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Breast cancer is the most common malignancy in women. Mammographic findingssuch as microcalcifications and masses, as well as morphologic features ofmasses in sonographic scans, are the main diagnostic targets for tumordetection. However, improved specificity of these imaging modalities isrequired. A leading alternative target is neoangiogenesis. When pathological,it contributes to the development of numerous types of tumors, and theformation of metastases. Hence, demonstrating neoangiogenesis by visualizationof the microvasculature may be of great importance. Super resolution ultrasoundlocalization microscopy enables imaging of the microvasculature at thecapillary level. Yet, challenges such as long reconstruction time, dependencyon prior knowledge of the system Point Spread Function (PSF), and separabilityof the Ultrasound Contrast Agents (UCAs), need to be addressed for translationof super-resolution US into the clinic. In this work we use a deep neuralnetwork architecture that makes effective use of signal structure to addressthese challenges. We present in vivo human results of three different breastlesions acquired with a clinical US scanner. By leveraging our trained network,the microvasculature structure is recovered in a short time, without prior PSFknowledge, and without requiring separability of the UCAs. Each of therecoveries exhibits a different structure that corresponds with the knownhistological structure. This study demonstrates the feasibility of in vivohuman super resolution, based on a clinical scanner, to increase US specificityfor different breast lesions and promotes the use of US in the diagnosis ofbreast pathologies.

Learned super resolution ultrasound for improved breast lesion characterization