The resolution of diffractive imaging devices, whether optical or acoustic, is fundamentally limited, as first established by Ernst Karl Abbe almost 150 years ago. This resolution limit poses a stringent constraint on the ability to resolve sub-diffraction features. In microscopy, organelles smaller than 200nm cannot be resolved, while in medical ultrasound arterioles and venules range between 10µm to a few tens of microns, an order of magnitude below the acoustic diffraction limit of clinical ultrasound scanners.

The 2014 Nobel prize in chemistry was awarded for methods which proved that although the diffraction limit poses a physical limitation, it can nonetheless be circumvented by altering the conventional measurement process in fluorescence microscopy. These methods, called photo-activated localization microscopy, or PALM (also notable due to algorithmic similarity is stochastic optical reconstruction microscopy, or STORM) and stimulated emission depletion (STED) microscopy have proven that optical imaging systems can surpass the optical diffraction limit by an order of magnitude. Drawing inspiration from microscopy, similar methods were applied to ultrasound imaging, achieving a precise mapping of sub-diffraction vascular networks deep within the tissue. However, although super-resolution techniques demonstrated unprecedented resolving power beyond the limit of diffraction, they lack in temporal resolution, as a single super-resolved image requires tens of thousands of diffraction limited exposures. Thus, the ability to image dynamic processes in sub-diffraction resolution is severely limited in these techniques.
In this work, we present methods for fast super-resolution by increasing fluorophore density and exploiting structural and statistical priors of the acquired signal. First, we demonstrate that by exploiting sparsity in the correlation domain, fluorescence microscopy can achieve sub-diffraction imaging with resolution comparable to state of-the-art, with two orders of magnitude less frames. Next, we present how similar ideas can be extended to contrast enhanced ultrasound to achieve time-lapse imaging of super-resolved hemodynamic changes. Moreover, we show how in ultrasound it is
possible to further exploit the inherent motion of contrast agents to achieve Doppler processing in sub-diffraction resolution on one hand, and on the other, how blood flow can be used as a structural prior for super-resolution. Finally, we apply recent developments in the field of deep learning to ultrasound imaging to achieve super-resolution, and to suppress tissue clutter signal for better visualization of blood vessels.

By exploiting all available priors of the acquired signal and algorithmic tools, super-resolution can be pushed further to include both high temporal resolution as well as sub-diffraction spatial resolution.