{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T01:49:17Z","timestamp":1777427357745,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["101057699"],"award-info":[{"award-number":["101057699"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification.<\/jats:p>","DOI":"10.3390\/jimaging10050115","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T10:31:16Z","timestamp":1715250676000},"page":"115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Image Quality Assessment Tool for Conventional and Dynamic Magnetic Resonance Imaging Acquisitions"],"prefix":"10.3390","volume":"10","author":[{"given":"Katerina","family":"Nikiforaki","sequence":"first","affiliation":[{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3909-7503","authenticated-orcid":false,"given":"Ioannis","family":"Karatzanis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5242-6060","authenticated-orcid":false,"given":"Aikaterini","family":"Dovrou","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"},{"name":"School of Medicine, University of Crete, 71003 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3608-1960","authenticated-orcid":false,"given":"Maciej","family":"Bobowicz","sequence":"additional","affiliation":[{"name":"2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4155-7203","authenticated-orcid":false,"given":"Katarzyna","family":"Gwozdziewicz","sequence":"additional","affiliation":[{"name":"2nd Department of Radiology, Medical University of Gdansk, 80-214 Gdansk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6789-5177","authenticated-orcid":false,"given":"Oliver","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Departament de Matem\u00e0tiques i Inform\u00e0tica, Universitat de Barcelona, 08007 Barcelona, Spain"},{"name":"Computer Vision Center, 08193 Bellaterra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8454-1450","authenticated-orcid":false,"given":"Manolis","family":"Tsiknakis","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"},{"name":"Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-5082","authenticated-orcid":false,"given":"Dimitrios I.","family":"Fotiadis","sequence":"additional","affiliation":[{"name":"Biomedical Research Institute, Foundation for Research and Technology\u2014Hellas (FORTH), 45500 Ioannina, Greece"}]},{"given":"Karim","family":"Lekadir","sequence":"additional","affiliation":[{"name":"Departament de Matem\u00e0tiques i Inform\u00e0tica, Universitat de Barcelona, 08007 Barcelona, Spain"},{"name":"Instituci\u00f3 Catalana de Recerca i Estudis Avan\u00e7ats (ICREA), 08010 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3783-5223","authenticated-orcid":false,"given":"Kostas","family":"Marias","sequence":"additional","affiliation":[{"name":"Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology\u2014Hellas (FORTH), 70013 Heraklion, Greece"},{"name":"Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"ref_1","unstructured":"Lekadir, K., Feragen, A., Fofanah, A.J., Frangi, A.F., Buyx, A., Emelie, A., Lara, A., Porras, A.R., Chan, A.-W., and Navarro, A. 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