Phantom-based quality control, the current standard of QC in medical imaging, calibrates image quality at a population level, but does not account for the influence of patient variation on quality. In this work, we present a method to evaluate task-based image quality directly in individual clinical CT exams. Noise power spectrum (NPS) is measured in selected local image regions satisfying linearity and noise stationarity constraints, and globally over the volumetric image. Together with a semi-empirical model of image resolution, NPS is used to calculate noise-equivalent quanta (NEQ), a fundamental metric of image fidelity and information content. The NEQ may be extended to task-based detectability (d’) via a specified task function and model observer. We show that this method can: 1) elucidate intra-patient variations in signal detectability, and 2) task performance variations across a patient population. The method may be implemented in a hospital-wide online system that monitors imaging performance in CT exams in real-time.
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