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Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Material and methods<\/jats:title>\n                <jats:p>In a single-center prospective study, 60 consecutive patients with LAM underwent chest CT at standard and ultra-low radiation doses. Standard dose scan was performed with standard copper filter and ultra-low dose scan was performed with SilverBeam filter. Scans were reconstructed using a soft tissue kernel with deep learning reconstruction (AiCE) technique and using a soft tissue kernel with hybrid iterative reconstruction (AIDR3D). Cyst scores were quantified by semi-automated software. Signal-to-noise ratio (SNR) was calculated for each reconstruction. Data were analyzed by linear correlation, paired <jats:italic>t<\/jats:italic>-test, and Bland\u2013Altman plots.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Patients averaged 49.4\u00a0years and 100% were female with mean BMI 26.6\u2009\u00b1\u20096.1\u00a0kg\/m<jats:sup>2<\/jats:sup>. Cyst score measured by AiCE reconstruction with SilverBeam filter correlated well with that of AIDR3D reconstruction with standard filter, with a 1.5% difference, and allowed for an 85.5% median radiation dosage reduction (0.33\u00a0mSv vs. 2.27\u00a0mSv, respectively, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Compared to standard filter with AIDR3D, SNR for SilverBeam AiCE images was slightly lower (3.2 vs. 3.1, respectively, <jats:italic>p<\/jats:italic>\u2009=\u20090.005).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>SilverBeam filter with deep learning reconstruction reduces radiation dosage of chest CT, while maintaining accuracy of cyst quantification as well as image quality in cystic lung disease.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Clinical relevance statement<\/jats:title>\n                <jats:p>Radiation dosage from chest CT can be significantly reduced without sacrificing image quality by using silver filter in combination with a deep learning reconstructive algorithm.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p>\u2022 <jats:italic>Deep learning reconstruction in chest CT had no significant effect on cyst quantification when compared to conventional hybrid iterative reconstruction.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>SilverBeam filter reduced radiation dosage by 85.5% compared to standard dose chest CT.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>SilverBeam filter in coordination with deep learning reconstruction maintained image quality and diagnostic accuracy for cyst quantification when compared to standard dose CT with hybrid iterative reconstruction.<\/jats:italic><\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1007\/s00330-024-10649-z","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T20:02:20Z","timestamp":1708632140000},"page":"5613-5620","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM)"],"prefix":"10.1007","volume":"34","author":[{"given":"Alexa E.","family":"Golbus","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chloe","family":"Steveson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John L.","family":"Schuzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirley F.","family":"Rollison","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tat\u2019Yana","family":"Worthy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda M.","family":"Jones","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia","family":"Julien-Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel","family":"Moss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0743-9369","authenticated-orcid":false,"given":"Marcus Y.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"10649_CR1","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1055\/s-0040-1702195","volume":"41","author":"KF Xu","year":"2020","unstructured":"Xu KF, Xu W, Liu S et al (2020) Lymphangioleiomyomatosis. 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Chloe Steveson and John Schuzer are employees of Canon Medical Systems.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained (National Institutes of Health, 96-H-0100).","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"No study subjects or cohorts have been previously reported.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 prospective\u2022 experimental\u2022 performed at one institution","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}