{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T05:16:58Z","timestamp":1738559818704,"version":"3.35.0"},"reference-count":49,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82272941"],"award-info":[{"award-number":["82272941"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>PET\/CT and planning CT are commonly used medical images in radiotherapy for esophageal and nasopharyngeal cancer. However, repeated scans will expose patients to additional radiation doses and also introduce registration errors. This multimodal treatment approach is expected to be further improved.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>A new Transformer model is proposed to obtain pseudo\u2010PET\/CT fusion images for esophageal and nasopharyngeal cancer radiotherapy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The data of 129 cases of esophageal cancer and 141 cases of nasopharyngeal cancer were retrospectively selected for training, validation, and testing. PET and CT images are used as input. Based on the Transformer model with a \u201cfocus\u2010disperse\u201d attention mechanism and multi\u2010consistency loss constraints, the feature information in two images is effectively captured. This ultimately results in the synthesis of pseudo\u2010PET\/CT fusion images with enhanced tumor region imaging. During the testing phase, the accuracy of pseudo\u2010PET\/CT fusion images was verified in anatomy and dosimetry, and two prospective cases were selected for further dose verification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In terms of anatomical verification, the PET\/CT fusion image obtained using the wavelet fusion algorithm was used as the ground truth image after correction by clinicians. The evaluation metrics, including peak signal\u2010to\u2010noise ratio, structural similarity index, mean absolute error, and normalized root mean square error, between the pseudo\u2010fused images obtained based on the proposed model and ground truth, are represented by means (standard deviation). They are 37.82 (1.57), 95.23 (2.60), 29.70 (2.49), and 9.48 (0.32), respectively. These numerical values outperform those of the state\u2010of\u2010the\u2010art deep learning comparative models. In terms of dosimetry validation, based on a 3%\/2\u00a0mm gamma analysis, the average passing rates of global and tumor regions between the pseudo\u2010fused images (with a PET\/CT weight ratio of 2:8) and the planning CT images are 97.2% and 95.5%, respectively. These numerical outcomes are superior to those of pseudo\u2010PET\/CT fusion images with other weight ratios.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This pseudo\u2010PET\/CT fusion images obtained based on the proposed model hold promise as a new modality in the radiotherapy for esophageal and nasopharyngeal cancer.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17512","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T12:39:50Z","timestamp":1732192790000},"page":"1070-1085","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthesis of pseudo\u2010PET\/CT fusion images in radiotherapy based on a new transformer model"],"prefix":"10.1002","volume":"52","author":[{"given":"Hongfei","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Xijing Hospital Fourth Military Medical University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liting","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Xijing Hospital Fourth Military Medical University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Xijing Hospital Fourth Military Medical University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Xijing Hospital Fourth Military Medical University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiarui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Xijing Hospital Fourth Military Medical University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Xijing Hospital Fourth Military Medical University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14235773"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101909"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-00173-0"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acd29f"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","unstructured":"JinD GuoD HoTY et\u00a0al.Accurate esophageal gross tumor volume segmentation in PET\/CT using two\u2010stream chained 3D deep network fusion. 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