Recently parallel imaging reconstruction based on deep learning has made lots of progresses, however, there still exist several common challenges, i.e. generalization, transferability and robustness. On the contrary, SENSE reconstruction has been routinely used in clinical scans due to its high robustness and excellent image quality. A high-quality coil sensitivity map (HQCSM) is the key to achieve good SENSE reconstruction. We proposed a hybrid SENSE reconstruction frame, combining the SENSE reconstruction algorithm with a deep convolutional neural network to learn HQCSM from a few automatic calibration lines (ACS), which shows good generalization for different under-sampling ratio and enhanced robustness.
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