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

Morotti, Elena; Evangelista, Davide; Loli Piccolomini, Elena

doi:10.3390/jimaging7080139

Open AccessArticle

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

by

Elena Morotti

^1,†

,

Davide Evangelista

^2,†

and

Elena Loli Piccolomini

^3,*,†

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.

J. Imaging 2021, 7(8), 139; https://doi.org/10.3390/jimaging7080139

Submission 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)

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Abstract

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.

Keywords: 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

APA Style

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

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

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