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Article

A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction

1
Department of Political and Social Sciences, University of Bologna, 40126 Bologna, Italy
2
Department of Mathematics, University of Bologna, 40126 Bologna, Italy
3
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Tanja Tarvainen
J. Imaging 2021, 7(8), 139; https://doi.org/10.3390/jimaging7080139
Received: 11 July 2021 / Revised: 29 July 2021 / Accepted: 4 August 2021 / Published: 7 August 2021
(This article belongs to the Special Issue Inverse Problems and Imaging)
Deep Learning is developing interesting tools that are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green AI literature, we propose a shallow neural network to perform efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results show that the proposed inexpensive network computes images of comparable (or even higher) quality in about one-fourth of time and is more robust than the widely used and very deep ResUNet for tomographic reconstructions from sparse-view protocols. View Full-Text
Keywords: green AI; sparse-views tomography; learned post-processing; CNN; UNet; tomographic reconstruction green AI; sparse-views tomography; learned post-processing; CNN; UNet; tomographic reconstruction
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MDPI and ACS Style

Morotti, E.; Evangelista, D.; Loli Piccolomini, E. A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction. J. Imaging 2021, 7, 139. https://doi.org/10.3390/jimaging7080139

AMA Style

Morotti E, Evangelista D, Loli Piccolomini E. A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction. Journal of Imaging. 2021; 7(8):139. https://doi.org/10.3390/jimaging7080139

Chicago/Turabian Style

Morotti, Elena, Davide Evangelista, and Elena Loli Piccolomini. 2021. "A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction" Journal of Imaging 7, no. 8: 139. https://doi.org/10.3390/jimaging7080139

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