Keywords: Machine Learning/Artificial Intelligence, New Trajectories & Spatial Encoding Methods
Motivation: Simultaneous multi-slice imaging (SMS) can obtain multiple slice images simultaneously, but it requires the sensitivity distribution of the receiver coils.
Goal(s): Our goal was to separate slice images using deep learning reconstruction without coil sensitivity.
Approach: Different amplitude modulation is given to each slice, and the CNN separates each slice from the focal image based on the value of the amplitude modulation.
Results: Simulation experiments showed that image separation was successfully achieved not only for real-valued images but also for complex-valued images. Image quality decreases when the number of excitation images was increased.
Impact: SMS can be used much more easily because it does not require coil sensitivity distribution for image separation. No non-uniform residual noise will be generated. The proposed method may expand the application of SMS.
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