Three-Dimensional, Bayesian Image Reconstruction from Sparse and Noisy Data Sets: Near-Infrared Fluorescence Tomography

A method for inverting measurements made on the surfaces of tissues for recovery of interior optical property maps is demonstrated for sparse near-infrared (NIR) fluorescence measurement sets on large tissue-simulating volumes with highly variable signal-to-noise ratio. A Bayesian minimum-variance r...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2002-07, Vol.99 (15), p.9619-9624
Main Authors: Eppstein, Margaret J., Hawrysz, Daniel J., Godavarty, Anuradha, Sevick-Muraca, Eva M.
Format: Article
Language:English
Subjects:
Algorithms
Analysis of Variance
Bayes Theorem
Covariance
Covariance matrices
Damping
Fluence
Fluorescence
Image Processing, Computer-Assisted
Image reconstruction
Medical imaging
Noise measurement
Physical Sciences
Reproducibility of Results
Spectrophotometry, Infrared - methods
Statistical variance
Tomography
Tomography - methods
Wavelengths
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Summary:A method for inverting measurements made on the surfaces of tissues for recovery of interior optical property maps is demonstrated for sparse near-infrared (NIR) fluorescence measurement sets on large tissue-simulating volumes with highly variable signal-to-noise ratio. A Bayesian minimum-variance reconstruction algorithm compensates for the spatial variability in signal-to-noise ratio that must be expected to occur in actual NIR contrast-enhanced diagnostic medical imaging. Image reconstruction is demonstrated by using frequency-domain photon migration measurements on 256-cm3tissue-mimicking phantoms containing none, one, or two 1-cm3heterogeneities with 50- to 100-fold greater concentration of Indocyanine Green dye over background levels. The spatial parameter estimate of absorption owing to the dye was reconstructed from only 160 to 296 surface measurements of emission light at 830 nm in response to incident 785-nm excitation light modulated at 100 MHz. Measurement error of acquired fluence at fluorescent emission wavelengths is shown to be highly variable. Convergence and quality of image reconstructions are improved by Bayesian conditioning incorporating (i) experimentally determined measurement error variance, (ii) recursively updated estimates of parameter uncertainty, and (iii) dynamic zonation. The results demonstrate that, to employ NIR fluorescence-enhanced optical imaging for large volumes, reconstruction approaches must account for the large range of signal-to-noise ratio associated with the measurements.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.112217899