Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Green Prospective for Learned Post-processing in Sparse-view Tomographic Reconstruction

Version 1 : Received: 11 July 2021 / Approved: 12 July 2021 / Online: 12 July 2021 (13:49:15 CEST)

A peer-reviewed article of this Preprint also exists.

Morotti, E.; Evangelista, D.; Loli Piccolomini, E. A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction. J. Imaging 2021, 7, 139. Morotti, E.; Evangelista, D.; Loli Piccolomini, E. A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction. J. Imaging 2021, 7, 139.

Journal reference: J. Imaging 2021, 7, 139
DOI: 10.3390/jimaging7080139

Abstract

Deep Learning is developing interesting tools which 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 here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.

Keywords

Green AI; Sparse-views tomography; Learned Post Processing; UNet; Tomographic reconstruction

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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