Kyle J. Myers

Addition of a channel mechanism to the ideal-observer model

Several authors have measured the detection ability of human observers for objects in correlated (nonwhite) noise. These studies have shown that the human observer has approximately constant efficiency when compared with a nonprewhitening ideal observer. In this paper we add a frequency-selective mechanism to the ideal-observer model, similar to the channel mechanism that has been demonstrated through experiments that measure a subjects ability to detect grating stimuli. For a number of detection and discrimination tasks, the nonprewhitening ideal-observer model and the channelized ideal-observer model yield similar performance predictions. Thus both models seem equally capable of explaining a considerable body of psychophysical data, and it would be difficult to devise an experiment to determine which model is more nearly correct.

Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance.

Figures of merit for image quality are derived on the basis of the performance of mathematical observers on specific detection and estimation tasks. The tasks include detection of a known signal superimposed on a known background, detection of a known signal on a random background, estimation of Fourier coefficients of the object, and estimation of the integral of the object over a specified region of interest. The chosen observer for the detection tasks is the ideal linear discriminant, which we call the Hotelling observer. The figures of merit are based on the Fisher information matrix relevant to estimation of the Fourier coefficients and the closely related Fourier crosstalk matrix introduced earlier by Barrett and Gifford [Phys. Med. Biol. 39, 451 (1994)]. A finite submatrix of the infinite Fisher information matrix is used to set Cramer-Rao lower bounds on the variances of the estimates of the first N Fourier coefficients. The figures of merit for detection tasks are shown to be closely related to the concepts of noise-equivalent quanta (NEQ) and generalized NEQ, originally derived for linear, shift-invariant imaging systems and stationary noise. Application of these results to the design of imaging systems is discussed.

Noncalcified Lung Nodules: Volumetric Assessment with Thoracic CT

Lung nodule volumetry is used for nodule diagnosis, as well as for monitoring tumor response to therapy. Volume measurement precision and accuracy depend on a number of factors, including image-acquisition and reconstruction parameters, nodule characteristics, and the performance of algorithms for nodule segmentation and volume estimation. The purpose of this article is to provide a review of published studies relevant to the computed tomographic (CT) volumetric analysis of lung nodules. A number of underexamined areas of research regarding volumetric accuracy are identified, including the measurement of nonsolid nodules, the effects of pitch and section overlap, and the effect of respiratory motion. The need for public databases of phantom scans, as well as of clinical data, is discussed. The review points to the need for continued research to examine volumetric accuracy as a function of a multitude of interrelated variables involved in the assessment of lung nodules. Understanding and quantifying the sources of volumetric measurement error in the assessment of lung nodules with CT would be a first step toward the development of methods to minimize that error through system improvements and to correctly account for any remaining error.

Effect of noise correlation on detectability of disk signals in medical imaging

Pixel signal-to-noise ratio is one accepted measure of image quality for predicting observer performance in medical imaging. We have found, however, that images with equal pixel signal-to-noise ratio (SNRp) but different correlation properties give quite different observer-performance measures for a simple detection experiment. The SNR at the output of an ideal detector with the ability to prewhiten the noise is also a poor predictor of human performance for disk signals in high-pass noise. We have found constant observer efficiencies for humans relative to the performance of a nonprewhitening detector for this task.

Hotelling trace criterion and its correlation with human-observer performance

The Hotelling trace criterion (HTC) is used to find a set of linear features that optimally separate two classes of objects. The objects used in our study were simulated livers with and without tumors, with noise, blur, and object variability. Using the receiver-operating-characteristic parameter da as our measure, we have found that the ability of the HTC to separate these objects into their correct classes, by detecting the presence or absence of a tumor, has a correlation of 0.988 with the ability of humans to separate the same two classes of objects. This suggests, therefore, that the HTC can be used as a figure of merit for optimizing system parameters, since it calculates a single, scalar figure of merit that has a high correlation with human-observer performance.

Aperture optimization for emission imaging : effect of a spatially varying background

A method for optimizing the aperture size in emission imaging is presented that takes into account limitations due to the Poisson nature of the detected radiation stream as well as the conspicuity limitation imposed by a spatially varying background. System assessment is based on the calculated performance of two model observers: the best linear observer, also called the Hotelling observer, and the nonprewhitening matched-filter observer. The tasks are the detection of a Gaussian signal and the discrimination of a single from a double Gaussian signal. When the background is specified, detection is optimized by enlarging the aperture; an inhomogeneous background results in an optimum aperture size matched naturally to the signal. The discrimination task has a finite optimum aperture for a flat background; a nonuniform background drives the optimum toward still-finer resolution.

Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons

Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research.

A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom

A number of interrelated factors can affect the precision and accuracy of lung nodule size estimation. To quantify the effect of these factors, we have been conducting phantom CT studies using an anthropomorphic thoracic phantom containing a vasculature insert to which synthetic nodules were inserted or attached. Ten repeat scans were acquired on different multi-detector scanners, using several sets of acquisition and reconstruction protocols and various nodule characteristics (size, shape, density, location). This study design enables both bias and variance analysis for the nodule size estimation task. The resulting database is in the process of becoming publicly available as a resource to facilitate the assessment of lung nodule size estimation methodologies and to enable comparisons between different methods regarding measurement error. This resource complements public databases of clinical data and will contribute towards the development of procedures that will maximize the utility of CT imaging for lung cancer screening and tumor therapy evaluation.

Objective assessment of image quality. IV. Application to adaptive optics.

The methodology of objective assessment, which defines image quality in terms of the performance of specific observers on specific tasks of interest, is extended to temporal sequences of images with random point spread functions and applied to adaptive imaging in astronomy. The tasks considered include both detection and estimation, and the observers are the optimal linear discriminant (Hotelling observer) and the optimal linear estimator (Wiener). A general theory of first- and second-order spatiotemporal statistics in adaptive optics is developed. It is shown that the covariance matrix can be rigorously decomposed into three terms representing the effect of measurement noise, random point spread function, and random nature of the astronomical scene. Figures of merit are developed, and computational methods are discussed.

Objective assessment of image quality and dose reduction in CT iterative reconstruction

PURPOSE Iterative reconstruction (IR) algorithms have the potential to reduce radiation dose in CT diagnostic imaging. As these algorithms become available on the market, a standardizable method of quantifying the dose reduction that a particular IR method can achieve would be valuable. Such a method would assist manufacturers in making promotional claims about dose reduction, buyers in comparing different devices, physicists in independently validating the claims, and the United States Food and Drug Administration in regulating the labeling of CT devices. However, the nonlinear nature of commercially available IR algorithms poses challenges to objectively assessing image quality, a necessary step in establishing the amount of dose reduction that a given IR algorithm can achieve without compromising that image quality. This review paper seeks to consolidate information relevant to objectively assessing the quality of CT IR images, and thereby measuring the level of dose reduction that a given IR algorithm can achieve. METHODS The authors discuss task-based methods for assessing the quality of CT IR images and evaluating dose reduction. RESULTS The authors explain and review recent literature on signal detection and localization tasks in CT IR image quality assessment, the design of an appropriate phantom for these tasks, possible choices of observers (including human and model observers), and methods of evaluating observer performance. CONCLUSIONS Standardizing the measurement of dose reduction is a problem of broad interest to the CT community and to public health. A necessary step in the process is the objective assessment of CT image quality, for which various task-based methods may be suitable. This paper attempts to consolidate recent literature that is relevant to the development and implementation of task-based methods for the assessment of CT IR image quality.