{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:25:34Z","timestamp":1778286334244,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT). The proposed algorithm generates a volumetric map as an output, informing clinicians of the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the complexity of the MAE prediction task. The workflow was trained and tested on two cohorts of head and neck cancer patients with different imaging modalities: 27 MR scans and 33 CBCT. The algorithm evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4 HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do not affect the clinical decisions made on the basis of the proposed estimation. The workflow exhibited no systematic error in MAE prediction. This work represents a proof of concept about the feasibility of synthetic CT evaluation in daily clinical practice, and it paves the way for future patient-specific quality assessment strategies.<\/jats:p>","DOI":"10.3390\/jimaging10120316","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T04:15:03Z","timestamp":1733804103000},"page":"316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0219-0157","authenticated-orcid":false,"given":"Paolo","family":"Zaffino","sequence":"first","affiliation":[{"name":"Department of Experimental and Clinical Medicine, Magna Graecia University, viale Europa, 88100 Catanzaro, Italy"},{"name":"Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2443-4029","authenticated-orcid":false,"given":"Ciro Benito","family":"Raggio","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-5030","authenticated-orcid":false,"given":"Adrian","family":"Thummerer","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands"},{"name":"Department of Radiation Oncology, University Hospital, LMU Munich, 81377 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-7001","authenticated-orcid":false,"given":"Gabriel Guterres","family":"Marmitt","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1083-372X","authenticated-orcid":false,"given":"Johannes Albertus","family":"Langendijk","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3639-8714","authenticated-orcid":false,"given":"Anna","family":"Procopio","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine, Magna Graecia University, viale Europa, 88100 Catanzaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5768-1829","authenticated-orcid":false,"given":"Carlo","family":"Cosentino","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine, Magna Graecia University, viale Europa, 88100 Catanzaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9458-2202","authenticated-orcid":false,"given":"Joao","family":"Seco","sequence":"additional","affiliation":[{"name":"Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsfoschungszentrum (DKFZ), 69120 Heidelberg, Germany"},{"name":"Department of Physics and Astronomy, Heidelberg University, 69120 Heidelberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8212-571X","authenticated-orcid":false,"given":"Antje Christin","family":"Knopf","sequence":"additional","affiliation":[{"name":"Institute for Medical Engineering and Medical Informatics, School of Life Science FHNW, 4132 Muttenz, Switzerland"},{"name":"Department of Radiotherapy and Radiation Oncology, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universit\u00e4t Dresden, 01307 Dresden, Germany"}]},{"given":"Stefan","family":"Both","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5339-9583","authenticated-orcid":false,"given":"Maria Francesca","family":"Spadea","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.1002\/mp.13187","article-title":"Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region","volume":"45","author":"Arabi","year":"2018","journal-title":"Med. 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