{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:46:43Z","timestamp":1776890803621,"version":"3.51.2"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,11]],"date-time":"2023-06-11T00:00:00Z","timestamp":1686441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014Portugal","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia\u2014Portugal","award":["2022.12271.BD"],"award-info":[{"award-number":["2022.12271.BD"]}]},{"name":"Bolsa de Investiga\u00e7\u00e3o para Doutoramento FCT","award":["UIDB\/00645\/2020"],"award-info":[{"award-number":["UIDB\/00645\/2020"]}]},{"name":"Bolsa de Investiga\u00e7\u00e3o para Doutoramento FCT","award":["2022.12271.BD"],"award-info":[{"award-number":["2022.12271.BD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image quality improvement provided by DBT is accompanied by an increase in the radiation dose for the patient. Here, a method based on 2D Total Variation (2D TV) minimization to improve image quality without the need to increase the dose was proposed. Two phantoms were used to acquire data at different dose ranges (0.88\u20132.19 mGy for Gammex 156 and 0.65\u20131.71 mGy for our phantom). A 2D TV minimization filter was applied to the data, and the image quality was assessed through contrast-to-noise ratio (CNR) and the detectability index of lesions before and after filtering. The results showed a decrease in 2D TV values after filtering, with variations of up to 31%, increasing image quality. The increase in CNR values after filtering showed that it is possible to use lower doses (\u221226%, on average) without compromising on image quality. The detectability index had substantial increases (up to 14%), especially in smaller lesions. So, not only did the proposed approach allow for the enhancement of image quality without increasing the dose, but it also improved the chances of detecting small lesions that could be overlooked.<\/jats:p>","DOI":"10.3390\/jimaging9060119","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T01:28:21Z","timestamp":1686533301000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Digital Breast Tomosynthesis: Towards Dose Reduction through Image Quality Improvement"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1931-294X","authenticated-orcid":false,"given":"Ana M.","family":"Mota","sequence":"first","affiliation":[{"name":"Faculdade de Ci\u00eancias, Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5524-2846","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Mendes","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Universidade de Lisboa, 1749-016 Lisboa, Portugal"},{"name":"Faculdade de Ci\u00eancias, LASIGE, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8048-7896","authenticated-orcid":false,"given":"Nuno","family":"Matela","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, Instituto de Biof\u00edsica e Engenharia Biom\u00e9dica, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.breast.2022.08.010","article-title":"Current and future burden of breast cancer: Global statistics for 2020 and 2040","volume":"66","author":"Arnold","year":"2022","journal-title":"Breast"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21708","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.soncn.2017.02.009","article-title":"Early detection and screening for breast cancer","volume":"Volume 33","author":"Coleman","year":"2017","journal-title":"Proceedings of the Seminars in Oncology Nursing"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lewin, J.M. (2008). Digital mammography. Cancer Imaging, 455\u2013458.","DOI":"10.1016\/B978-012374212-4.50050-X"},{"key":"ref_5","first-page":"1","article-title":"Benefits and harms of mammography screening","volume":"17","author":"Lousdal","year":"2015","journal-title":"Breast Cancer Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1148\/radiol.2015141303","article-title":"Digital breast tomosynthesis: State of the art","volume":"277","author":"Vedantham","year":"2015","journal-title":"Radiology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rcl.2017.06.004","article-title":"Screening for breast cancer","volume":"55","author":"Niell","year":"2017","journal-title":"Radiol. Clin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1148\/radiol.13130307","article-title":"Comparison of tomosynthesis plus digital mammography and digital mammography alone for breast cancer screening","volume":"269","author":"Haas","year":"2013","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1016\/S1470-2045(18)30521-7","article-title":"One-view breast tomosynthesis versus two-view mammography in the Malm\u00f6 Breast Tomosynthesis Screening Trial (MBTST): A prospective, population-based, diagnostic accuracy study","volume":"19","author":"Zackrisson","year":"2018","journal-title":"Lancet Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1016\/S1470-2045(16)30101-2","article-title":"Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): A population-based prospective study","volume":"17","author":"Bernardi","year":"2016","journal-title":"Lancet Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s00330-015-3803-3","article-title":"Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: Results from the Malm\u00f6 Breast Tomosynthesis Screening Trial, a population-based study","volume":"26","author":"Andersson","year":"2016","journal-title":"Eur. