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Prior-guided craniofacial soft-tissue reconstruction from CBCT under acquisition uncertainty via identity-quantized shape priors.

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Bibliographic Details
Title: Prior-guided craniofacial soft-tissue reconstruction from CBCT under acquisition uncertainty via identity-quantized shape priors.
Authors: Chen, Mingzhang1 (AUTHOR) mingzhang.chen@univ-rennes.fr, Wu, Jiasong1 (AUTHOR), Liu, Luwei2 (AUTHOR), Bao, Han2 (AUTHOR), Cao, Ye2 (AUTHOR), Sun, Qingmo1 (AUTHOR), Senhadji, Lotfi3 (AUTHOR), Shu, Huazhong1 (AUTHOR) shu.list@seu.edu.cn, Yan, Bin2 (AUTHOR) byan@njmu.edu.cn
Source: Physics in Medicine & Biology. 2026, Vol. 71 Issue 9, p1-16. 16p.
Subjects: Cone beam computed tomography, Vector quantization, Computer-assisted image analysis (Medicine), Image reconstruction, Diagnostic imaging
Abstract: Objective. Cone-beam computed tomography (CBCT) suffers from low soft-tissue contrast and metal artifacts, yielding noisy and incomplete soft-tissue surface observations that limit craniofacial modeling for surgical planning. This study aims to reconstruct identity-preserving facial soft-tissue surfaces from CBCT-derived sparse surface evidence for clinical decision support. Approach. We propose a prior-guided reconstruction framework that introduces identity quantization as an anatomical shape prior to regularize an inherently underdetermined inference problem. By embedding residual vector quantization within a hierarchical encoder, we learn a discrete identity codebook that improves robustness to acquisition-induced outliers and missing regions while preserving patient-specific anatomical structure. A continuous style branch captures fine-scale details, and the two representations are fused to generate detailed meshes. Main results. Evaluation on 490 subjects, including 50 test cases, shows that our method achieves a 34-point landmark distance of 1.53 mm. Geometric accuracy (GA) is confirmed with an L1 Chamfer distance of 1.13 mm and normal consistency of 0.98. A prospective expert study reveals high clinical acceptance, with GA rated 4.27 ± 0.53 (out of 5). Significance. By incorporating an explicit anatomical prior to regularize reconstruction under acquisition uncertainty, our method improves the clinical usability of CBCT-based soft-tissue surface modeling for orthognathic surgery planning. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Objective. Cone-beam computed tomography (CBCT) suffers from low soft-tissue contrast and metal artifacts, yielding noisy and incomplete soft-tissue surface observations that limit craniofacial modeling for surgical planning. This study aims to reconstruct identity-preserving facial soft-tissue surfaces from CBCT-derived sparse surface evidence for clinical decision support. Approach. We propose a prior-guided reconstruction framework that introduces identity quantization as an anatomical shape prior to regularize an inherently underdetermined inference problem. By embedding residual vector quantization within a hierarchical encoder, we learn a discrete identity codebook that improves robustness to acquisition-induced outliers and missing regions while preserving patient-specific anatomical structure. A continuous style branch captures fine-scale details, and the two representations are fused to generate detailed meshes. Main results. Evaluation on 490 subjects, including 50 test cases, shows that our method achieves a 34-point landmark distance of 1.53 mm. Geometric accuracy (GA) is confirmed with an L1 Chamfer distance of 1.13 mm and normal consistency of 0.98. A prospective expert study reveals high clinical acceptance, with GA rated 4.27 ± 0.53 (out of 5). Significance. By incorporating an explicit anatomical prior to regularize reconstruction under acquisition uncertainty, our method improves the clinical usability of CBCT-based soft-tissue surface modeling for orthognathic surgery planning. [ABSTRACT FROM AUTHOR]
ISSN:00319155
DOI:10.1088/1361-6560/ae5ebc