{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T21:06:33Z","timestamp":1765487193880,"version":"3.41.2"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T00:00:00Z","timestamp":1740873600000},"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":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Reducing radiation dose attributed to computed tomography (CT) may compromise the accuracy of organ segmentation, an important step in <jats:sup>177<\/jats:sup>Lu DOTATATE therapy that affects both activity and mass estimates. This study aimed to facilitate CT dose reduction using deep learning methods for patients undergoing serial single photon emission computed tomography (SPECT)\/CT imaging during <jats:sup>177<\/jats:sup>Lu DOTATATE therapy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The <jats:sup>177<\/jats:sup>Lu DOTATATE patient dataset hosted in Deep Blue Data was used in this study. The noise insertion method incorporating the effect of bowtie filter, automatic exposure control, and electronic noise was applied to simulate images at four reduced dose levels. Organ segmentation was carried out using the TotalSegmentator model, while image denoising was performed with the DenseNet model. The impact of segmentation performance on the dosimetry accuracy of <jats:sup>177<\/jats:sup>Lu DOTATATE therapy was quantified by calculating the percent difference between a dose rate map segmented with a reference mask and the same dose rate map segmented with a test mask (PD<jats:sub>dose<\/jats:sub>) for spleen, right kidney, left kidney, and liver.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Before denoising, the mean\u00a0\u00b1\u00a0standard deviation of PD<jats:sub>dose<\/jats:sub> for all critical organs were 2.31\u00a0\u00b1\u00a02.94%, 4.86\u00a0\u00b1\u00a09.42%, 8.39\u00a0\u00b1\u00a014.76%, 12.95\u00a0\u00b1\u00a019.99% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively. After denoising, the corresponding results were 1.69\u00a0\u00b1\u00a02.25%, 2.84\u00a0\u00b1\u00a04.46%, 3.72\u00a0\u00b1\u00a04.22%, 7.98\u00a0\u00b1\u00a015.05% in CT images at dose levels down to 20%, 10%, 5%, 2.5% of the normal dose, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>As dose reduction increased, CT image segmentation gradually deteriorated, which in turn deteriorated the dosimetry accuracy of <jats:sup>177<\/jats:sup>Lu DOTATATE therapy. Improving CT image quality through denoising could enhance <jats:sup>177<\/jats:sup>Lu DOTATATE dosimetry, making it a valuable tool to support CT dose reduction for patients undergoing serial SPECT\/CT imaging during treatment.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.70066","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T05:38:21Z","timestamp":1741757901000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating auto\u2010contouring accuracy in reduced CT dose images for radiopharmaceutical therapies: Denoising and evaluation of <sup>177<\/sup>Lu DOTATATE therapy dataset"],"prefix":"10.1002","volume":"26","author":[{"given":"Hung\u2010Te","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Kaohsiung Municipal Siaogang Hospital  Kaohsiung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan\u2010Yin","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine National Taiwan University Cancer Center  Taipei Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching\u2010Ching","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Department of Medical Research Kaohsiung Medical University Hospital  Kaohsiung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-020-0073-9"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2020200021"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2022.02.002"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2018.11.001"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13550-023-00997-0"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.110.085100"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13550-018-0427-z"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-010-1422-4"},{"key":"e_1_2_9_10_1","first-page":"102","article-title":"Overcoming nephrotoxicity in peptide receptor radionuclide therapy using [(177)Lu]Lu\u2010DOTA\u2010TATE for the treatment of neuroendocrine tumours","author":"Geenen L","year":"2021","journal-title":"Nucl Med Biol"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40658-024-00658-8"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-022-06092-1"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnume.2024.1355912"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/MNM.0b013e328355b6d5"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-022-10310-2"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2007.08.062"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.12.9768"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4938588"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.14245\/ns.1938396.198"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100723"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.zemedi.2018.11.002"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2020.02.44"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108112"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00405-x"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15397"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/45\/8\/315"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1097\/RCT.0b013e318258e891"},{"key":"e_1_2_9_30_1","doi-asserted-by":"crossref","unstructured":"TongT LiG LiuX GaoQ.Image super\u2010resolution using dense skip connections.2017 IEEE International Conference on Computer Vision (ICCV) Venice Italy 2017 pp.4809\u20104817. doi:10.1109\/ICCV.2017.514","DOI":"10.1109\/ICCV.2017.514"},{"key":"e_1_2_9_31_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS SunJ.Delving deep into rectifiers: surpassing human\u2010level performance on ImageNet classification.IEEE International Conference on Computer Vision (ICCV2015).IEEE.2015;1502.","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_9_32_1","unstructured":"KingmaD BaJ.Adam: a method for stochastic optimization.3rd International Conference on Learning Representations ICLR 2015 San Diego CA USA May 7\u20139 2015. doi:10.48550\/arXiv.1412.6980"},{"key":"e_1_2_9_33_1","doi-asserted-by":"crossref","unstructured":"JiaY ShelhamerE DonahueJ et\u00a0al.Caffe: Convolutional Architecture for Fast Feature Embedding. InMM '14: Proceedings of the 22nd ACM international conference on Multimedia.ACM;2014.","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12710"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2022.e09830"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.121.262748"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.122.265094"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.123.267293"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.11.011"},{"key":"e_1_2_9_40_1","article-title":"Deep learning for autosegmentation for radiotherapy treatment planning: state\u2010of\u2010the\u2010art and novel perspectives","author":"Erdur AC","year":"2024","journal-title":"Strahlenther Onkol"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-023-00804-1"},{"key":"e_1_2_9_44_1","unstructured":"TsandaA NickischH WisselT KlinderT KnoppT GrassM On TotalSegmentator's performance on low\u2010dose CT images. InProceedings Volume 12926 Medical Imaging 2024: Image Processing.129260B.SPIE2024."},{"key":"e_1_2_9_45_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2384830"},{"key":"e_1_2_9_46_1","unstructured":"MilanfarP DelbracioM Denoising: A Powerful Building\u2010Block for Imaging Inverse Problems and Machine Learning.2024. doi:10.48550\/arXiv.2409.06219"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.semnuclmed.2020.05.004"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-009-1072-6"},{"key":"e_1_2_9_49_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40658-016-0153-4"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70066","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T12:36:19Z","timestamp":1743856579000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.70066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,2]]},"references-count":48,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["10.1002\/acm2.70066"],"URL":"https:\/\/doi.org\/10.1002\/acm2.70066","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"type":"print","value":"1526-9914"},{"type":"electronic","value":"1526-9914"}],"subject":[],"published":{"date-parts":[[2025,3,2]]},"assertion":[{"value":"2024-10-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70066"}}