Simultaneously Multi-slice Imaging by the Fusion of Reconstruction and Collecting Under-sampled Signal with Deep Learning (FoCUS)

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.