{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T05:11:45Z","timestamp":1792213905067,"version":"4.4.0"},"reference-count":42,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32371367"],"award-info":[{"award-number":["32371367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate 3D assessment of the weight\u2010bearing lumbar spine is crucial for diagnosing various spinal pathologies. However, existing biplanar X\u2010ray reconstruction methods struggle with complex pathologies and low\u2010contrast structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to propose a fully automated framework for high\u2010accuracy 3D lumbar spine reconstruction from biplanar X\u2010ray images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Statistical shape models (SSMs) of L1\u2013L5 vertebrae were constructed from 270 lumbar CT scans. A multi\u2010task deep learning network was designed to simultaneously isolate vertebral signals from biplanar X\u2010rays and detect anatomical landmarks. Landmark predictions were used for pose initialization, followed by SSM\u2010based 2D\u20133D registration using an anatomy\u2010aware weighted optimization strategy that emphasized the transverse and spinous processes. The method was validated against patient\u2010specific CT segmentations registered to the biplanar imaging geometry. The method was evaluated in subjects without significant osseous abnormalities and in pathological patients from an independent center.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed network achieved Dice coefficients of 0.991 and 0.989 for anteroposterior and lateral vertebral signal isolation, respectively. The overall 3D reconstruction accuracy was 0.85\u00a0\u00b1\u00a00.24\u00a0mm. Reconstruction accuracy was 0.80\u00a0\u00b1\u00a00.15\u00a0mm in Center 1 and 1.32\u00a0\u00b1\u00a00.46\u00a0mm in the pathological cohort from Center 2. The method maintained stable performance under additive Gaussian noise and across different lumbar postures. Ablation studies confirmed the contributions of signal isolation, landmark\u2010based initialization, and anatomy\u2010aware optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This robust, high\u2010accuracy automated method precisely reconstructs complex lumbar structures from biplanar X\u2010rays, even for severely pathological vertebrae. Its ability to maintain accuracy under extreme movement and improve reconstruction quality in complex regions highlights its significant potential for clinical diagnosis and surgical planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70570","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T07:09:58Z","timestamp":1784704198000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate automated 3D lumbar spine reconstruction from biplanar X\u2010rays using multi\u2010task deep learning and anatomy\u2010aware optimization"],"prefix":"10.1002","volume":"53","author":[{"given":"Wanxin","family":"Yu","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Med\u2010X Research Institute Shanghai Jiao Tong University  Shanghai China"},{"name":"Engineering Research Center of Digital Medicine and Clinical Translation Ministry of Education  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