An Automated Region-Selection Method for Adaptive ALARA Ultrasound Imaging

• Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control Vol. 69; no. 7; pp. 2257 - 2269
• Main Authors: Flint, Katelyn M., Barre, Emily C., Huber, Matthew T., McNally, Patricia J., Ellestad, Sarah C., Trahey, Gregg E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic exposure; Acoustics; adaptive imaging; Algorithms; as low as reasonably achievable; Automation; Brightness; fetal ultrasound; Humans; Image quality; Imaging; Noise prediction; Quality assessment; safety; segmentation; Signal to noise ratio; Transducers; Ultrasonic imaging; Ultrasonic scanners; Ultrasonic testing; Ultrasonography - methods
• ISSN: 0885-3010; 1525-8955; 2373-7840; 1525-8955
• DOI: 10.1109/TUFFC.2022.3172690

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.