Sub-micrometer, periodic motion detection using blind source separation (BSS) via principal component analysis (PCA) is presented in the context of magnetomotive ultrasound (MMUS) imaging and Shearwave Dispersion Ultrasound Vibrometry (SDUV). In MMUS, an oscillating external magnetic field displaces tissue loaded with superparamagnetic iron oxide (SPIO) particles, whereas in SDUV, periodic tissue motion is induced using acoustic radiation force (ARF) to measure visco-elastic properties. BSS motion detection performance in MMUS imaging and SDUV was compared against frequency-phase locked (FPL) and normalized cross-correlation (NCC) motion detectors, respectively, in silico and in experimental phantoms. Parametric MMUS phantom images constructed using the BSS method had nearly twice the SNR of the corresponding images constructed using FPL method when a 0.043 mm or smaller kernel size was used. In FEM models of SDUV, the error in the BSS-estimated viscoelastic properties of simulated materials was < 10%, whereas the error was > 20% using NCC when the simulated SNR was 15 dB. In a calibrated elasticity phantom, the amplitude of the motion was ≤ 0.5 μm for a scanner power level ≤ 20%. The median percent error in BSS-derived shear modulus of the phantom was -6.8%, -1.55%, -17.11% for power level of 20%, 15%, and 10%, respectively. The corresponding NCC-derived errors were 29.90%, 127.1%, and 244.70%. These results suggest the relevance of using BSS for the detection of sub-micrometer, periodic motion in MMUS and SDUV imaging, particularly when SNR is less than 15 dB and/or induced displacements are less than 0.5 μm.
Keywords: Blind Source Separation (BSS); MMUS Imaging; Motion Detection; SDUV.