Automatic Normal Positioning of Robotic Ultrasound Probe Based only on Confidence Map Optimization and Force Measurement

Zhongliang Jiang, Matthias Grimm, Mingchuan Zhou, Javier Esteban, Walter Simson, Guillaume Zahnd, Nassir Navab

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


Acquiring good image quality is one of the main challenges for fully-automatic robot-assisted ultrasound systems (RUSS). The presented method aims at overcoming this challenge for orthopaedic applications by optimizing the orientation of the robotic ultrasound (US) probe, i.e. aligning the central axis of the US probe to the tissue's surface normal at the point of contact in order to improve sound propagation within the tissue. We first optimize the in-plane orientation of the probe by analyzing the confidence map of the US image. We then carry out a fan motion and analyze the resulting forces estimated from joint torques to align the central axis of the probe to the normal within the plane orthogonal to the initial image plane. This results in the final 3D alignment of the probe's main axis with the normal to the anatomical surface at the point of contact without using external sensors for surface reconstruction or localizing the point of contact in an anatomical atlas. The algorithm is evaluated both on a phantom and on human tissues (forearm, upper arm and lower back). The mean absolute angular difference (±STD) between true and estimated normal on stationary phantom, forearm, upper arm and lower back was 3.1 ± 1.0°, 3.7 ± 1.7°, 5.3 ± 1.3° and 6.9 ± 3.5°, respectively. In comparison, six human operators obtained errors of 3.2 ± 1.7° on the phantom. Hence the method is able to automatically position the probe normal to the scanned tissue at the point of contact and thus improve the quality of automatically acquired ultrasound images.

Original languageEnglish
Article number8963620
Pages (from-to)1342-1349
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2020


  • Medical robots and systems
  • force and tactile sensing
  • robotic ultrasound


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