ArticleAnnals of biomedical engineering2026
A Miniature A-Mode Ultrasound System for Noninvasive Bone Surface Point Cloud Acquisition.
Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Effects of Crew Seat Inclination on Multi-Organ Injury Risk in Astronauts During Off-Nominal High-g Landing Impact.Annals of biomedical engineering · 2026Article
- Generative adversarial networks for high-fidelity 3D point cloud completion.Scientific reports · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
purposeBone surface registration in current computer-assisted surgical navigation primarily relies on the manual selection of anatomical landmarks and invasive bone surface sampling using mechanical probes. This approach is traumatic, highly dependent on operator expertise, and difficult to automate. Although ultrasound-based registration methods have been explored and demonstrated certain application potential, most existing techniques still rely on manual feature extraction. Moreover, their accuracy is generally limited by the structural and geometric constraints of the probes. To address these limitations, we propose and develop a real-time, noninvasive bone surface depth acquisition system based on A-mode ultrasound, aiming to replace traditional mechanical probing and provide a high-precision, automated, and noninvasive alternative for bone surface registration.
methodsWe optimized the probe geometry using finite-difference time-domain simulations and fabricated a four-element A-mode probe with integrated control and data acquisition systems. An automatic bone-echo detection algorithm based on Gaussian filtering and Hilbert transform was developed. System performance was validated through simulations and benchtop experiments on 3D-printed tibia and spine phantoms.
resultsSimulation and experimental results demonstrate the system achieves millimeter-level bone surface sampling accuracy within a 40 mm depth range, with multiple measurement errors below 1 mm. In registration tests using tibia and spine models, the system achieved average registration errors of 1.426 ± 0.300 mm and 1.262 ± 0.283 mm, respectively.
conclusionThis study proposes and builds an A-mode ultrasound system that, through optimization of a miniature probe and development of an automatic bone echo recognition algorithm, establishes a novel and potentially clinically valuable approach for achieving millimeter-level accuracy in automated, noninvasive registration.
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