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

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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]
© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved (Copyright applies to all Abstracts.)
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  Data: Prior-guided craniofacial soft-tissue reconstruction from CBCT under acquisition uncertainty via identity-quantized shape priors.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Mingzhang%22">Chen, Mingzhang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mingzhang.chen@univ-rennes.fr</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Jiasong%22">Wu, Jiasong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Luwei%22">Liu, Luwei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bao%2C+Han%22">Bao, Han</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Ye%22">Cao, Ye</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Qingmo%22">Sun, Qingmo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Senhadji%2C+Lotfi%22">Senhadji, Lotfi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shu%2C+Huazhong%22">Shu, Huazhong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shu.list@seu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Bin%22">Yan, Bin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> byan@njmu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Physics+in+Medicine+%26+Biology%22">Physics in Medicine & Biology</searchLink>. 2026, Vol. 71 Issue 9, p1-16. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Cone+beam+computed+tomography%22">Cone beam computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+quantization%22">Vector quantization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink>
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  Label: Abstract
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  Data: 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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  Data: <i>© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved</i> (Copyright applies to all Abstracts.)
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              M: 05
              Text: 2026
              Type: published
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