{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:59:10Z","timestamp":1776884350937,"version":"3.51.2"},"reference-count":31,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"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":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and objective<\/jats:title><jats:p>Cone\u2010beam computed tomography (CBCT) has become a more and more active cutting\u2010edge topic in the international computed tomography (CT) research due to its advantages of fast scanning speed, high ray utilization rate and high precision. However, scatter artifacts affect the imaging performance of CBCT, which hinders its application seriously. Therefore, our study aimed to propose a novel algorithm for scatter artifacts suppression in thorax CBCT based on a feature fusion residual network (FFRN), where the contextual loss was introduced to adapt the unpaired datasets better.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In the method we proposed, a FFRN with contextual loss was used to reduce CBCT artifacts in the region of chest. Unlike L1 or L2 loss, the contextual loss function makes input images which are not aligned strictly in space available, so we performed it on our unpaired datasets. The algorithm aims to reduce artifacts via studying the mapping between CBCT and CT images, where the CBCT images were set as the beginning while planning CT images as the end.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method effectively removes artifacts in thorax CBCT, including shadow artifacts and cup artifacts which can be collectively referred to as uneven grayscale artifacts, in the CBCT image, and perform well in preserving details and maintaining the original shape. In addition, the average PSNR number of our proposed method achieved 27.7, which was higher than the methods this paper referred which indicated the significance of our method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>What is revealed by the results is that our method provides a highly effective, rapid, and robust solution for the removal of scatter artifacts in thorax CBCT images. Moreover, as is shown in Table 1, our method has demonstrated better artifact reduction capability than other methods.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13968","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T12:05:47Z","timestamp":1680264347000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artifact removal for unpaired thorax CBCT images using a feature fusion residual network and contextual loss"],"prefix":"10.1002","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6071-7861","authenticated-orcid":false,"given":"Wenqin","family":"Zhuang","sequence":"first","affiliation":[{"name":"College of Communications and Information Engineering Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Communications and Information Engineering Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haochen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Communications and Information Engineering Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Ying","sequence":"additional","affiliation":[{"name":"College of Communications and Information Engineering Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mo","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Communications and Information Engineering Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13963"},{"issue":"3","key":"e_1_2_9_3_1","first-page":"941","article-title":"Scattering estimation for cone\u2010beam CT using local measurement based on compressed sensing","volume":"65","author":"Yang F","year":"2018","journal-title":"IEEE Trans TNS"},{"issue":"9","key":"e_1_2_9_4_1","first-page":"515","article-title":"Scatter correction method for cone\u2010beam CT based on interlacing\u2010slit scan","volume":"23","author":"Huang KD","year":"2014","journal-title":"Chin Phys"},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","unstructured":"ZhangH ShiY HuangK An improved scatter correction method for cone beam computed tomography.IEEE ICSPCC.2011;1\u20105.","DOI":"10.1109\/ICSPCC.2011.6061576"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.595920"},{"issue":"1","key":"e_1_2_9_7_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.ejmp.2017.09.103","article-title":"A fast cone\u2010beam CT scatter correction method with Monte Carlo simulation using GPUMCD","volume":"42","author":"Saucier MA","year":"2017","journal-title":"Med Phys"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2017.09.002"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.4236\/ijmpcero.2017.64032"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"ZhaoC OuyangL WangJ JinM Multi\u2010view scatter estimation for moving blocker scatter correction of CBCT. inNuclear Science Symposium Medical Imaging Conference and Room\u2010Temperature Semiconductor Detector Workshop.IEEE.2017.","DOI":"10.1109\/NSSMIC.2016.8069477"},{"issue":"12","key":"e_1_2_9_11_1","first-page":"1573","article-title":"Scatter correction method for X\u2010ray CT using primary modulation: theory and preliminary results","volume":"25","author":"Zhu L","year":"2006","journal-title":"IEEE Trans TMI"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4903261"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3231824"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598914"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3152114"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12385"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4926851"},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"IsolaP ZhuJ ZhouT EfrosAA Image\u2010to\u2010image translation with conditional adversarial networks. in2017 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017.Honolulu HI USA. 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