MUlti-Dimensional Spline-Based Estimator (MUSE) for Motion Estimation: Algorithm Development and Initial Results

• Published in: Annals of biomedical engineering Vol. 36; no. 12; pp. 1942 - 1960
• Main Authors: Viola, Francesco, Coe, Ryan L., Owen, Kevin, Guenther, Drake A., Walker, William F.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01-12-2008; Springer Nature B.V
• Subjects: Algorithms; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Computer Simulation; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Light microscopy; Models, Statistical; Motion; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Regression Analysis; Reproducibility of Results; Signal Processing, Computer-Assisted; Standard deviation; Ultrasonics; Strain estimation; Elasticity imaging; Image registration; Motion estimation; Splines
• ISSN: 0090-6964; 1573-9686; 1521-6047
• DOI: 10.1007/s10439-008-9564-2

Image registration and motion estimation play central roles in many fields, including RADAR, SONAR, light microscopy, and medical imaging. Because of its central significance, estimator accuracy, precision, and computational cost are of critical importance. We have previously presented a highly accurate, spline-based time delay estimator that directly determines sub-sample time delay estimates from sampled data. The algorithm uses cubic splines to produce a continuous representation of a reference signal and then computes an analytical matching function between this reference and a delayed signal. The location of the minima of this function yields estimates of the time delay. In this paper we describe the MUlti-dimensional Spline-based Estimator (MUSE) that allows accurate and precise estimation of multi-dimensional displacements/strain components from multi-dimensional data sets. We describe the mathematical formulation for two- and three-dimensional motion/strain estimation and present simulation results to assess the intrinsic bias and standard deviation of this algorithm and compare it to currently available multi-dimensional estimators. In 1000 noise-free simulations of ultrasound data we found that 2D MUSE exhibits maximum bias of 2.6 × 10
−4
samples in range and 2.2 × 10
−3
samples in azimuth (corresponding to 4.8 and 297 nm, respectively). The maximum simulated standard deviation of estimates in both dimensions was comparable at roughly 2.8 × 10
−3
samples (corresponding to 54 nm axially and 378 nm laterally). These results are between two and three orders of magnitude better than currently used 2D tracking methods. Simulation of performance in 3D yielded similar results to those observed in 2D. We also present experimental results obtained using 2D MUSE on data acquired by an Ultrasonix Sonix RP imaging system with an L14-5/38 linear array transducer operating at 6.6 MHz. While our validation of the algorithm was performed using ultrasound data, MUSE is broadly applicable across imaging applications.