Direct ultrasonic imaging of arterial and venous thrombi could aid in diagnosis and treatment planning by providing rapid and cost-effective measurements of thrombus volume and elastic modulus. Toward this end, it was demonstrated that open-air magnetomotive ultrasound (MMUS) provides specific contrast to superparamagnetic iron oxide-labeled model thrombi embedded in gelatin-based blood vessel-mimicking flow phantoms. MMUS was performed on model thrombi in the presence of pulsatile flow that mimics cardiac-induced motion found in real vasculature. The MMUS signal and contrast-to-noise ratio (CNR) were measured across a range of physiologically relevant thrombus volumes and elastic moduli. Model thrombus volumes as small as 0.5 ml were shown to be detectable (CNR > 1) over the entire range of elastic moduli tested (3.5-40 kPa). It was also found that MMUS signal and CNR are increased with increasing thrombus volume ( ) and decreasing elastic modulus ( ), while variations in pulsatile flow rate had little effect. These findings demonstrate that MMUS has promise as a direct in vivo thrombosis imaging modality for quantifying thrombus volume and stiffness.

Optical coherence tomography (OCT) has gained widespread application for many biomedical applications, yet the traditional array of contrast agents used in incoherent imaging modalities do not provide contrast in OCT. Owing to the high biocompatibility of iron oxides and noble metals, magnetic and plasmonic nanoparticles, respectively, have been developed as OCT contrast agents to enable a range of biological and pre-clinical studies. Here we provide a review of these developments within the past decade, including an overview of the physical contrast mechanisms and classes of OCT system hardware addons needed for magnetic and plasmonic nanoparticle contrast. A comparison of the wide variety of nanoparticle systems is also presented, where the figures of merit depend strongly upon the choice of biological application.

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.

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