Compressed sensing-based super-resolution ultrasound imaging for faster acquisition and high quality images

Abstract

Super-resolution ultrasound (SRUS) imaging technique has overcome the diffraction limit of conventional ultrasound imaging, resulting in an improved spatial resolution while preserving imaging depth. Typical SRUS images are reconstructed by localizing ultrasound microbubbles (MBs) injected in a vessel using normalized 2-dimensional cross-correlation (2DCC) between MBs signals and the point spread function of the system. However, current techniques require isolated MBs in a confined area due to inaccurate localization of densely populated MBs. To overcome this limitation, we developed the {ell}1-homotopy based compressed sensing (L1H-CS) based SRUS imaging technique which localizes densely populated MBs to visualize microvasculature in vivo. To evaluate the performance of L1H-CS, we compared the performance of 2DCC, interior-point method based compressed sensing (CVX-CS), and L1H-CS algorithms. Localization efficiency was compared using axially and laterally aligned point targets (PTs) with known distances and randomly distributed PTs generated by simulation. We developed post-processing techniques including clutter reduction, noise equalization, motion compensation, and spatiotemporal noise filtering for in vivo imaging. We then validated the capabilities of L1H-CS based SRUS imaging technique with high-density MBs in a mouse tumor model, kidney, and zebrafish dorsal trunk, and brain. Compared to 2DCC, and CVX-CS algorithm, L1H-CS algorithm, considerable improvement in SRUS image quality and data acquisition time was achieved. These results demonstrate that the L1H-CS based SRUS imaging technique has the potential to examine the microvasculature with reduced acquisition and reconstruction time of SRUS image with enhanced image quality, which may be necessary to translate it into the clinics.