Abstract
Reliable assessment of image quality is an important but challenging task in complex imaging environments such as those encountered in vivo. To address this challenge, we propose a novel imaging metric, known as the lag-one coherence (LOC), which leverages the spatial coherence between nearest-neighbor array elements to provide a local measure of thermal and acoustic noise. In this paper, we derive the theory that relates LOC and the conventional image quality metrics of contrast and contrast-to-noise ratio (CNR) to channel noise. Simulation and phantom studies are performed to validate this theory and compare the variability of LOC to that of conventional metrics. We further evaluate the performance of LOC using matched measurements of contrast, CNR, and temporal correlation from in vivo liver images formed with varying mechanical index (MI) to assess the feasibility of adaptive acoustic output selection using LOC feedback. Simulation and phantom results reveal a lower variability in LOC relative to contrast and CNR over a wide range of clinically relevant noise levels. This improved stability is supported by in vivo measurements of LOC which show an increased monotonicity with changes in MI compared to matched measurements of contrast and CNR (88.6% and 85.7% of acquisitions, respectively). The sensitivity of LOC to stationary acoustic noise is evidenced by positive correlations between LOC and contrast ( ) and LOC and CNR ( ) at high acoustic output levels in the absence of thermal noise. Results indicate that LOC provides repeatable characterization of patient-specific trends in image quality, demonstrating feasibility in the selection of acoustic output using LOC and its application for in vivo image quality assessment.