Direct image reconstruction from raw measurement data using an encoding transform refinement-and-scaling neural network
William Whiteley, Jens Gregor · 2019
Direct reconstruction of raw measurement data into a final image using a neural network is currently an uncommon approach to the use of deep learning in medical imaging. One reason may be the relatively recent adoption of deep learning. Another reason may be the computational requirements associated with performing the domain transform using fully connected perceptron layers. We propose an AUTOMAP inspired multi-segment Encoding Transform Refinement-and-Scaling (ETRS) neural network that allows reconstruction of full size 512x512 images compared to the 128x128 image size of AUTOMAP.