Medical professionals rely on ultrasound imagery for a variety of key diagnostics—from fetal health to cardiac function—so the images they work with must be perfectly clear. Clinical scanners generate raw data that go through advanced post-processing to enhance contrast and reduce electronic noise and speckle, which occurs when sound waves interfere with each other. That post-processing transforms the “beamformed” image into a drastically improved “clinical-grade” image that medical providers can interpret.
Yet clinical post-processing is typically proprietary and varies across manufacturers, making it difficult to establish current imaging baselines. In turn, this challenges clinical translation for medical researchers spanning fields from hardware to algorithmic development. Ouwen Huang, M.D./Ph.D. candidate at Duke University’s Biomedical Engineering program, wanted to solve that problem by designing a universal open-source framework that researchers anywhere could use to process raw data from any scanner to match images from a clinical-grade scanner.