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Abstract #1980

Compressed sensing MRI via a fusion model based on image and gradient priors

Yuxiang Dai1, Cheng yan Wang2, and He Wang1
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Human Phenome Institute, Fudan University, Shanghai, China

Compressed sensing (CS) has been employed to accelerate magnetic resonance imaging (MRI) by sampling fewer measurements. We proposed a fusion model based on the optimization method to integrate the image and gradient-based priors into CS-MRI for better reconstruction results via convolutional neural network models. In addition, the proposed fusion model exhibited effective reconstruction performance in magnetic resonance angiography (MRA).

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