Technical Note: Automatic real‐time ultrasound tracking of respiratory signal using selective filtering and dynamic template matching. Issue 8 (7th July 2015)

• Record Type: Journal Article
• Authors: Wu, Jiaze; Li, Cheng; Gogna, Apoorva; Tan, Bien Soo; Ooi, London Lucien; Liu, Jimin; Yu, Haoyong
• Is Part Of: Medical physics. Volume 42:Issue 8(2015)Part 1
• Page Start: 4536
• Page End: 4541
• Publication Date: 2015-07-07
• Subjects: biomedical ultrasonics -- edge detection -- expectation‐maximisation algorithm -- filtering theory -- Hessian matrices -- image enhancement -- image matching -- liver -- medical image processing -- pneumodynamics; Ultrasonographic imaging -- Pneumodyamics, respiration -- Edge enhancement -- Matrix theory -- Numerical approximation and analysis; Diagnosis using ultrasonic, sonic or infrasonic waves -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; liver ultrasound imaging -- respiratory signal tracking -- selective filtering -- dynamic template matching; Liver -- Ultrasonography -- Digital tomosynthesis mammography -- Tracking devices -- Motion estimation -- Optical inspection -- Transducers -- Image sensors; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4924803
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: 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.