{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:02:20Z","timestamp":1790121740500,"version":"4.0.1"},"reference-count":34,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Jiangsu Natural Science Foundation","award":["SJ214027"],"award-info":[{"award-number":["SJ214027"]}]},{"name":"Natural Science Research Program of Jiangsu Higher Education Institutions","award":["TJ212031"],"award-info":[{"award-number":["TJ212031"]}]},{"DOI":"10.13039\/501100011512","name":"Jiangsu Provincial Key Laboratory of Advanced Catalytic Materials and Technology Department, Jiangsu Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["JS-2013-199"],"award-info":[{"award-number":["JS-2013-199"]}],"id":[{"id":"10.13039\/501100011512","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012470","name":"Doctoral Program Foundation of Institutions of Higher Education of China","doi-asserted-by":"publisher","award":["BJ213019"],"award-info":[{"award-number":["BJ213019"]}],"id":[{"id":"10.13039\/501100012470","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of X-Ray Science and Technology"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Digital Breast Tomosynthesis (DBT) is an emerging imaging technique for diagnosing breast disease. It reconstructs three-dimensional (3D) tomographic images from a limited number of projections within a restricted angular range. This technique effectively reduces tissue overlap and improves lesion detection rates. However, conventional reconstruction relies on a paraxial approximation, neglecting the increasing vertical geometric distortion at the detector periphery. This, distortion, blurring the microcalcifications and subtle lesions, is to be decreased by a proposed reconstruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Approach<\/jats:title>\n                    <jats:p>The proposed reconstruction uses 3D coordinate transformation to overcome the geometric distortion and so it follows a sequential five-step pipeline: preprocessing, coordinate transformation, interpolation, weighting, and reconstructing. First, multi-angle projections undergo the preprocessing of background segmentation and filtering. Second, a 3D coordinate transformation is applied to each projection based on its acquisition angle and depth. Then, linear interpolation is used to fill data gaps caused by the spatial shift. Next, an angular weighting scheme balances the sampling density from different projections. Finally, tomographic images are reconstructed from these processed projections.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results show that the 3D coordinate transformation improves geometric accuracy and reduces distortion. Specifically, for FBP reconstruction, the proposed method increased Peak Signal-to-Noise Ratio (PSNR) by 1.19\u2005dB and reduced Standard Deviation (SD) by 403.53. For MLEM reconstruction, the Contrast-to-Noise Ratio (CNR) is improved by approximately 15.4%. Clinical data further confirm that these quantitative improvements help in differentiating lesions from the surrounding background.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed DBT reconstruction based on 3D coordinate transformation improves image quality, showing potential for broader clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/08953996261469039","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:23:16Z","timestamp":1785331396000},"page":"682-695","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["DBT reconstruction based on 3D coordinate transformation of multi-angle projections"],"prefix":"10.1177","volume":"34","author":[{"given":"Ping","family":"Chen","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, College of Electronic and Optical Engineering &amp; College of Flexible Electronics (Future Technology), Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2990-2289","authenticated-orcid":false,"given":"Chunyu","family":"Yu","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, College of Electronic and Optical Engineering &amp; College of Flexible Electronics (Future Technology), Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"970","article-title":"Analysis of disease burden of breast cancer in Chinese women","volume":"37","author":"Wang L","year":"2016","unstructured":"Wang L, Zhang Y, Shi J, et al. Analysis of disease burden of breast cancer in Chinese women. Chinese Journal of Epidemiology 2016; 37: 970\u2013976.","journal-title":"Chinese Journal of Epidemiology"},{"key":"e_1_3_3_3_2","first-page":"11","article-title":"To explore the new features of digital breast 3D tomography","volume":"6","author":"Fang W","year":"2015","unstructured":"Fang W, Song J, Zhou C. To explore the new features of digital breast 3D tomography. Chinese Journal for Clinicians 2015; 6: 11\u201314.","journal-title":"Chinese Journal for Clinicians"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.3431996"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2732824"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/0284185117748487"},{"key":"e_1_3_3_7_2","unstructured":"Li H. Digital breast tomosynthesis based on WASOBI and digital breast enhancement algorithm based on morphology feature extraction. Nanjing University of Posts and Telecommunications.Master\u2019s Thesis Nanjing University of Posts and Telecommunications CN 2021."},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","unstructured":"Barufaldi B Vent TL Acciavatti RJ et al. Determining the optimal angular range of the X-ray source motion in tomosynthesis using virtual clinical trials. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 2020; 11312 113120I.","DOI":"10.1117\/12.2549600"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.2776256"},{"key":"e_1_3_3_10_2","unstructured":"Zhang X. Study on optimization and correction of digital breast tomography projection data. Master\u2019s Thesis Southern Medical University CN 2017."},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/58\/3\/569"},{"key":"e_1_3_3_12_2","first-page":"192","article-title":"Design and optimization of image chain in digital breast tomography system","volume":"39","author":"Li M","year":"2019","unstructured":"Li M, Ma K, Tao X, et al. Design and optimization of image chain in digital breast tomography system. Journal of Southern Medical University 2019; 39: 192\u2013200.","journal-title":"Journal of Southern Medical University"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.3285038"},{"key":"e_1_3_3_14_2","unstructured":"Deng Y. Evaluation of digital breast tomography algorithm and study of image enhancement methods. Master\u2019s Thesis Southern Medical University CN 2021."