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

Abstract

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 ( -off) from on ( -on) data. Shortcomings to this approach include long tracking ensembles and the requirement for -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 -off data. MMUS imaging of two phantoms containing an SPIO-laden cubical inclusion and one control phantom was performed using an open-air 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 -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 -off data may be misaligned with -on due to physiological motion.

External Power-Driven Microrobotic Swarm: From Fundamental Understanding to Imaging-Guided Delivery

“…A dynamic magnetic field is used to excite the magnetic agents inside the imaging region, and an ultrasound probe scans the region at the same time. The moving agents cause the phase shift of the backscattered ultrasound waves, and the acoustic signal is collected by the probe and then analyzed by algorithms . MMUS imaging of a swarm of magnetic nanoparticles has been investigated .…”

“…The moving agents cause the phase shift of the backscattered ultrasound waves, and the acoustic signal is collected by the probe and then analyzed by algorithms. 142 MMUS imaging of a swarm of magnetic nanoparticles has been investigated. 133 The comparison of imaging results among B-mode imaging, common MMUS, and inverse MMUS in a tissue-mimicking phantom is presented (Figure 7f).…”

Inversion of displacement fields to quantify the magnetic particle distribution in homogeneous elastic media from magnetomotive ultrasound

“…Further, the inversion scheme presented in this work may be executed with other implementations of motion detection algorithms and hardware in the future. Inversion can be applied to displacement fields generated through Doppler (Oh et al 2006), cross-correlation (Mehrmohammadi et al 2007), and blind source separation-based FPL (Hossain et al 2018) methods, among others. Inversion can also be applied to systems where varying waveforms for temporal modulation of the magnetic gradient force are used, including pulsed (Mehrmohammadi et al 2009) or coded (Fink et al 2017), to the extent that the rendered image signals are linear with respect to the displacement field.…”

Magnetic particles in motion: magneto-motive imaging and sensing

“…Sharp, fast magneto-motive motion can be separated from gradual, slow tissue motion by analyzing frequency content. Developing more accurate and sensitive motion detection algorithms, such as frequency or phase locking and blind-source separation, can enhance the sensitivity of magneto-motive imaging and reduce background noise 79 , 168 .…”