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6 February 2014 On the use of the Cramér–Rao lower bound for diffuse optical imaging system design
Vivian E. Pera, Dana H. Brooks, Mark J. Niedre
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Funded by: National Institutes of Health, US National Institutes of Health (NIH)
Journal of Biomedical Optics, Vol. 19, Issue 2, 025002 (February 2014). https://doi.org/10.1117/1.JBO.19.2.025002
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Vivian E. Pera, Dana H. Brooks, and Mark J. Niedre "On the use of the Cramér–Rao lower bound for diffuse optical imaging system design," Journal of Biomedical Optics 19(2), 025002 (6 February 2014). https://doi.org/10.1117/1.JBO.19.2.025002
Published: 6 February 2014
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Cited by 5 scholarly publications.
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KEYWORDS
Imaging systems
Optical design
Diffuse optical imaging
Sensors
Data modeling
Reconstruction algorithms
Target detection
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