Abstract: Over the past decade, developments towards near-infrared (NIR) optical tomography involve the recovery of interior optical maps from boundary measurements using the first principles of light propagation models. The refractive-index mismatch parameter in the boundary condition of the light propagation model, namely the diffusion equation, can significantly impact model prediction of measurements and therefore image recovery. In this contribution, the influence of refractive-index mismatch parameter between predictions and referenced measurements of fluorescence-enhanced frequency-domain photon migration (FDPM) are established; its greater influence on emission over excitation predictions are demonstrated, and the methods to accurately determine refractive index mismatch parameter from basic principles are reviewed.
Abstract: 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-cm^3 tissue-mimicking phantoms containing none, one, or two 1-cm^3 heterogeneities 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.
Abstract: We present three-dimensional tomographic images of the absorption coefficient that is due to the presence of a fluorophore reconstructed from frequency domain fluence measurements of a tissue phantom containing a single, fluorescence contrast-enhanced inclusion. We show that such a reconstruction may be improved when the importance of measurement error correlations between relative phase shift and amplitude is assessed and when measurements are preprocessed to reduce the magnitude and the bias of system error.
Abstract: Most current efforts in near-infrared optical tomography are effectively limited to two-dimensional reconstructions due to the computationally intensive nature of full three-dimensional (3-D) data inversion. Previously, we described a new computationally efficient and statistically powerful inversion method APPRIZE (automatic progressive parameter-reducing inverse zonation and estimation). The APPRIZE method computes minimum-variance estimates of parameter values (here, spatially variant absorption due to a fluorescent contrast agent) and covariance, while simultaneously estimating the number of parameters needed as well as the size, shape, and location of the spatial regions that correspond to those parameters. Estimates of measurement and model error are explicitly incorporated into the procedure and implicitly regularize the inversion in a physically based manner. The optimal estimation of parameters is bounds-constrained, precluding infeasible values. In this paper, the APPRIZE method for optical imaging is extended for application to arbitrarily large 3-D domains through the use of domain decomposition. The effect of sub-domain size on the performance of the method is examined by assessing the sensitivity for identifying 112 randomly located single-voxel heterogeneities in 58 3-D domains. Also investigated are the effects of unmodeled heterogeneity in background optical properties. The method is tested on simulated frequency-domain photon migration measurements at 100 MHz in order to recover absorption maps owing to fluorescent contrast agent. This study provides a new approach for computationally tractable 3-D optical tomography.
Abstract: Research into the near-infrared biomedical optical imaging has produced a multitude of inverse imaging algorithms. Recent experience has shown that when these algorithms are tested with experimental data, they falter due to a mismatch between observed and simulated measurements. When considering measurements for imaging, one must consider both measurement and model error. If data is recorded properly, then measurement error tends to be normally distributed with a mean of zero. Model error can be biased and spatially correlated due to inaccuracies in the diffusion approximation, inaccurate parameter estimates, numerical error, and other factors. This contribution discusses trends in the measurement and model error observed from measurements on a single-pixel, frequency domain photon migration system developed for biomedical optical imaging. In order to reduce the model error bias, an empirical approach was applied to find experimental variables that significantly affect it. This approach reduced the mean of the model error on a test data set and produced a slight smoothing effect on its distribution. Image reconstruction attempts show that the modified data set produces an improved image over the image reconstructed from the raw data set. To our knowledge, this is the first time that model and measurement error information have been incorporated into a three dimensional image reconstruction algorithm.