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ZTE-DLCSC images were reconstructed from raw data. Ten bone regions were assessed for lesion presence and number by a senior radiologist with 26 years\u2019 experience and two junior readers (a radiology fellow and a resident). Repeatability and reproducibility (Gwet\u2019s agreement coefficients AC1 and AC2), differences in quantitative counts, and accuracy were evaluated\n                      <jats:italic>per<\/jats:italic>\n                      -sequence\/region\/reader.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Ten patients, aged 67\u2009\u00b1\u200912 years (mean\u2009\u00b1\u2009standard deviation), were enrolled. Considering all regions, repeatability was at least moderate for ZTE (AC1 \u2265 0.45), good for ZTE-DLCSC and BB (AC1 \u2265 0.60) and very good for CT (AC1\u2009\u2265\u20090.80). Reproducibility was at least fair for ZTE and BB (AC2\u2009\u2265\u20090.20), good for ZTE-DLCSC (AC2\u2009\u2265\u20090.60) and very good for CT (AC2\u2009\u2265\u20090.80). Accuracy of ZTE-DLCSC ranged 80\u201293%; compared to ZTE, accuracy increased by 23% and 25% for junior readers (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.010 and\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.002 respectively) and by 32% for the senior reader (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). ZTE-DLCSC detected more lesions than ZTE\/BB (+30%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.011; +25%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.024).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DL-driven DLCSC reconstruction improves the reliability and accuracy of the ZTE sequence for detecting MM lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>The combination of deep learning reconstruction and chemical shift correction improves the accuracy of zero echo time MRI for detecting myeloma lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration<\/jats:title>\n                    <jats:p>This study was approved by the institutional ethics committee and registered at ClinicalTrials.gov (NCT05381077).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>The performance of ZTE-DLCSC for detecting osteolytic MM lesions was compared with that of native ZTE and BB sequences using CT as reference.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>ZTE-DLCSC showed better intra-reader agreement (repeatability) and inter-reader agreement (reproducibility) than ZTE and BB sequences.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>ZTE-DLCSC had fewer false positives and false negatives than ZTE and BB sequences.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>ZTE-DLCSC detected more lesions than ZTE and BB sequences.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00734-x","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T12:51:32Z","timestamp":1780663892000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions"],"prefix":"10.1186","volume":"10","author":[{"given":"Darius","family":"Lepot","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Chabot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ga\u00ebtan","family":"Duch\u00eane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagar","family":"Mandava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maggie","family":"Fung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julie","family":"Poujol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie-Christiane","family":"Vekemans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Perrine","family":"Triqueneaux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Watt\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Gheysens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Michoux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3568-3254","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric E.","family":"Lecouvet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"734_CR1","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.3109\/10428194.2012.740559","volume":"54","author":"J Hillengass","year":"2013","unstructured":"Hillengass J, Landgren O (2013) Challenges and opportunities of novel imaging techniques in monoclonal plasma cell disorders: imaging \u201cearly myeloma. 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These authors had no control over the data or analysis in the study. All other authors are not GE HealthCare employees and controlled the inclusion of all data and information that might present a conflict of interest for the GE HealthCare employees. These authors declare that they have no financial or non-financial competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"81"}}