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However, it faces limitations due to the lag effect and concerns about radiation dose. While sparse\u2010view CBCT can alleviate these problems by reducing the detector readout frequency and the exposure time, it introduces streak artifacts that can degrade image quality. Recent research has explored techniques for sparse\u2010view CBCT reconstruction. Nevertheless, it remains an ongoing challenge to achieve improved image\u00a0quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The purpose of this study is to eliminate the streak artifacts and recover image details with dual\u2010domain deep learning networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study proposed a Multi\u2010dimensional Attention\u2010Enhanced reconstruction (MAE\u2010Recon) algorithm for sparse\u2010view CBCT. MAE\u2010Recon consists of four key components: a linear interpolation module, a projection domain network, an FDK operator, and an image domain network. Firstly, the linear interpolation module generates an initial full\u2010view projection. Subsequently, the projection domain network predicts high\u2010quality projection data. The FDK operator then reconstructs CBCT images. Finally, the image domain network refines the reconstructed images. Considering the long\u2010range dependency among projections and the information redundancy within features, this study introduces two plug\u2010and\u2010play modules for\u00a0enhancement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A real chest dataset and a simulated abdomen dataset were chosen for validation. The RMSE was reduced by   and  , respectively, and the SSIM and PSNR scores were improved by 0.066, 0.009, and 4.62 dB, 1.44 dB on the two datasets. An ablation study was also performed. The improved RMSE, SSIM, and PSNR scores illustrate that the modules are practical and efficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The MAE\u2010Recon proposed in this study obtains high\u2010quality images in sparse\u2010view CBCT reconstruction, demonstrating the application\u00a0potential.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70299","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T10:13:47Z","timestamp":1769508827000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010dimensional attention\u2010enhanced reconstruction for sparse\u2010view CBCT"],"prefix":"10.1002","volume":"53","author":[{"given":"Yuhao","family":"Liu","sequence":"first","affiliation":[{"name":"Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine Beijing Institute of Respiratory Medicine and Beijing Chao Yang Hospital Capital Medical University Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyuan","family":"Tao","sequence":"additional","affiliation":[{"name":"Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianling","family":"Lyu","sequence":"additional","affiliation":[{"name":"Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Xi","sequence":"additional","affiliation":[{"name":"Shanghai First\u2010Imaging Information Technology Co., Ltd. Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yikun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine Beijing Institute of Respiratory Medicine and Beijing Chao Yang Hospital Capital Medical University Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengcheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Dynamic Testing Technology North University of China Taiyuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics Beijing Institute of Technology Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"TomographyC.physical principles clinical applications and quality control.2009."},{"issue":"1","key":"e_1_2_8_3_1","first-page":"157","article-title":"Review of the application of computed tomography technology in safety inspection domain","volume":"21","author":"Jin\u2010zhou Z","year":"2012","journal-title":"CT Theory Appl"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0883-2889(90)90159-E"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1179\/146531205225021285"},{"issue":"5","key":"e_1_2_8_6_1","first-page":"390","article-title":"Applications of CBCT in dental practice: a review of the literature","volume":"60","author":"Alamri HM","year":"2012","journal-title":"General dentistry"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2019.02.014"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvir.2008.02.018"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr\/55733882"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1120\/jacmp.v8i3.2483"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.1.000612"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/0022-5193(70)90109-8"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1029\/JB095iB08p12553"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.3233\/XST-2006-00155"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2836423"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108010"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107168"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107167"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15930"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13687"},{"key":"e_1_2_8_21_1","first-page":"692","volume-title":"Medical Imaging 2019: Physics of Medical Imaging","author":"Dong X","year":"2019"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2018.2867611"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823768"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823338"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2020.3011413"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.12.096"},{"key":"e_1_2_8_28_1","doi-asserted-by":"crossref","unstructured":"WangY ChaoL ShanW ZhangH WangZ LiQ.Improving the quality of sparse\u2010view cone\u2010beam computed tomography via reconstruction\u2010friendly interpolation network. 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