},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","unstructured":"Acciavatti RJ Rodr\u00edguez-Ruiz A Vent TL et al. Analysis of volumeoverestimation artifacts in the breast outline segmentation in tomosynthesis. Medical Imaging 2018: Physics of Medical Imaging- Proceedings of SPIE 2018; 10573: 1057359.","DOI":"10.1117\/12.2293253"},{"key":"e_1_3_3_16_2","first-page":"898","article-title":"Performance analysis of digital breast tomography based on system simulation","volume":"41","author":"Deng Y","year":"2021","unstructured":"Deng Y, Zhu M, Li H, et al. Performance analysis of digital breast tomography based on system simulation. Journal of Southern Medical University 2021; 41: 898\u2013908.","journal-title":"Journal of Southern Medical University"},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","unstructured":"Bakic PR Dustler M Ng S et al. Pre-processing for image quality improvement in simultaneous DBT and mechanical imaging. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 2020;11312.1131257.","DOI":"10.1117\/12.2550986"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2879921"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1118\/1.4770281"},{"key":"e_1_3_3_20_2","unstructured":"Geng ZY. Research on denoising algorithms in CT image reconstruction. Master\u2019s Thesis Tianjin University of Technology and Education CN 2024."},{"key":"e_1_3_3_21_2","first-page":"189","article-title":"Low-dose CT image denoising based on improved DTCWT and guided filtering","volume":"42","author":"Xu Y","year":"2023","unstructured":"Xu Y, Zhang Z, Zhu H, et al. Low-dose CT image denoising based on improved DTCWT and guided filtering. Foreign Electronic Measurement Technology 2023; 42: 189\u2013194.","journal-title":"Foreign Electronic Measurement Technology"},{"key":"e_1_3_3_22_2","first-page":"126","article-title":"Sacroiliac Joint CT Image Enhancement Algorithm Based on Improved MSR and \u03b3-CLAHE","volume":"9","author":"Yan J","year":"2025","unstructured":"Yan J, Li J. Sacroiliac Joint CT Image Enhancement Algorithm Based on Improved MSR and \u03b3-CLAHE. Modern Information Technology 2025; 9: 126\u2013129+134.","journal-title":"Modern Information Technology"},{"key":"e_1_3_3_23_2","first-page":"44","article-title":"Research on calculating CLAHE parameters algorithm based on image entropy","volume":"38","author":"Yu Y","year":"2024","unstructured":"Yu Y. Research on calculating CLAHE parameters algorithm based on image entropy. Journal of Lanzhou University of Arts and Science (Natural Science Edition) 2024; 38: 44\u201350.","journal-title":"Journal of Lanzhou University of Arts and Science (Natural Science Edition)"},{"key":"e_1_3_3_24_2","unstructured":"Huang J. Research on fast iterative reconstruction algorithm of digital breast tomography. Master\u2019s Thesis Southern Medical University CN 2019."},{"key":"e_1_3_3_25_2","first-page":"5883","article-title":"Digital breast tomosynthesis: studies of the effects of acquisition geometry on contrast-to-noise ratio and observer preference of low-contrast objects in breast phantom images","volume":"59","author":"Goodsitt MM","year":"2014","unstructured":"Goodsitt MM, Chan H-P, Schmitz A, et al. Digital breast tomosynthesis: studies of the effects of acquisition geometry on contrast-to-noise ratio and observer preference of low-contrast objects in breast phantom images. Phys. Med 2014; 59: 5883\u20135902.","journal-title":"Phys. Med"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2458983"},{"key":"e_1_3_3_27_2","unstructured":"Liu M. Statistical research on DBT reconstruction and artifact suppression. Bachelor\u2019s Thesis Nanjing University of Posts and Telecommunications CN 2022."},{"key":"e_1_3_3_28_2","unstructured":"Yu C Li H Liu M et al. A reconstruction algorithm for digital breast tomography based on weight-adjusted second order blind identification - a new method. Patent CN202010668016.5 CN 2022."},{"key":"e_1_3_3_29_2","first-page":"129","article-title":"Research on CT characteristics reconstruction technology","volume":"7","author":"Zhao Y","year":"2016","unstructured":"Zhao Y, Wang L, Han Y. Research on CT characteristics reconstruction technology. J Meas Sci Instrum. 2016; 7: 129\u2013135.","journal-title":"J Meas Sci Instrum."},{"key":"e_1_3_3_30_2","unstructured":"Ye H Cai Y Xu W et al.The diagnostic value of full-field digital mammography combined with digital three-dimensional mammography in breast diseases. Modern Hospitals 2017; 17: 764\u2013766+769."},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radphyschem.2023.111241"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1177\/15330338221104567"},{"key":"e_1_3_3_33_2","article-title":"Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data","volume":"67","author":"Marshall NW","year":"2022","unstructured":"Marshall NW, Bosmans H. Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data. Phys Med Biol 2022; 67:22TR03.","journal-title":"Phys Med Biol"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apradiso.2021.109862"},{"key":"e_1_3_3_35_2","first-page":"327","article-title":"Development of a quality control protocol for digital breast tomosynthesis systems in the TOMMY trial","volume":"7361","author":"Strudley CJ","year":"2012","unstructured":"Strudley CJ, Young KC, Oduko JM, et al. Development of a quality control protocol for digital breast tomosynthesis systems in the TOMMY trial. Lect Notes Comput Sci 2012; 7361: 327\u2013334.","journal-title":"Lect Notes Comput Sci"}],"container-title":["Journal of X-Ray Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08953996261469039","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/08953996261469039","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08953996261469039","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T23:16:26Z","timestamp":1790118986000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/08953996261469039"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,29]]},"references-count":34,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1177\/08953996261469039"],"URL":"https:\/\/doi.org\/10.1177\/08953996261469039","relation":{},"ISSN":["0895-3996","1095-9114"],"issn-type":[{"value":"0895-3996","type":"print"},{"value":"1095-9114","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,29]]}}}