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Bimodal Tactile Tomography with Bayesian Sequential Palpation for Intracavitary Microstructure Profiling and

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This study introduces a novel robotic palpation technique using ElastoSight for real-time tissue evaluation. The method precisely locates and segments lesions, enhancing disease diagnosis in minimally invasive surgery.

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Area of Science:

  • Medical Robotics
  • Surgical Technology
  • Biomedical Engineering

Background:

  • Robotic palpation is vital for in situ tissue biomechanical evaluation in diagnosing diseases, particularly in luminal organs.
  • Current surgical robotic systems lack tactile intelligence for comprehensive tissue assessment, hindering abnormality detection.
  • Optical coherence tomography offers 3D visualization but lacks tactile sensing for lesion profiling.

Purpose of the Study:

  • To develop a robotic bimodal palpation technique for precise, real-time tissue biomechanical evaluation.
  • To integrate optical coherence tomography-based tactile sensing for enhanced lesion detection and boundary identification.
  • To improve abnormality detection and surgical precision in robot-assisted minimally invasive procedures.

Main Methods:

  • Development of a robotic bimodal palpation technique utilizing the ElastoSight optical coherence tomography-based tactile sensor.
  • Implementation of circumferential and sliding B-scan modes combined with Bayesian optimization.
  • Validation using tumor phantom models for lesion localization and segmentation accuracy.

Main Results:

  • Achieved tumor localization within 30 iterations, with F1 scores > 0.976 and centroid error < 0.032 mm in phantom models.
  • Demonstrated accurate segmentation of hard tissue inclusions with a precision rate of 0.983 and area error < 0.25 mm² using sliding B-scan mode.
  • The technique effectively addressed real-time lesion localization and segmentation challenges in simulations and experiments.

Conclusions:

  • The proposed robotic bimodal palpation technique significantly enhances real-time lesion localization and segmentation capabilities.
  • This approach has the potential to improve the precision and efficiency of abnormality detection in robot-assisted minimally invasive surgery.
  • The ElastoSight sensor integrated into this technique shows promise for advancing tissue assessment during procedures like tumor removal.