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Med. Biol."],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Existing volumetric magnetic resonance imaging (MRI) techniques are constrained by the trade-off between acquisition time and image quality, limiting accuracy in motion-impacted sites such as the liver. To enable fast, better-quality volumetric imaging with sufficient spatiotemporal resolution, we developed a time-resolved volumetric MRI technique that recovers 3D volumes from acquired 2D MR slices for real-time 3D anatomy and motion tracking.\n                    <jats:italic>Approach.<\/jats:italic>\n                    2D MR slices dynamically acquired in time and space were mapped to time-resolved 3D MRIs using a one-shot slice-to-volume framework, S2V-DREME. The model jointly estimates a reference 3D MRI and time-resolved deformation vector fields (DVFs) that warp the reference volume into dynamic 3D MRIs. The reference volume is represented by a spatial implicit neural representation (INR), while the DVFs are derived via low-rank motion modeling. Motion basis components (MBCs) are generated by a spline-enhanced INR (SINR)-based motion generator, with coefficients inferred by a feature-wise linear modulation-based motion encoder. A progressive optimization strategy sequentially initializes the spatial INR and MBCs before joint optimization. The loss function integrates slice data fidelity, total variation regularization, MBC normalization, and DVF smoothness constraints.\n                    <jats:italic>Main results.<\/jats:italic>\n                    S2V-DREME generates time-resolved volumetric MRIs from 2D MR slice inputs. It was evaluated on digital phantom extended cardiac torso (XCAT), physical phantom, and human studies. In XCAT, it accurately captured regular and irregular motion during dynamic reconstruction (training stage, Dice similarity coefficient (DSC)\/COME: 0.92 \u00b1 0.03\/0.98 \u00b1 0.43 mm) and real-time motion estimation (testing stage, DSC\/COME: 0.91 \u00b1 0.02\/0.99 \u00b1 0.73 mm). Physical phantom experiments achieved a mean COME of 1.16 mm, and human studies demonstrated the feasibility of time-resolved 3D reconstruction from orthogonal-view and single-view slice acquisitions.\n                    <jats:italic>Significance.<\/jats:italic>\n                    By combining a novel step-and-shoot acquisition protocol with motion-compensated one-shot learning, S2V-DREME enables accurate time-resolved volumetric MRI reconstruction and motion tracking from cineslices, with strong potential for rapid volumetric imaging and real-time MR-guided adaptive radiotherapy.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae9d13","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T22:54:14Z","timestamp":1787352854000},"page":"175018","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["S2V-DREME: a time-resolved slice-to-volume MR image reconstruction framework with dynamic reconstruction and motion estimation"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6185-1074","authenticated-orcid":true,"given":"Xiaoxue","family":"Qian","sequence":"first","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7664-7257","authenticated-orcid":true,"given":"Hua-Chieh","family":"Shao","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2518-7989","authenticated-orcid":true,"given":"Jie","family":"Deng","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6285-3194","authenticated-orcid":true,"given":"Yan","family":"Dai","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8033-2755","authenticated-orcid":true,"given":"You","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Texas Southwestern Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"pmbae9d13bib1","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.1088\/0031-9155\/52\/12\/012","type":"journal-article","article-title":"Phase and amplitude binning for 4D-CT imaging","volume":"52","author":"Abdelnour","year":"2007","journal-title":"Phys. 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