ABSTRACT
The purpose of the present study was to test an idea of and describe a concept of a novel method of detecting defects related to horizontal nonuniformities in ultrasound equipment. The method is based on the analysis of ultrasound images collected directly from the clinical workflow. In total over 31000 images from three ultrasound scanners from two vendors were collected retrospectively from a database. An algorithm was developed and applied to the images, 150 at a time, for detection of systematic dark regions in the superficial part of the images. The result was compared with electrical measurements (FirstCall) of the transducers, performed at times when the transducers were known to be defective. The algorithm made similar detection of horizontal nonuniformities for images acquired at different time points over long periods of time. The results showed good subjective visual agreement with the available electrical measurements of the defective transducers, indicating a potential use of clinical images for early and automatic detection of defective transducers, as a complement to quality control.
Subject(s)
Algorithms , Image Processing, Computer-Assisted/methods , Neoplasms/diagnostic imaging , Phantoms, Imaging , Quality Assurance, Health Care/standards , Transducers/standards , Ultrasonography/standards , Equipment Design , Equipment Failure Analysis , Humans , Quality ControlABSTRACT
The purpose of the present study was to evaluate the relevance of using a phantom to simulate a clinical situation where small low contrast objects are embedded in relatively homogeneous organs in order to discriminate between different ultrasound machines, taking into account human observer variability. One high-end and one general ultrasound machine using the same probe were included. Images containing 4 and 6.4-mm objects of four different contrasts were collected from a greyscale phantom at different depths. Six observers participated in a 4-alternative forced choice study based on 960 images. Variability was determined using bootstrapping. At four of sixteen depth/size/contrast combinations, the visual performance of the high-end machine was significantly higher. The results indicate that it is possible to use a greyscale phantom to discriminate between ultrasound machines in terms of their ability to reproduce clinically relevant low-contrast objects. However, the number of images and number of observers needed are larger than those usually used for constancy control, if the large uncertainties caused by human observer variability is to be taken correctly into account.
