Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization.

Super-resolution imaging methods promote tissue characterization beyond the spatial resolution limits of the devices and bridge the gap between histopathological analysis and non-invasive imaging. Here, we introduce motion model ultrasound localization microscopy (mULM) as an easily applicable and robust new tool to morphologically and functionally characterize fine vascular networks in tumors at super-resolution. In tumor-bearing mice and for the first time in patients, we demonstrate that within less than 1 min scan time mULM can be realized using conventional preclinical and clinical ultrasound devices. In this context, next to highly detailed images of tumor microvascularization and the reliable quantification of relative blood volume and perfusion, mULM provides multiple new functional and morphological parameters that discriminate tumors with different vascular phenotypes. Furthermore, our initial patient data indicate that mULM can be applied in a clinical ultrasound setting opening avenues for the multiparametric characterization of tumors and the assessment of therapy response.

Detection and Tracking of Multiple Microbubbles in Ultrasound B-Mode Images.

The imaging of microvessels and the quantification of their blood flow is of particular interest in the characterization of tumor vasculature. The imaging resolution (50-200 µm) of high-frequency ultrasound (US) (20-50 MHz) is not sufficient to image microvessels (~10 µm) and Doppler sensitivity is not high enough to measure capillary blood flow (~1 mm/s). For imaging of blood flow in microvessels, our approach is to detect single microbubbles (MBs), track them over several frames, and to estimate their velocity. First, positions of MBs will be detected by separating B-mode frames in a moving foreground and a static background. For the crucial task of association of these positions to tracks, we implemented a modified Markov chain Monte Carlo data association (MCMCDA) algorithm, which can handle a high number of MBs. False alarms, the detection, initiation, and termination of MBs tracks are incorporated in the underlying model. To test the performance of algorithms, a US imaging simulation of a vessel tree with flowing MBs was set up (resolution 148 µm). The trajectories and flow velocity in the vessels with a lateral distance of 100 µm were reconstructed with super-resolution. In a phantom experiment, a suspension of MBs was pumped through a tube (diameter 0.4 mm) at speeds of 2.2, 4.2, 6.3, and 10.5 mm/s and was imaged with a Vevo2100 system (Visualsonics). Estimated mean speeds of the MBs were 2.1, 4.7, 7, and 10.5 mm/s. To demonstrate the applicability for in vivo measurements, a tumor xenograft-bearing mouse was imaged by this approach. The tumor vasculature was visualized with higher resolution than in a maximum intensity persistence image and the velocity values were in the expected range 0-1 mm/s.