Oren Solomon

Fast Vascular Ultrasound Imaging With Enhanced Spatial Resolution and Background Rejection

Ultrasound super-localization microscopy techniques presented in the last few years enable non-invasive imaging of vascular structures at the capillary level by tracking the flow of ultrasound contrast agents (gas microbubbles). However, these techniques are currently limited by low temporal resolution and long acquisition times. Super-resolution optical fluctuation imaging (SOFI) is a fluorescence microscopy technique enabling sub-diffraction limit imaging with high temporal resolution by calculating high order statistics of the fluctuating optical signal. The aim of this work is to achieve fast acoustic imaging with enhanced resolution by applying the tools used in SOFI to contrast-enhance ultrasound (CEUS) plane-wave scans. The proposed method was tested using numerical simulations and evaluated using two in-vivo rabbit models: scans of healthy kidneys and VX-2 tumor xenografts. Improved spatial resolution was observed with a reduction of up to 50% in the full width half max of the point spread function. In addition, substantial reduction in the background level was achieved compared to standard mean amplitude persistence images, revealing small vascular structures within tumors. The scan duration of the proposed method is less than a second while current super-localization techniques require acquisition duration of several minutes. As a result, the proposed technique may be used to obtain scans with enhanced spatial resolution and high temporal resolution, facilitating flow-dynamics monitoring. Our method can also be applied during a breath-hold, reducing the sensitivity to motion artifacts.

Photoacoustic imaging beyond the acoustic diffraction-limit with dynamic speckle illumination and sparse joint support recovery

In deep tissue photoacoustic imaging the spatial resolution is inherently limited by the acoustic wavelength. Recently, it was demonstrated that it is possible to surpass the acoustic diffraction limit by analyzing fluctuations in a set of photoacoustic images obtained under unknown speckle illumination patterns. Here, we purpose an approach to boost reconstruction fidelity and resolution, while reducing the number of acquired images by utilizing a compressed sensing computational reconstruction framework. The approach takes into account prior knowledge of the system response and sparsity of the target structure. We provide proof of principle experiments of the approach and demonstrate that improved performance is obtained when both speckle fluctuations and object priors are used. We numerically study the expected performance as a function of the measurements signal to noise ratio and sample spatial-sparsity. The presented reconstruction framework can be applied to analyze existing photoacoustic experimental data sets containing dynamic fluctuations.

Sparsity-based super-resolution optical fluctuation imaging

We present a new imaging technique optimizing the spatio-temporal resolution in fluorescence microscopy. This method achieves short integration time as SOFI, with high spatial resolution comparable to STORM, leading towards super-resolution imaging within living cells.

Sparsity based super-resolution optical imaging using correlation information

Traditionally, spatial resolution in optical imaging is limited by diffraction. Although sub-wavelength information is absent in the measurements, state-of-the-art fluorescence based localization techniques such as PALM and STORM manage to achieve spatial resolution of tens of nano-meters, but with limited temporal resolution. A more recent technique super-resolution optical fluctuation imaging (SOFI) exploits the temporal statistical behavior of uncorrelated fluorescence emissions to practically improve the spatial resolution by a factor of two over the diffraction limit, but with considerably faster image capturing. Here we propose to exploit the sparse nature of the fluorophores distribution, combined with a statistical prior of uncorrelated emissions such as in SOFI to achieve spatial resolution comparable to PALM/STORM, while retaining the temporal resolution of SOFI. We demonstrate our method on simulations and show improved results over STORM and SOFI. Our method may facilitate super-resolution imaging and capturing of intra-cellular dynamics within living cells.

Sparsity-driven super-localization in clinical contrast-enhanced ultrasound

Super-resolution (SR) ultrasound enables detailed assessment of the fine vascular network by pinpointing individual microbubbles (MBs), using ultrasound contrast agents (UCAs). The information in SR images is determined by the density of localized MBs and their localization accuracy. To obtain high densities, one can evaluate extremely sparse subsets of MBs across thousands of frames by using a very low MB dose and imaging for a very long time, which is impractical for clinical routine. While ultrafast imaging somewhat alleviates this problem, long acquisition times are still required to enhance the full vascular bed. As a result, localization accuracy remains hampered by patient motion. Recently, Sparsity-based Ultrasonic Super resolution Hemodynamic Imaging (SUSHI) achieved comparable spatial resolution with a sub-second temporal resolution. However, in the current implementation of SUSHI this temporal resolution was achieved using very high frame-rate, e.g. plane-wave imaging, which is not currently widely available in clinical scanners. The aim of this work is twofold. First, to attain a high MB localization accuracy on dense contrast-enhanced ultrasound (CEUS) data using a clinical dose of UCA and a widespread clinical scanner. Second, to retain a high resolution by motion compensation.

Fast and background free super-resolution ultrasound angiography

Spatial resolution in classic contrast-enhanced ultrasound (CEUS) is limited by acoustic diffraction. Ultrafast ultrasound localization microscopy (uULM) has enabled a sub-diffraction spatial resolution of tens of micrometers in-vivo, although at the expense of a scan duration of tens of seconds. In contrast, by exploiting the statistical properties of CEUS, super resolution optical fluctuation imaging (SOFI) recently demonstrated temporal resolution of 90ms in-vivo with a spatial resolution two times better than the diffraction limit. Here, we report on a method which achieves a spatial resolution similar to that of uULM, but with temporal resolution of CEUS SOFI, termed sparsity based ultrasonic super resolution hemodynamic imaging (SUSHI).