Adaptive Ultrasonic Frequency Selection Using Principles of Spatial Coherence

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This dissertation investigates the clinical utility of adaptive ultrasonic frequency selection using principles of spatial coherence. Presently, the status quo for the selection of settings on an ultrasound scanner leaves much room for improvement. Time constraints and the prevalence of injury to sonographers limit the degree to which scanner settings may optimized for a given patient or acoustic window. One such setting is the frequency, which balances the levels of acoustic noise and resolution. Manufacturers usually include a low- and high-frequency option, but these settings are coarse relative to the overall transducer bandwidth, and leave little room for personalized scanning of each patient. The goal of adaptive frequency selection is to maximize image quality by selecting an optimal frequency at a per-image basis. Automating the process of selecting scanner settings requires a user-independent image quality metric, and conventional metrics, such as contrast and contrast-to-noise ratio (CNR), often require user input to draw a multiple regions-of-interest (ROIs) on the image. This is time consuming as well as prone to further user bias. However, spatial coherence-based metrics, a category of image quality metrics developed by our group and others for use in medical ultrasound, avoids these issues while remaining sensitive to acoustic noise.

This work is presented in four chapters. Chapter 1 provides a review of spatial coherence in medical ultrasound, including image quality characterization techniques, beamforming methods, and a discussion of potential future areas of exploration.

Chapter 2 details a simulation study in which spatial coherence is used to predict the loss in imaging contrast as well as separate the effects of different acoustic noise sources. Results showed agreement between theory and simulations for a multitude of image quality metrics when considering two types of noise: incoherent noise and partially coherent noise. Minimal error was seen between coherence-predicted contrast loss and measured contrast loss. This presented framework shows promise to improve the evaluation of noise reduction strategies.

Chapter 3 details the development of an efficient method to collect frequency-dependent spatial coherence information by leveraging a type of coded transmission known as a chirp. Chirp-collected measurements of coherence were compared to those acquired by individually transmitted conventional pulses over a range of frequencies. Results from ex vivo and in vivo acquisitions showed that chirps replicated the mean coherence in a region-of-interest. This work indicates that the use of chirps is a viable strategy to expedite the collection of frequency-dependent spatial coherence, presenting an avenue for real-time adaptive frequency selection.

Lastly, Chapter 4 details the clinical validation of adaptive frequency selection through a reader study. Image quality improvements shown with coherence-based metrics were corroborated by reader outcomes scores for overall quality, border detection, and target conspicuity. Statistical testing revealed a significant difference between the rated image quality of adaptive images and transducer default images. These results suggest that an optimal frequency can be automatically selected for target detection.





Long, James (2022). Adaptive Ultrasonic Frequency Selection Using Principles of Spatial Coherence. Dissertation, Duke University. Retrieved from


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