  Image post-processing is used in clinical-grade ultrasound scanners to
improve image quality (e.g., reduce speckle noise and enhance contrast). These
post-processing techniques vary across manufacturers and are generally kept
proprietary, which presents a challenge for researchers looking to match
current clinical-grade workflows. We introduce a deep learning framework,
MimickNet, that transforms raw conventional delay-and-summed (DAS) beams into
the approximate post-processed images found on clinical-grade scanners.
Training MimickNet only requires post-processed image samples from a scanner of
interest without the need for explicit pairing to raw DAS data. This
flexibility allows it to hypothetically approximate any manufacturer's
post-processing without access to the pre-processed data. MimickNet generates
images with an average similarity index measurement (SSIM) of 0.930$\pm$0.0892
on a 300 cineloop test set, and it generalizes to cardiac cineloops outside of
our train-test distribution achieving an SSIM of 0.967$\pm$0.002. We also
explore the theoretical SSIM achievable by evaluating MimickNet performance
when trained under gray-box constraints (i.e., when both pre-processed and
post-processed images are available). To our knowledge, this is the first work
to establish deep learning models that closely approximate current
clinical-grade ultrasound post-processing under realistic black-box constraints
where before and after post-processing data is unavailable. MimickNet serves as
a clinical post-processing baseline for future works in ultrasound image
formation to compare against. To this end, we have made the MimickNet software
open source.
