Observer-Usable Information as a Task-Specific Image Quality Metric

Objective, task-based measures of image quality (IQ) have been widely advocated for assessing and optimizing medical imaging technologies. Besides signal detection theory-based measures, information-theoretic quantities have been proposed to quantify task-based IQ. For example, task-specific information (TSI), defined as the mutual information between an image and a task variable, represents an optimal measure of how informative an image is for performing a specified task. However, like the ideal observer from signal detection theory, TSI does not quantify the amount of task-relevant information in an image that can be exploited by a sub-ideal observer. A recently proposed relaxation of TSI, termed predictive $\mathcal {V}$ -information ( $\mathcal {V}$ -info), removes this limitation and can quantify the utility of an image with consideration of a specified family of sub-ideal observers. In this study, for the first time, we introduce and investigate $\mathcal {V}$ -info as an objective, task-specific IQ metric. To corroborate its usefulness, a stylized magnetic resonance image restoration problem is considered in which $\mathcal {V}$ -info is employed to quantify signal detection or discrimination performance. The presented experiments show that, for binary classification tasks, $\mathcal {V}$ -info varies consistently with the area under the receiver operating characteristic (ROC) curve in regimes where class separability changes with observer capacity or imaging conditions. However, unlike AUC, $\mathcal {V}$ -info remains sensitive in regimes where discrimination performance approaches saturation. In addition, $\mathcal {V}$ -info is readily applicable to multi-class ( $\gt {2}$ ) tasks where ROC analysis is less natural. These findings suggest that $\mathcal {V}$ -info can serve as a complementary task-based image quality measure alongside traditional signal detection theory-based metrics.