title: Technical Note: Automatic real-time ultrasound tracking of respiratory signal using selective filtering and dynamic template matching.
abstract: OBJECTIVE
  In respiratory motion modeling for liver interventions, the respiratory signal is usually obtained by using special tracking devices to monitor external skin. However, due to intrinsic limits and cost consideration of these tracking devices, a purely ultrasound image-based approach to tracking the signal is a more feasible option.
  
  
  METHODS
  In this study, a novel image-based method is proposed to obtain the respiratory signal directly from 2D ultrasound images by automatically identifying and tracking the liver boundary. The boundary identification is a multistage process, which is the key to utilize a Hessian matrix-based 2D filter to enhance the line-like liver boundary and weaken other liver tissues. For tracking the identified boundary, a new dynamic template matching technique is first applied to estimate 2D displacements, and a boundary-specific selection mechanism is then introduced to extract the respiratory signal from the 2D displacements.
  
  
  RESULTS
  The experiments demonstrate that their method can obtain accurate breathing signals, which are in key phases comparable to the manually annotation and highly consistent to the electromagnetic-tracked ground-truth signals (average correlation coefficients 0.9209 and statistically significant p < 0.01). Meanwhile, the experiments also prove their method can achieve high real-time performance of about 80-160 Hz.
  
  
  CONCLUSIONS
  This method provides a good alternative to traditional external-landmark-based tracking methods and may be widely applied for respiratory compensation in ultrasound-guided liver interventions.
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore 117575.
authors.forename: Jiaze
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore 117575.
authors.forename: Cheng
authors.initials: C
authors.lastname: Li
authors.affiliation: Singapore General Hospital, Singapore 169608.
authors.forename: Apoorva
authors.initials: A
authors.lastname: Gogna
authors.affiliation: Singapore General Hospital, Singapore 169608.
authors.forename: Bien Soo
authors.initials: BS
authors.lastname: Tan
authors.affiliation: Singapore General Hospital, Singapore 169608.
authors.forename: London Lucien
authors.initials: LL
authors.lastname: Ooi
authors.affiliation: Hexalotus Technology Pte Ltd, Singapore 349247.
authors.forename: Jimin
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore 117575.
authors.forename: Haoyong
authors.initials: H
authors.lastname: Yu
mesh_terms: D005240:Feasibility Studies; D008099:Liver / Q000000981:diagnostic imaging / Q000601:surgery; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D012119:Respiration; D025321:Surgery, Computer-Assisted / Q000379:methods; D014463:Ultrasonography / Q000295:instrumentation / Q000379:methods*