Progressive surface representation and refinement for rigid ultrasound-CT registration: a phantom study

Rigid ultrasound (US) and computed tomography (CT) registration remains challenging in computer assisted knee surgery because intraoperative US provides incomplete bone-surface observations that may contain outliers and spatially extended echo bands. This study presents and evaluates Progressive Surface Representation and Refinement (PSRR), a staged covariance-aware framework for rigid US-CT registration. PSRR constructs confidence-weighted local representative points and anisotropic covariance descriptors from segmented US bone-surface observations. It then estimates the rigid US-CT transformation using progressively gated correspondences, with CT-normal-guided dynamic updates of target points and their covariances during iterative refinement. PSRR was evaluated against five representative baseline methods in 120 controlled water-tank phantom registration trials spanning different effective overlap ratios and initial-pose perturbation levels. Across the 120 registration trials, PSRR achieved the lowest overall mean value of the average surface distance (ASD), at 2.51 ± 2.77 mm, and the highest 5-mm ASD threshold-attainment rate of 89.2% at the 5-mm ASD threshold among the compared methods. Compared with ICP and G-ICP, PSRR reduced all-case ASD by 48.6% and 35.8%, respectively. PSRR also achieved the lowest ASD under low overlap (2.57 ± 3.00 mm) and high perturbations (4.18 ± 4.21 mm) conditions. In landmark-based evaluation, PSRR yielded a mean target registration error (TRE) of 2.09 mm, lower than those of ICP and G-ICP. Ablation analyses further supported the contributions of confidence-weighted local representative construction, CT-normal-guided target refinement, and dynamic covariance updating. In controlled water-tank phantom experiments, PSRR improved surface-distance and landmark-based registration accuracy under reduced-overlap and perturbed-initialization conditions. Further evaluation in tissue-mimicking, cadaveric, or clinical settings is required to establish its intraoperative applicability.

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Publication Details

Journal
BMC Medical Imaging
Published
2026-09-11
DOI
https://doi.org/10.1186/s12880-026-02745-x
Primary Topic
Ultrasound Imaging and Elastography
Type
article
Field-Weighted Citation Impact
0.00

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article

Progressive surface representation and refinement for rigid ultrasound-CT registration: a phantom study

Juying Huang, Shangqi Cui, Zhi Yang
BMC Medical Imaging
Ultrasound Imaging and Elastography
article

Progressive surface representation and refinement for rigid ultrasound-CT registration: a phantom study

Juying Huang, Shangqi Cui, Zhi Yang
article en

Abstract

Rigid ultrasound (US) and computed tomography (CT) registration remains challenging in computer assisted knee surgery because intraoperative US provides incomplete bone-surface observations that may contain outliers and spatially extended echo bands. This study presents and evaluates Progressive Surface Representation and Refinement (PSRR), a staged covariance-aware framework for rigid US-CT registration. PSRR constructs confidence-weighted local representative points and anisotropic covariance descriptors from segmented US bone-surface observations. It then estimates the rigid US-CT transformation using progressively gated correspondences, with CT-normal-guided dynamic updates of target points and their covariances during iterative refinement. PSRR was evaluated against five representative baseline methods in 120 controlled water-tank phantom registration trials spanning different effective overlap ratios and initial-pose perturbation levels. Across the 120 registration trials, PSRR achieved the lowest overall mean value of the average surface distance (ASD), at 2.51 ± 2.77 mm, and the highest 5-mm ASD threshold-attainment rate of 89.2% at the 5-mm ASD threshold among the compared methods. Compared with ICP and G-ICP, PSRR reduced all-case ASD by 48.6% and 35.8%, respectively. PSRR also achieved the lowest ASD under low overlap (2.57 ± 3.00 mm) and high perturbations (4.18 ± 4.21 mm) conditions. In landmark-based evaluation, PSRR yielded a mean target registration error (TRE) of 2.09 mm, lower than those of ICP and G-ICP. Ablation analyses further supported the contributions of confidence-weighted local representative construction, CT-normal-guided target refinement, and dynamic covariance updating. In controlled water-tank phantom experiments, PSRR improved surface-distance and landmark-based registration accuracy under reduced-overlap and perturbed-initialization conditions. Further evaluation in tissue-mimicking, cadaveric, or clinical settings is required to establish its intraoperative applicability.

BMC Medical Imaging
Capital Medical University (CN)
National Natural Science Foundation of China, Beijing Municipal Health Commission
Openalex Percentile: Top 11%
Ultrasound Imaging and Elastography
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