Blind Source Separation-Based Motion Detector for Imaging Super-Paramagnetic Iron Oxide (SPIO) Particles in Magnetomotive Ultrasound Imaging

In magnetomotive ultrasound (MMUS) imaging, an oscillating external magnetic field displaces tissue loaded with super-paramagnetic iron oxide (SPIO) particles. The induced motion is on the nanometer scale, which makes its detection and its isolation from background motion challenging. Previously, a frequency and phase locking (FPL) algorithm was used to suppress background motion by subtracting magnetic field off (B-off) from on (B-on) data. Shortcomings to this approach include long tracking ensembles and the requirement for B-off data. In this paper, a novel blind source separation-based FPL (BSS-FPL) algorithm is presented for detecting motion using a shorter ensemble length (EL) than FPL and without B-off data. MMUS imaging of two phantoms containing an SPIO-laden cubical inclusion and one control phantom was performed using an openair MMUS system. When background subtraction was used, contrast and contrast to noise ratio (CNR) were, respectively, 1.20±0.20 and 1.56±0.34 times higher in BSS-FPL as compared to FPL-derived images for EL