  Objective: The transmit encoding model for synthetic aperture imaging is a
robust and flexible framework for understanding the effects of acoustic
transmission on ultrasound image reconstruction. Our objective is to use
machine learning (ML) to construct scanning sequences, parameterized by time
delays and apodization weights, that produce high-quality B-mode images.
Approach: We use a custom ML model in PyTorch with simulated RF data from Field
II to probe the space of possible encoding sequences for those that minimize a
loss function that describes image quality. This approach is made
computationally feasible by a novel formulation of the derivative for
delay-and-sum beamforming. Main Results: When trained for a specified
experimental setting (imaging domain, hardware restrictions, etc.), our ML
model produces optimized encoding sequences that, when deployed in the REFoCUS
imaging framework, improve a number of standard quality metrics over
conventional sequences including resolution, field of view, and contrast. We
demonstrate these results experimentally on both wire targets and a
tissue-mimicking phantom. Significance: This work demonstrates that the set of
commonly used encoding schemes represent only a narrow subset of those
available. Additionally, it demonstrates the value for ML tasks in synthetic
transmit aperture imaging to consider the beamformer within the model, instead
of purely as a post-processing step.
