Optimization of Array Encoding for Ultrasound Imaging
Jacob Spainhour Korben Smart Stephen Becker Nick Bottenus
Objective: The transmit encoding model for synthetic aperture imaging is a
robust and flexible framework for understanding the effect 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 an ML model in PyTorch and 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. We demonstrate these results experimentally on wire targets and a
tissue-mimicking phantom. Main Results: When trained according to a given set
of imaging parameters (imaging domain, hardware restrictions), our ML imaging
model produces optimized encoding sequences that improve a number of standard
quality metrics including resolution, field of view, and contrast, over
conventional sequences. Significance: This work demonstrates that the set of
encoding schemes that are commonly used 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 as purely post-processing.