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 <; 3.5 s. However, contrast and CNR were similar for BSS-FPL and FPL for EL ≥ 3.5 s. When only B-on data was used, contrast and CNR were 1.94 ± 0.21 and 1.56 ± 0.28 times higher, respectively, in BSS-FPL as compared to FPL-derived images for all ELs. Percent error in the estimated width and height was 39.30% ± 19.98% and 110.37% ± 6.5% for FPL and was 7.30% ± 7.6% and 16.21% ± 10.29% for BSS-FPL algorithm. This paper is an important step toward translating MMUS imaging to in vivo application, where long tracking ensembles would increase acquisition time and B-off data may be misaligned with B-on due to physiological motion.