Makarand Parigi1, Abhinav Saksena1, Nicole Seiberlich2, and Yun Jiang2
1Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States, 2Department of Radiology, University of Michigan, Ann Arbor, MI, United States
1Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States, 2Department of Radiology, University of Michigan, Ann Arbor, MI, United States
Applying a neural network before and after the coil combination step of MRI reconstruction produces higher quality images with lower memory usage.
Figure 1: Illustration of the Multinet architecture, featuring the Multi-image U-net and a traditional U-net. The Multi-image U-net takes in as many images as there are coils, and outputs that many images.
Figure 2: Illustration of the Multi-image U-net, the first component of the Multinet. The number in the boxes indicates the number of channels, while the width and height of the data is displayed to the side. x is the number of channels in the output of the first convolution, i.e. Multinet-16 would have x=16. n is the number of input and output channels of the entire model. For the Multinet, this is the number of coils.