Multi-Contrast Reconstruction using Neural Network for Higher Acceleration

Clinical diagnosis requires several examinations to present various characteristics of organs, which are very time-consuming. To reduce total imaging time, many techniques have been proposed. Among them, parallel imaging techniques utilize sensitivity difference between multichannel RF coils. However, it is difficult to apply these techniques to higher acceleration due to SNR degradation. In this study, it is a key concept that each image in clinical protocols has different contrast, but shares similar structure information, and they are helpful for reconstructing each other. We propose a reconstruction model based on artificial neural network to allow to use higher acceleration factors.

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