The ideal observer objective assessment metric for magnetic resonance imaging: application to signal detection tasks

The ideal Bayesian observer is a mathematical construct which makes optimal use of all statistical information about the object and imaging system to perform a task. Its performance serves as an upper bound on any observer's task performance. In this paper a methodology based on the ideal observer for assessing magnetic resonance (MR) acquisition sequences and reconstruction algorithms is developed. The ideal observer in the context of MR imaging is defined and expressions for ideal observer performance metrics are derived. Comparisons are made between the raw-data ideal observer and image-based ideal observer to elucidate the effect of image reconstruction on task performance. Lesion detection tasks are studied in detail via analytical expressions and simulations. The effect of imaging sequence parameters on lesion detectability is shown and the advantages of this methodology over image quality metrics currently in use in MR imaging is demonstrated.