Evolutionarily Optimized Electromagnetic Sensor Measurements for Robust Surgical Navigation

Xiongbiao Luo, Chaoyang Shi, Hui Qing Zeng, Henry C. Ewurum, Ying Wan, Yingying Guo, Seang Pagnha, Xiao Bin Zhang, Yan Ping Du, Xiangjian He

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

Miniaturized electromagnetic sensors are increasingly introduced to navigate surgical instruments to anatomical targets during minimally invasive procedures, such as endoscopic surgery. These sensors are usually attached at the distal tips of surgical instruments to track their three-dimensional motion represented by the position and orientation in six degrees of freedom. Unfortunately, these sensors suffer from inaccurate measurements and jitter errors due to the patient movement (e.g., respiratory motion) and magnetic field distortion. This paper proposes an evolutionary computing strategy to optimize the sensor measurements and improve the tracking accuracy of surgical navigation. We modified two evolutionary computation algorithms and proposed adaptive particle swarm optimization (APSO) and observation-boosted differential evolution (OBDE) to enhance the navigation accuracy. The experimental results demonstrate that our modified algorithms to evolutionarily optimize electromagnetic sensor measurements can critically reduce the tracking error from 4.8 to 2.9 mm. In particular, OBDE outperforms APSO for electromagnetic endoscopic navigation.

Original languageEnglish
Article number8764026
Pages (from-to)10859-10868
Number of pages10
JournalIEEE Sensors Journal
Volume19
Issue number22
DOIs
Publication statusPublished - 15 Nov 2019
Externally publishedYes

Keywords

  • differential evolution
  • Electromagnetic sensor
  • evolutionary computation
  • image-guided intervention
  • particle swarm optimization
  • surgical tracking and navigation

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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