Multilevel and Motion Model-Based Ultrasonic Speckle Tracking Algorithms

• Published in: Ultrasound in medicine & biology Vol. 24; no. 3; pp. 427 - 441
• Main Authors: Yeung, Fai, Levinson, Stephen F, Parker, Kevin J
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.03.1998; Elsevier
• Subjects: Acoustics; Algorithms; Biological and medical sciences; Biomechanics; Computer Simulation; Doppler ultrasound; Elasticity; Forearm; Humans; Image processing; Image Processing, Computer-Assisted; Investigative techniques, diagnostic techniques (general aspects); Mathematical models; Medical sciences; Miscellaneous. Technology; Models, Biological; Motion compensation; Motion estimation; Muscle; Muscle Contraction - physiology; Muscle, Skeletal - diagnostic imaging; Muscle, Skeletal - physiology; Muscles; Phantoms, Imaging; Reproducibility of Results; Signal to noise ratio; Speckle; Speckle tracking; Tissue; Tracking (position); Ultrasonic investigative techniques; Ultrasonics; Ultrasonography, Doppler; Biomechanics; Motion estimation; Image processing; Computer simulation; Elasticity; Muscles; Acoustics; Speckle tracking; Doppler ultrasound; Ultrasonics; Sonography; Acoustic parameter; Motion study; Speckle; Muscle; Algorithm; Test object
• ISSN: 0301-5629; 1879-291X; 1879-291X
• DOI: 10.1016/S0301-5629(97)00281-0

A multilevel motion model-based approach to ultrasonic speckle tracking has been developed that addresses the inherent trade-offs associated with traditional single-level block matching (SLBM) methods. The multilevel block matching (MLBM) algorithm uses variable matching block and search window sizes in a coarse-to-fine scheme, preserving the relative immunity to noise associated with the use of a large matching block while preserving the motion field detail associated with the use of a small matching block. To decrease further the sensitivity of the multilevel approach to noise, speckle decorrelation and false matches, a smooth motion model-based block matching (SMBM) algorithm has been implemented that takes into account the spatial inertia of soft tissue elements. The new algorithms were compared to SLBM through a series of experiments involving manual translation of soft tissue phantoms, motion field computer simulations of rotation, compression and shear deformation, and an experiment involving contraction of human forearm muscles. Measures of tracking accuracy included mean squared tracking error, peak signal-to-noise ratio (PSNR) and blinded observations of optical flow. Measures of tracking efficiency included the number of sum squared difference calculations and the computation time. In the phantom translation experiments, the SMBM algorithm successfully matched the accuracy of SLBM using both large and small matching blocks while significantly reducing the number of computations and computation time when a large matching block was used. For the computer simulations, SMBM yielded better tracking accuracies and spatial resolution when compared with SLBM using a large matching block. For the muscle experiment, SMBM outperformed SLBM both in terms of PSNR and observations of optical flow. We believe that the smooth motion model-based MLBM approach represents a meaningful development in ultrasonic soft tissue motion measurement.