Objective Assessment of Sonographic Quality I: Task Information

Abstract

In this paper, we explore relationships between the performance of the ideal observer and information-based measures of class separability in the context of sonographic breast-lesion diagnosis. This investigation was motivated by a finding that, since the test statistic of the ideal observer in sonography is a quadratic function of the echo data, it is not generally normally distributed. We found for some types of boundary discrimination tasks often required for sonographic lesion diagnosis, the deviation of the test statistic from a normal distribution can be significant. Hence the usual relationships between performance and information metrics become uncertain. Using Monte Carlo studies involving five common sonographic lesion-discrimination tasks, we found in each case that the detectability index d(A)(2) from receiver operating characteristic analysis was well approximated by the Kullback-Leibler divergence J, a measure of clinical task information available from the recorded radio-frequency echo data. However, the lesion signal-to-noise ratio, SNR(I)(2), calculated from moments of the ideal observer test statistic, consistently underestimates d(A)(2) for high-contrast boundary discrimination tasks. Thus, in a companion paper, we established a relationship between image-quality properties of the imaging system and J in order to predict ideal performance. These relationships provide a rigorous basis for sonographic instrument evaluation and design.

Method for optimizing channelized quadratic observers for binary classification of large-dimensional image datasets

“…This symmetric measure of the difference between two PDFs has been related to the ideal-observer AUC in applications where ḡ 1 = ḡ 2 , such as ultrasound imaging [ 6 – 8 ]. This will be an important special case in our development, below.…”

“…When the means are equal but the covariances are different we show a new result: that the same optimal T solution is achieved using optimization with respect to the Bhattacharyya distance, Jeffrey's divergence, and the area under the curve (AUC). This equal mean assumption is valid in ultrasound imaging [ 6 – 8 ] and in many texture discrimination tasks.…”

Sensitivity of Microwave Imaging Systems Employing Scattering-Parameter Measurements

“…When the parameter of interest is the complex permittivity of an object filled uniformly with an isotropic dielectric medium, the complex -parameter derivative is computed as (5) where is the volume of the object, is its complex permittivity, is the radian frequency, is the electric field due to the excitation at port , is the known modal magnitude of the incident wave at port , and . Bearing in mind the complex-permittivity representation , the derivatives with respect to the real part and the imaginary part are obtained from (5) as and , respectively.…”

“…The method of obtaining the SNRI and the physical sensitivity needs rigorous statistical analysis of the system of interest. It was developed further and was applied to other medical imaging modalities in [2]- [5]. Microwave imaging systems have not yet been used in wide clinical practice, and to our knowledge, there are no rigorous or common means to quantify their performance in terms of physical sensitivity.…”

Beamforming designs for breast sonography

“…Generally, an ROC curve is formed from responses as the threshold is swept over the entire response range. However, we used the two-alternative forced-choice (2AFC) observer format that imposes symmetries on the ROC curve such that P c equals the area under the ROC curve (AUC) [8], [9]. Given an ensemble of images drawn from the two classes, performance is measured via AUC.…”