acquisition. The present work compared the choices of sampling functions and the regularization information in the reconstruction in a hope to optimize the framework of Compressed Sensing based MRSI. The results suggest that the spectral quality can be retained for as high as five-fold acceleration with an appropriate undersampling and reconstruction setting.

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