{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:28:42Z","timestamp":1772252922979,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017142","name":"Gruppo Nazionale per il Calcolo Scientifico","doi-asserted-by":"publisher","award":["Ottimizzazione per l'apprendimento automatico e apprendimento automatico per l'ottimizzazione"],"award-info":[{"award-number":["Ottimizzazione per l'apprendimento automatico e apprendimento automatico per l'ottimizzazione"]}],"id":[{"id":"10.13039\/100017142","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jimaging7080139","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:49:41Z","timestamp":1628459381000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4283-2994","authenticated-orcid":false,"given":"Elena","family":"Morotti","sequence":"first","affiliation":[{"name":"Department of Political and Social Sciences, University of Bologna, 40126 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6261-7717","authenticated-orcid":false,"given":"Davide","family":"Evangelista","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Bologna, 40126 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9951-3564","authenticated-orcid":false,"given":"Elena","family":"Loli Piccolomini","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S0962492919000059","article-title":"Solving inverse problems using data-driven models","volume":"28","author":"Arridge","year":"2019","journal-title":"Acta Numer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/MSP.2017.2739299","article-title":"Convolutional neural networks for inverse problems in imaging: A review","volume":"34","author":"McCann","year":"2017","journal-title":"IEEE Signal Process. 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