Adaptive imaging systems alter their data-acquisition configuration or protocol in response to the image information received. An adaptive pinhole single-photon emission computed tomography (SPECT) system might acquire an initial scout image to obtain preliminary information about the radiotracer distribution and then adjust the configuration or sizes of the pinholes, the magnifications, or the projection angles in order to improve performance. This paper briefly describes two small-animal SPECT systems that allow this flexibility and then presents a framework for evaluating adaptive systems in general, and adaptive SPECT systems in particular. The evaluation is in terms of the performance of linear observers on detection or estimation tasks. Expressions are derived for the ideal linear (Hotelling) observer and the ideal linear (Wiener) estimator with adaptive imaging. Detailed expressions for the performance figures of merit are given, and possible adaptation rules are discussed.

Prior work demonstrated significant contrast in visible wavelength Mueller matrix images for healthy and pre-cancerous regions of excised cervical tissue. This work demonstrates post-processing compressions of the full Mueller matrix that preserve detection performance. The purpose of this post-processing is to understand polarimetric measurement utility for computing mathematical observers and designing future imaging protocols. The detection performance of the full Mueller matrix, and both linear and non-linear parameters of the Mueller matrix will be compared. The area under the receiver operating characteristic (ROC) curve, otherwise known as the AUC, is the gold standard metric to quantify detection performance in medical applications. An AUC = 1 is perfect detection and AUC = 0.5 is the performance of guessing. Either the scalar retardance or the 3 smallest eigenvalues of the coherency matrix yield an average AUC of 0.94 or 0.93, respectively. When these four non-linear parameters are used simultaneously the average AUC is 0.95. The J-optimal Channelized Quadratic Observer (J-CQO) method for optimizing polarimetric measurements demonstrates equivalent AUC values for the full Muller matrix and 6 J-CQO optimized measurements. The advantage of this optimization is that only 6 measurements, instead of 16 for the full Mueller matrix, are required to achieve this AUC.

The likelihood ratio, or ideal observer, decision rule is known to be optimal for two-class classification tasks in the sense that it maximizes expected utility (or, equivalently, minimizes the Bayes risk). Furthermore, using this decision rule yields a receiver operating characteristic (ROC) curve which is never above the ROC curve produced using any other decision rule, provided the observer's misclassification rate with respect to one of the two classes is chosen as the dependent variable for the curve (i.e., an "inversion" of the more common formulation in which the observer's true-positive fraction is plotted against its false-positive fraction). It is also known that for a decision task requiring classification of observations into N classes, optimal performance in the expected utility sense is obtained using a set of N − 1 likelihood ratios as decision variables. In the N-class extension of ROC analysis, the ideal observer performance is describable in terms of an (N 2 − N − 1)-parameter hypersurface in an (N 2 − N)-dimensional probability space. We show that the result for two classes holds in this case as well, namely that the ROC hypersurface obtained using the ideal observer decision rule is never above the ROC hypersurface obtained using any other decision rule (where in our formulation performance is given exclusively with respect to between-class error rates rather than within-class sensitivities).

Multimodality imaging is becoming increasingly important in medical imaging. Since the motivation for combining multiple imaging modalities is generally to improve diagnostic or prognostic accuracy, the benefits of multimodality imaging cannot be assessed through the display of example images. Instead, we must use objective, task-based measures of image quality to draw valid conclusions about system performance. In this paper, we will present a general framework for utilizing objective, taskbased measures of image quality in assessing multimodality and adaptive imaging systems. We introduce a classification scheme for multimodality and adaptive imaging systems and provide a mathematical description of the imaging chain along with block diagrams to provide a visual illustration. We show that the task-based methodology developed for evaluating single-modality imaging can be applied, with minor modifications, to multimodality and adaptive imaging. We discuss strategies for practical implementing of task-based methods to assess and optimize multimodality imaging systems.