VPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging

Abstract

Often, the large amounts of data generated in diagnostic imaging cause overload problems for IT systems and radiologists. This entails a need of effective use of data reduction beyond lossless levels, which, in turn, underlines the need to measure and control the image fidelity. Existing image fidelity metrics, however, fail to fully support important requirements from a modern clinical context: support for high-dimensional data, visualization awareness, and independence from the original data. We propose an image fidelity metric, called the visual peak signal-to-noise ratio (vPSNR), fulfilling the three main requirements. A series of image fidelity tests on CT data sets is employed. The impact of visualization transform (grayscale window) on diagnostic quality of irreversibly compressed data sets is evaluated through an observer-based study. In addition, several tests were performed demonstrating the benefits, limitations, and characteristics of vPSNR in different data reduction scenarios. The visualization transform has a significant impact on diagnostic quality, and the vPSNR is capable of representing this effect. Moreover, the tests establish that the vPSNR is broadly applicable. vPSNR fills a gap not served by existing image fidelity metrics, relevant for the clinical context. While vPSNR alone cannot fulfill all image fidelity needs, it can be a useful complement in a wide range of scenarios.