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1148\/radiol.2015142566","article-title":"Accuracy of digital breast tomosynthesis for depicting breast cancer subgroups in a UK retrospective reading study (TOMMY Trial)","volume":"277","author":"Gilbert","year":"2015","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1148\/radiol.2018171361","article-title":"Digital breast tomosynthesis and synthetic 2D mammography versus digital mammography: Evaluation in a population-based screening program","volume":"287","author":"Hofvind","year":"2018","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10549-017-4431-1","article-title":"Clinical implementation of synthesized mammography with digital breast tomosynthesis in a routine clinical practice","volume":"166","author":"Freer","year":"2017","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s11517-021-02313-1","article-title":"Comparison of two-dimensional synthesized mammograms versus original digital mammograms: A quantitative assessment","volume":"59","author":"Tan","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1093\/rpd\/nct120","article-title":"\u2018In vivo\u2019 average glandular dose evaluation: One-to-one comparison between digital breast tomosynthesis and full-field digital mammography","volume":"157","author":"Cavagnetto","year":"2013","journal-title":"Radiat. Prot. Dosim."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s00330-017-5024-4","article-title":"Radiation dose with digital breast tomosynthesis compared to digital mammography: Per-view analysis","volume":"28","author":"Gennaro","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1109\/TMI.2017.2715826","article-title":"Method for simulating dose reduction in digital breast tomosynthesis","volume":"36","author":"Borges","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1118\/1.3273064","article-title":"Simulation of dose reduction in tomosynthesis","volume":"37","author":"Svalkvist","year":"2010","journal-title":"Med. Phys."},{"key":"ref_20","first-page":"89","article-title":"Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing","volume":"Volume 10574","author":"Liu","year":"2018","journal-title":"Proceedings of the Medical Imaging 2018: Image Processing"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gomi, T., Kijima, Y., Kobayashi, T., and Koibuchi, Y. (2022). Evaluation of a Generative Adversarial Network to Improve Image Quality and Reduce Radiation-Dose during Digital Breast Tomosynthesis. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020495"},{"key":"ref_22","first-page":"36","article-title":"Pipeline for effective denoising of digital mammography and digital breast tomosynthesis","volume":"Volume 10132","author":"Borges","year":"2017","journal-title":"Proceedings of the Medical Imaging 2017: Physics of Medical Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.1118\/1.1786692","article-title":"A comparison of reconstruction algorithms for breast tomosynthesis","volume":"31","author":"Wu","year":"2004","journal-title":"Med. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20190345","DOI":"10.1259\/bjr.20190345","article-title":"Evaluation of a new image reconstruction method for digital breast tomosynthesis: Effects on the visibility of breast lesions and breast density","volume":"92","author":"Krammer","year":"2019","journal-title":"Br. J. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1118\/1.4919680","article-title":"Total variation minimization filter for DBT imaging","volume":"42","author":"Mota","year":"2015","journal-title":"Med. Phys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/B:JMIV.0000011321.19549.88","article-title":"An algorithm for total variation minimization and applications","volume":"20","author":"Chambolle","year":"2004","journal-title":"J. Math. Imaging Vis."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sawatzky, A., Brune, C., M\u00fcller, J., and Burger, M. (2009, January 2\u20134). Total variation processing of images with Poisson statistics. Proceedings of the Computer Analysis of Images and Patterns: 13th International Conference, CAIP 2009, M\u00fcnster, Germany.","DOI":"10.1007\/978-3-642-03767-2_65"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5949","DOI":"10.1088\/0031-9155\/56\/18\/011","article-title":"Low-dose CT reconstruction via edge-preserving total variation regularization","volume":"56","author":"Tian","year":"2011","journal-title":"Phys. Med. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1137\/15M1047325","article-title":"Multicontrast MRI reconstruction with structure-guided total variation","volume":"9","author":"Ehrhardt","year":"2016","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-12-112","article-title":"Digital breast tomosynthesis image reconstruction using 2D and 3D total variation minimization","volume":"12","author":"Ertas","year":"2013","journal-title":"Biomed. Eng. Online"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Seyyedi, S., and Yildirim, I. (2014, January 26\u201330). 3D digital breast tomosynthesis image reconstruction using anisotropic total variation minimization. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6945009"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5239082","DOI":"10.1155\/2018\/5239082","article-title":"Use of a total variation minimization iterative reconstruction algorithm to evaluate reduced projections during digital breast Tomosynthesis","volume":"2018","author":"Gomi","year":"2018","journal-title":"BioMed Res. Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e735","DOI":"10.1002\/mp.13763","article-title":"Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233","volume":"46","author":"Samei","year":"2019","journal-title":"Med. Phys."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1134\/S1054661816020231","article-title":"A method of total variation to remove the mixed Poisson-Gaussian noise","volume":"26","author":"Thanh","year":"2016","journal-title":"Pattern Recognit. Image Anal."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/6\/119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:52:49Z","timestamp":1760125969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/6\/119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,11]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["jimaging9060119"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9060119","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,11]]}}}