Ultrasonic tracking of shear waves using a particle filter. Issue 11 (29th October 2015)

• Record Type: Journal Article
• Authors: Ingle, Atul N.; Ma, Chi; Varghese, Tomy
• Is Part Of: Medical physics. Volume 42:Issue 11(2015)
• Page Start: 6711
• Page End: 6724
• Publication Date: 2015-10-29
• Subjects: biomedical ultrasonics -- hidden Markov models -- least squares approximations -- medical image processing -- tracking -- ultrasonic imaging; Ultrasonographic imaging -- Noise; 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; ultrasound -- shear wave elastography -- electrode vibration elastrography -- particle filter; Ultrasonography -- Medical image noise -- Tissue ablation -- Acoustic waves -- Ultrasonic velocity -- Finite element methods -- Elastic moduli -- Cancer -- Markov processes; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4934372
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: This paper discusses an application of particle filtering for estimating shear wave velocity in tissue using ultrasound elastography data. Shear wave velocity estimates are of significant clinical value as they help differentiate stiffer areas from softer areas which is an indicator of potential pathology. Methods: Radio‐frequency ultrasound echo signals are used for tracking axial displacements and obtaining the time‐to‐peak displacement at different lateral locations. These time‐to‐peak data are usually very noisy and cannot be used directly for computing velocity. In this paper, the denoising problem is tackled using a hidden Markov model with the hidden states being the unknown (noiseless) time‐to‐peak values. A particle filter is then used for smoothing out the time‐to‐peak curve to obtain a fit that is optimal in a minimum mean squared error sense. Results: Simulation results from synthetic data and finite element modeling suggest that the particle filter provides lower mean squared reconstruction error with smaller variance as compared to standard filtering methods, while preserving sharp boundary detail. Results from phantom experiments show that the shear wave velocity estimates in the stiff regions of the phantoms were within 20% of those obtained from a commercial ultrasound scanner and agree with estimates obtained using a standard method using least‐squares fit. Estimates of area obtained from the particle filtered shear wave velocity maps were within 10% of those obtained from B‐mode ultrasound images. Conclusions: The particle filtering approach can be used for producing visually appealing SWV reconstructions by effectively delineating various areas of the phantom with good image quality properties comparable to existing techniques.