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Med. Biol."],"published-print":{"date-parts":[[2026,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Accurate lung motion estimation from 4D computed tomography (CT) is essential for image-guided radiotherapy and thoracic motion analysis, but remains challenging in vessel-rich regions where weak contrast, fine structures, and heterogeneous mechanics limit conventional registration. This work aims to develop an anatomy-informed finite-element digital volume correlation (FE-DVC) framework for accurate and mechanically plausible lung motion estimation.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose a structure-tensor-guided heterogeneous anisotropic FE-DVC method. Structure tensors extracted from the reference CT image are used as heuristic anatomical priors to modulate element-wise regularization strength and align an effective anisotropic regularization frame with bronchovascular directions. A lung-specific multi-mesh strategy uses finer elements in vessel-containing regions and coarser elements in parenchyma. A practical workflow based on L-curve analysis and Jacobian-based deformation regularity is introduced to reduce empirical parameter tuning. The method was evaluated on three lung 4D-CT datasets and compared with Demons and pTV registration.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The proposed method consistently reduced landmark target registration error across all datasets, with most errors concentrated below 2 mm and fewer large outliers. In a representative DIR-Lab case, normalized correlation residuals were reduced to 0.059 and 0.100 in axial and coronal views, compared with 0.172\/0.256 for Demons and 0.088\/0.138 for pTV. The recovered strain fields showed coherent vessel-oriented deformation, and boundary-driven FE simulations confirmed vessel-aligned strain concentrations only under heterogeneous anisotropic regularization. Singular value decomposition further showed that the first motion mode explained more than 90% of deformation energy and the first three modes exceeded 99%.\n                    <jats:italic>Significance.<\/jats:italic>\n                    This framework integrates CT-derived anatomical organization with mechanically interpretable regularization, improving vessel-scale lung motion recovery and supporting future strain-based biomarkers and pulmonary structure\u2013function modeling.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae79cd","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T22:54:03Z","timestamp":1780959243000},"page":"125042","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Lung motion estimation from 4D CT using structure-tensor-guided finite-element digital volume correlation"],"prefix":"10.1088","volume":"71","author":[{"given":"Haizhou","family":"Liu","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences"},{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Liu","sequence":"additional","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxi","family":"Jin","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0618-6240","authenticated-orcid":true,"given":"Zhanli","family":"Hu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehong","family":"Luo","sequence":"additional","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3340-3778","authenticated-orcid":true,"given":"Liwen","family":"Wan","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"pmbae79cdbib1","doi-asserted-by":"publisher","first-page":"95","DOI":"10.5566\/ias.v33.p95-105","type":"journal-article","article-title":"Structure tensor image filtering using Riemannian L1 and L\u221e center-of-mass","volume":"33","author":"Angulo","year":"2014","journal-title":"Image Anal. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-12-03","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-08","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-29","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}