{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:18:18Z","timestamp":1786976298666,"version":"build-2736575974"},"reference-count":23,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Synthetic computed tomography (sCT)\u2010algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and compare commercial bulk\u2010density\u2010method (BM)\u2010based and AI (artificial intelligence)\u2010based\u2010algorithms using 3T magnetic resonance imaging (MRI) with patient data as part of the local clinical commissioning process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>44 prostate radiotherapy patients were subjected to MRI and treatment planning CT (TPCT) scans. From the MRI images, sCT images with two different sCT algorithms were generated. The sCT images were evaluated by visual inspection of artifacts. Both sCT methods were compared to TPCT, with Dice similarity score(DSC) of bone and body contours, DVH parameters for CTV, bladder and rectum, and gamma\u2010analysis. Accuracy for treatment alignment using sCT images was also tested. Various limits were used to define whether the differences between sCT methods to TPCT were clinically relevant (DVH parameters\u00a0&lt;2%, gamma\u2010analysis passing rates 90%, 95%, and 98%, and the DSC 0.98 for body and 0.7 for bone).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our results show that, differences in CTV\u2010dose coverage values were\u00a0&lt;2% in most of the patients with both sCT algorithms when compared to reference dose coverage. While AI\u2010sCT had mean dose coverage difference\u00a0&lt;0,5% and BM\u2010sCT\u00a0&lt;1%. Gamma\u2010analysis showed that the AI\u2010sCT mean passing rates were 95.4%, 98.6%, and 99.4% with 1mm1%, 2mm2%, and 3mm3% criteria, respectively. Similarly for BM\u2010sCT the mean passing rates were 93.4%, 98.2%, and 99.2%. For the treatment alignment accuracy, the mean difference in magnitude of the translational shifts was 1.43\u00a0mm for BM\u2010sCT and 1.57\u00a0mm for AI\u2010sCT. Even though AI\u2010sCT showed statistically better correspondence to TPCT, the differences were not clinically relevant with any of the limits. Visual evaluation showed artifacts in the AI\u2010sCT especially in the bowel area and fiducial markers were not generated with either of the sCT algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In conclusion, sCT\u2010algorithms were clinically usable on prostate treatments using 3T MR\u2010only workflow. While AI\u2010sCT showed better correspondence to TPCT than BM\u2010sCT, it generated characteristic artifacts. As sCT algorithms perform well, we still recommend testing the sCT\u2010algorithms with retrospective analyses from patient data prior to implementing sCT into the routine workflow to better understand the specific limitations and capabilities of these algorithms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.14581","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T10:39:57Z","timestamp":1732876797000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI\u2010based versus bulk density method"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3401-4198","authenticated-orcid":false,"given":"Sakari S.","family":"Karhula","sequence":"first","affiliation":[{"name":"Department of Oncology and Radiotherapy Oulu University Hospital  Oulu Finland"},{"name":"Research unit of Health Sciences and Technology Oulu University  Oulu Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piia","family":"Karppinen","sequence":"additional","affiliation":[{"name":"Department of Oncology and Radiotherapy Oulu University Hospital  Oulu Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henna","family":"Hietala","sequence":"additional","affiliation":[{"name":"Department of Oncology and Radiotherapy Oulu University Hospital  Oulu Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juha","family":"Nikkinen","sequence":"additional","affiliation":[{"name":"Department of Oncology and Radiotherapy Oulu University Hospital  Oulu Finland"},{"name":"Research unit of Health Sciences and Technology Oulu University  Oulu Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.06.2530"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.08.043"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2021.07.027"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.202107085"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010021\u201001794\u20106"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.11.027"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/aada6d"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010015\u20100549\u20107"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13236"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13051082"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.822687"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010023\u201002336\u2010y"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13641"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2023.102557"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/02841860802256426"},{"key":"e_1_2_12_17_1","unstructured":"HealthineersSiemens.MR\u2010only RT planning for the brain and pelvis with synthetic CT. 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