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Proceedings Paper

Direct image reconstruction from raw measurement data using an encoding transform refinement-and-scaling neural network
Author(s): William Whiteley; Jens Gregor
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Paper 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.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107225 (28 May 2019); doi: 10.1117/12.2534907
Show Author Affiliations
William Whiteley, The Univ. of Tennessee Knoxville (United States)
Siemens Molecular Imaging (United States)
Jens Gregor, The Univ. of Tennessee Knoxville (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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