Direct image reconstruction from raw measurement data using an encoding transform refinement-and-scaling neural network

William Whiteley; Jens Gregor

doi:10.1117/12.2534907

28 May 2019 Direct image reconstruction from raw measurement data using an encoding transform refinement-and-scaling neural network

William Whiteley, Jens Gregor

Author Affiliations +

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107225 (2019) https://doi.org/10.1117/12.2534907
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

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.

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William Whiteley and Jens Gregor "Direct image reconstruction from raw measurement data using an encoding transform refinement-and-scaling neural network", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107225 (28 May 2019); https://doi.org/10.1117/12.2534907

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