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Med. Biol."],"published-print":{"date-parts":[[2026,10,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    We propose DiffINR for rapid deformation-driven estimation of patient-specific target volumes from limited measurements. DiffINR uses a coordinate-based implicit neural representation (INR) to estimate a dense deformation vector field (DVF) that warps a high-quality prior volume (source) to the target anatomy, with patient-specific INR initialization provided by a population-based conditional hyperdiffusion model.\n                    <jats:italic>Approach.<\/jats:italic>\n                    During offline training, INRs were optimized on fully sampled source-target image volume pairs to represent DVFs. A Transformer diffusion model, hyperdiffusion, learned the distribution of the resulting INR weights. The model was jointly conditioned on anatomical features extracted from the training source and target volumes by a foundation model and on features encoded directly from limited measurements. At inference, hyperdiffusion generated INR initial weights, which were further fine-tuned by patient-specific optimization using measurement-domain fidelity and DVF-smoothness losses. We evaluated DiffINR on two tasks: cone-beam computed tomography (CBCT) estimation from limited-angle x-ray projections and real-time volumetric magnetic resonance imaging (MRI) estimation from ultra-sparse k-space data.\n                    <jats:italic>Main Results.<\/jats:italic>\n                    For orthogonal-view 90\u00b0 CBCT estimation, DiffINR achieved a structural similarity index measure (SSIM) of 0.962 \u00b1 0.021, relative error of 6.84% \u00b1 1.70%, and target registration error of 3.61 \u00b1 1.27 mm, outperforming the iterative methods, population-based models, and randomly initialized INR baselines. For volumetric MRI estimation with 13 radial spokes per slice, DiffINR achieved an SSIM of 0.985 \u00b1 0.007, Dice coefficient of 0.89 \u00b1 0.04, and center-of-mass error of 1.30 \u00b1 1.09 mm. DiffINR showed high robustness to data distribution shifts at test time, including dataset changes, limited-angle setting variations, and different MRI sampling sparsity levels. Test-time runtime was 23 s for CBCT estimation and 27 s for MRI estimation, compared with 795 s and 928 s, respectively, for INR optimization from scratch.\n                    <jats:italic>Significance.<\/jats:italic>\n                    By combining hyperdiffusion-based INR initialization with patient-specific data consistency, DiffINR achieves fast and accurate deformation-driven volumetric image estimation.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/aeaaa0","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T22:52:29Z","timestamp":1790031149000},"page":"195030","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DiffINR: conditional neural field diffusion for deformation-driven volumetric image estimation from limited measurements"],"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\/0000-0002-8033-2755","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":[[2026,10,9]]},"reference":[{"key":"pmbaeaaa0bib1","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","type":"journal-article","article-title":"Voxelmorph: a learning framework for deformable medical image registration","volume":"38","author":"Balakrishnan","year":"2019","journal-title":"IEEE Trans. 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