Scholarlink

Purpose <p>Bone 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.</p> Methods <p>We 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.</p> Results <p>Simulation and experimental results demonstrate the system achieves millimeter-level bone surface sampling accuracy within a 40&#xa0;mm depth range, with multiple measurement errors below 1&#xa0;mm. In registration tests using tibia and spine models, the system achieved average registration errors of 1.426 ± 0.300&#xa0;mm and 1.262 ± 0.283&#xa0;mm, respectively.</p> Conclusion <p>This 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Miniature A-Mode Ultrasound System for Noninvasive Bone Surface Point Cloud Acquisition

  • 期刊论文

  • 发表日期：

    2025年11月24日

查看全文
  • Tianya Xu,
  • Chuanba Liu,
  • Jiayang Mu,
  • Pengbo Wang,
  • WenJuan Ma,
  • Xiqi Jian,
  • Jiumin Yang,
  • Yanqiu Zhang

摘要

Purpose

Bone 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.

Methods

We 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.

Results

Simulation 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.

Conclusion

This 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.