title: On the use of the Cramér-Rao lower bound for diffuse optical imaging system design.
abstract: We evaluated the potential of the Cramér-Rao lower bound (CRLB) to serve as a design metric for diffuse optical imaging systems. The CRLB defines the best achievable precision of any estimator for a given data model; it is often used in the statistical signal processing community for feasibility studies and system design. Computing the CRLB requires inverting the Fisher information matrix (FIM), however, which is usually ill-conditioned (and often underdetermined) in the case of diffuse optical tomography (DOT). We regularized the FIM by assuming that the inhomogeneity to be imaged was a point target and assessed the ability of point-target CRLBs to predict system performance in a typical DOT setting in silico. Our reconstructions, obtained with a common iterative algebraic technique, revealed that these bounds are not good predictors of imaging performance across different system configurations, even in a relative sense. This study demonstrates that agreement between the trends predicted by the CRLBs and imaging performance obtained with reconstruction algorithms that rely on a different regularization approach cannot be assumed a priori. Moreover, it underscores the importance of taking into account the intended regularization method when attempting to optimize source-detector configurations.
authors: Vivian
authors: V
authors: Pera
authors: Dana H
authors: DH
authors: Brooks
authors: Mark
authors: M
authors: Niedre
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D041622:Tomography, Optical / Q000379:methods*