title: Implementation and validation of three automated methods for measuring ultrasound maximum depth of penetration: application to ultrasound quality control.
abstract: Systematic measurements of maximum depth of penetration (DOP) of ultrasound (US) scanners are essential for quality control (QC). Conventionally, DOP measurements are performed visually and as such they could be affected by various external factors, scanner control settings, and operator related errors. Automated methods should be free of the issues associated with interoperator dependence and are an attractive alternative to the visual DOP measurements. We implement and test three automated methods for measuring DOP. The methods base their measurements on signal to noise (SNR) analysis of uniform US phantom images. Two of the methods use pairs of phantom images. The third one uses a single phantom image and an "in-air" image. The validation tests included precision, sensitivity, repeatability, and usability in routine QC application. Methods based on pairs of phantom images measure the DOP with precision +/-0.2 cm or better. Precision of the single phantom image method is +/-0.05 cm, and that method is also the most sensitive of the three. All three methods are demonstrated to be repeatable among different users. Since the images for the DOP computation are collected free-hand the sensitivity to hand-transducer motion during image acquisition was also tested. Unlike the single-phantom-image based method, the methods using image pairs were found to be very sensitive to transducer motion and therefore less convenient for clinical QC applications. In conclusion, the single-phantom-image method is best suited for routine QC in a real-life clinical practice.
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905, USA.
authors.forename: K R
authors.initials: KR
authors.lastname: Gorny
authors.forename: D J
authors.initials: DJ
authors.lastname: Tradup
authors.forename: N J
authors.initials: NJ
authors.lastname: Hangiandreou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D019047:Phantoms, Imaging; D011785:Quality Assurance, Health Care / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000295:instrumentation / Q000379:methods* / Q000592:standards