ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2022
An Automated Region-Selection Method for Adaptive ALARA Ultrasound Imaging.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Spatial Coherence-Based Sound Speed Estimation in Plane Wave Ultrasound Imaging.IEEE transactions on ultrasonics · 2026Article
- In Vivo Demonstration of a Real-Time Temporal SNR Acoustic Output Adjustment Method.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Article
- Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST).IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
The objective of this work was to develop an automated region of the interest selection method to use for adaptive imaging. The as low as reasonably achievable (ALARA) principle is the recommended framework for setting the output level of diagnostic ultrasound devices, but studies suggest that it is not broadly observed. One way to address this would be to adjust output settings automatically based on image quality feedback, but a missing link is determining how and where to interrogate the image quality. This work provides a method of region of interest selection based on standard, envelope-detected image data that are readily available on ultrasound scanners. Image brightness, the standard deviation of the brightness values, the speckle signal-to-noise ratio, and frame-to-frame correlation were considered as image characteristics to serve as the basis for this selection method. Region selection with these filters was compared to results from image quality assessment at multiple acoustic output levels. After selecting the filter values based on data from 25 subjects, testing on ten reserved subjects' data produced a positive predictive value of 94% using image brightness, the speckle signal-to-noise ratio, and frame-to-frame correlation. The best case filter values for using only image brightness and speckle signal-to-noise ratio had a positive predictive value of 97%. These results suggest that these simple methods of filtering could select reliable regions of interest during live scanning to facilitate adaptive ALARA imaging.
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