Improved accuracy of reconstructed diffuse optical tomographic images by means of spatial deconvolution: two-dimensional quantitative characterization

Nonlinear approach to difference imaging in diffuse optical tomography

Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors.

Model-resolution-based basis pursuit deconvolution improves diffuse optical tomographic imaging

The image reconstruction problem encountered in diffuse optical tomographic imaging is ill-posed in nature, necessitating the usage of regularization to result in stable solutions. This regularization also results in loss of resolution in the reconstructed images. A frame work, that is attributed by model-resolution, to improve the reconstructed image characteristics using the basis pursuit deconvolution method is proposed here. The proposed method performs this deconvolution as an additional step in the image reconstruction scheme. It is shown, both in numerical and experimental gelatin phantom cases, that the proposed method yields better recovery of the target shapes compared to traditional method, without the loss of quantitativeness of the results.

Fluorescence tomography technique optimized for noninvasive imaging of the mouse brain

In vivo molecular fluorescence tomography of brain disease mouse models has two very specific demands on the optical setup: the use of pigmented furry mice does not allow for a purely noncontact setup, and a high spatial accuracy is required on the dorsal side of the animal due to the location of the brain. We present an optimized setup and tomographic scheme that meet these criteria through a combined CW reflectance-transmittance fiber illumination approach and a charge-coupled device contactless detection scheme. To consider the anatomy of the mouse head and take short source detector separations into account, the forward problem was evaluated by a Monte Carlo simulation input with a magnetic resonance image of the animal. We present an evaluation of reconstruction performance of the setup under three different condition. (i) Using a simulated dataset, with well-defined optical properties and low noise, the reconstructed position accuracy is below 0.5 mm. (ii) Using experimental data on a cylindrical tissue-simulating phantom with well-defined optical properties, a spatial accuracy of about 1 mm was found. (iii) Finally, on an animal model with a fluorescent inclusion in the brain, the target position was reconstructed with an accuracy of 1.6 mm.

Image correction scheme applied to functional diffuse optical tomography scattering images

We have extended our investigation on the use of a linear algorithm for enhancing the accuracy of diffuse optical tomography (DOT) images, to include spatial maps of the diffusion coefficient. The results show that the corrected images are markedly improved in terms of estimated size, spatial resolution, two-object resolving power, and quantitative accuracy. These image-enhancing effects are significant at expected levels of diffusion-coefficient contrast in tissue and noise levels typical of experimental DOT data. Overall, the types and magnitudes of image-enhancing effects obtained here are qualitatively similar to those seen in previous studies on mu(a) perturbations. The implications for practical implementations of DOT time-series imaging are discussed.

Image correction algorithm for functional three-dimensional diffuse optical tomography brain imaging

We outline a computationally efficient image correction algorithm, which we have applied to diffuse optical tomography (DOT) image time series derived from a magnetic resonance imaging (MRI)-based brain model. Results show that the algorithm increases spatial resolution, decreases spatial bias, and only modestly reduces temporal accuracy for noise levels typically seen in experiment, and produces results comparable to image reconstructions that incorporate information from MRI priors. We demonstrate that this algorithm has robust performance in the presence of noise, background heterogeneity, irregular external and internal boundaries, and error in the initial guess. However, the algorithm introduces artifacts when the absorption and scattering coefficients of the reference medium are overestimated--a situation that is easily avoided in practice. The considered algorithm offers a practical approach to improving the quality of images from time-series DOT, even without the use of MRI priors.

A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex

Functional near infrared spectro-imaging (fNIRSI) is potentially a very useful technique for obtaining information about the underlying physiology of the blood oxygenation level dependent (BOLD) signal used in functional magnetic resonance imaging (fMRI). In this paper the temporal and spatial statistical characteristics of fNIRSI data are compared to those of simultaneously acquired fMRI data in the human visual cortex during a variable-frequency reversing checkerboard activation paradigm. Changes in the size of activated volume caused by changes in checkerboard reversal frequency allowed a comparison of the behavior of the spatial responses measured by the two imaging methods. fNIRSI and fMRI data were each analyzed using standard correlation and fixed-effect group analyses of variance pathways. The statistical significance of fNIRSI data was found to be much lower than that of the fMRI data, due mainly to the low signal-to-noise of the measurements. Reconstructed images also showed that, while the time-course of changes in the oxy-, deoxy-, and total hemoglobin concentrations all exhibit high correlation with that of the BOLD response, the changes in the volume of tissue measured as "activated" by the BOLD response demonstrate a closer similarity to the corresponding changes in the oxy- and total hemoglobin concentrations than to that of the deoxyhemoglobin.

Improving performance of reflectance diffuse optical imaging using a multicentered mode

We propose a novel multicentered mode for arrangement of optical fibers to improve the imaging performance of reflectance diffuse optical imaging (rDOI). Simulations performed using a semi-infinite model show that the proposed multicentered geometries can achieve a maximum of 42 overlapping measurements. The contrast-to-noise ratio (CNR) analysis indicates that the best spatial resolution is 1 mm in radius and the contrast resolution is less than 1.05 for the multicentered geometries. The results from simulations indicate significant improvement in image quality compared to the single-centered mode and previous geometries. Additional experimental results on a single human subject lead to the conclusion that the proposed multicentered geometries are appropriate for exploring activations in the human brain. From the results of this research, we conclude that the proposed multicentered mode could advance the performance of rDOI both in image quality and practical convenience.

Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

We present the fourth in a series of studies devoted to the issue of improving image quality in diffuse optical tomography (DOT) by using a spatial deconvolution operation that seeks to compensate for the information-blurring property of first-order perturbation algorithms. Our earlier reports consider only static target media. Here we report spatial deconvolution applied to media with time-varying optical properties, as a model of tissue dynamics resulting from varying metabolic demand and modulation of the vascular bed. Issues under study include the influence of deconvolution on the accuracy of the recovered temporal and spatial information. The impact of noise is also explored, and techniques for ameliorating its information-degrading effects are examined. At low noise levels (i.e, < or = 5% of the time-varying signal amplitude), spatial deconvolution markedly improves the accuracy of recovered information. Temporal information is more seriously degraded by noise than is spatial information, and the impact of noise increases with the complexity of the time-varying signal. These effects, however, can be significantly reduced using simple noise suppression techniques (e.g., low-pass filtering). Results suggest that the deconvolution scheme should provide considerable enhancement of the quality of spatiotemporal information recovered from dynamic DOT techniques applied to tissue studies.