Historically, there are many options to improve image quality that are each derived from the same raw ultrasound sensor data. However, none of these historical options combine multiple contributions in a single image formation step. This invited contribution discusses novel alternatives to beamforming raw ultrasound sensor data to improve image quality, delivery speed, and feature detection after learning from the physics of sound wave propagation. Applications include cyst detection, coherence-based beamforming, and COVID-19 feature detection. A new resource for the entire community to standardize and accelerate research at the intersection of ultrasound beamforming and deep learning is summarized (https://cubdl.jhu.edu). The connection to optics with the integration of ultrasound hardware and software is also discussed from the perspective of photoacoustic source detection, reflection artifact removal, and resolution improvements. These innovations demonstrate outstanding potential to combine multiple outputs and benefits in a single signal processing step with the assistance of deep learning.

© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Muyinatu A. Lediju Bell, "Applications of ultrasound image formation in the deep learning age," Proc. SPIE 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, 1220403 (Presented at SPIE Nanoscience + Engineering: August 21, 2022; Published: 3 October 2022); https://doi.org/10.1117/12.2631614.