  Breast cancer is the most common malignancy in women. Mammographic findings
such as microcalcifications and masses, as well as morphologic features of
masses in sonographic scans, are the main diagnostic targets for tumor
detection. However, improved specificity of these imaging modalities is
required. A leading alternative target is neoangiogenesis. When pathological,
it contributes to the development of numerous types of tumors, and the
formation of metastases. Hence, demonstrating neoangiogenesis by visualization
of the microvasculature may be of great importance. Super resolution ultrasound
localization microscopy enables imaging of the microvasculature at the
capillary level. Yet, challenges such as long reconstruction time, dependency
on prior knowledge of the system Point Spread Function (PSF), and separability
of the Ultrasound Contrast Agents (UCAs), need to be addressed for translation
of super-resolution US into the clinic. In this work we use a deep neural
network architecture that makes effective use of signal structure to address
these challenges. We present in vivo human results of three different breast
lesions acquired with a clinical US scanner. By leveraging our trained network,
the microvasculature structure is recovered in a short time, without prior PSF
knowledge, and without requiring separability of the UCAs. Each of the
recoveries exhibits a different structure that corresponds with the known
histological structure. This study demonstrates the feasibility of in vivo
human super resolution, based on a clinical scanner, to increase US specificity
for different breast lesions and promotes the use of US in the diagnosis of
breast pathologies.
