Acoustic Shadow Detection: Study and Statistics of B-Mode and Radiofrequency Data

• Published in: Ultrasound in medicine & biology Vol. 45; no. 8; pp. 2248 - 2257
• Main Authors: Hu, Ricky, Singla, Rohit, Deeba, Farah, Rohling, Robert N.
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.08.2019
• Subjects: Acoustic shadow; Adult; Elbow - anatomy & histology; Forearm - anatomy & histology; Humans; Radiofrequency; Ribs - anatomy & histology; Segmentation; Speckle; Transducers; Ultrasonography - instrumentation; Ultrasonography - methods; Ultrasound; Upper Extremity - anatomy & histology; Speckle; Segmentation; Radiofrequency; Acoustic shadow; Ultrasound
• ISSN: 0301-5629; 1879-291X
• DOI: 10.1016/j.ultrasmedbio.2019.04.001

An acoustic shadow is an ultrasound artifact occurring at boundaries between significantly different tissue impedances, resulting in signal loss and a dark appearance. Shadow detection is important as shadows can identify anatomical features or obscure regions of interest. A study was performed to scan human participants (N = 37) specifically to explore the statistical characteristics of various shadows from different anatomy and with different transducers. Differences in shadow statistics were observed and used for shadow detection algorithms with a fitted Nakagami distribution on radiofrequency (RF) speckle or cumulative entropy on brightness-mode (B-mode) data. The fitted Nakagami parameter and entropy values in shadows were consistent across different transducers and anatomy. Both algorithms utilized adaptive thresholding, needing only the transducer pulse length as an input parameter for easy utilization by different operators or equipment. Mean Dice coefficients (± standard deviation) of 0.90 ± 0.07 and 0.87 ± 0.08 were obtained for the RF and B-mode algorithms, which is within the range of manual annotators. The high accuracy in different imaging scenarios indicates that the shadows can be detected with high versatility and without expert configuration. The understanding of shadow statistics can be used for more specialized techniques to be developed for specific applications in the future, including pre-processing for machine learning and automatic interpretation.