Strategies for absolute calibration of near infrared tomographic tissue imaging.

Quantitative near infrared (NIR) imaging of tissue requires the use of a diffusion model-based reconstruction algorithm, which solves for the absorption and scattering coefficients of a tissue volume by matching transmission measurements of light to the predictive diffusion equation solution. Calibration problems as well as other practical considerations arise for an imaging system when using a model-based method for a real system. For example, systematic noise in the data acquisition hardware and source/detector fibers must be removed to prevent spurious results in the reconstructed image. Practical considerations for a NIR diffuse tomographic imaging system include: (1) calibration with a homogeneous phantom, (2) use of a homogenous fitting algorithm to arrive at an initial optical property estimate for image reconstruction of a heterogeneous medium, and (3) correction for fluctuations in source strength and initial phase offset during data acquisition. These practical considerations, which rely on an accurate homogeneous fitting algorithm are described. They have allowed demonstration of a prototype imaging system that has the ability to quantitatively reconstruct heterogeneous images of hemoglobin concentrations within a highly scattering medium with no a priori information.

Hemoglobin imaging with hybrid magnetic resonance and near-infrared diffuse tomography.

This study examines the methodology of combining high-resolution information from magnetic resonance imaging into the reconstruction of near-infrared images of hemoglobin concentration and oxygen saturation. This type of hybrid imaging modality has the potential to provide noninvasive maps of hemoglobin concentration and oxygen saturation with relatively high spatial resolution with a fast time response. The study uses (i) tissue-simulating phantoms, as well as (ii) a rat cranial model, to test the method in two well-controlled situations. The phantom test demonstrates that better reconstruction accuracy can be achieved with the use of MRI-generated spatial regions in near-infrared reconstruction. The rat functional testing reveals that the technique can be applied to in vivo physiology, even in situations where the tissue is quite heterogenous. It also shows that the application of a priori structure in the finite element mesh as well as spatial constraints in the near-infrared image reconstruction, can significantly improve the quality of the resulting hemoglobin images.

Comparisons of three alternative breast modalities in a common phantom imaging experiment.

Four model-based imaging systems are currently being developed for breast cancer detection at Dartmouth College. A potential advantage of multimodality imaging is the prospect of combining information collected from each system to provide a more complete diagnostic tool that covers the full range of the patient and pathology spectra. In this paper it is shown through common phantom experiments on three of these imaging systems that it was possible to correlate different types of image information to potentially improve the reliability of tumor detection. Imaging experiments were conducted with common phantoms which mimic both dielectric and optical properties of the human breast. Cross modality comparison was investigated through a statistical study based on the repeated data sets of reconstructed parameters for each modality. The system standard error between all methods was generally less than 10% and the correlation coefficient across modalities ranged from 0.68 to 0.91. Future work includes the minimization of bias (artifacts) on the periphery of electrical impedance spectroscopy images to improve cross modality correlation and implementation of the multimodality diagnosis for breast cancer detection.

Quantitative analysis of near-infrared tomography: sensitivity to the tissue-simulating precalibration phantom.

A near-infrared (NIR) imaging system is evaluated as a diagnostic clinical tool to image total hemoglobin concentration and oxygen saturation within tissue. Calibration of this type of system requires measurement of the response at each detector and source location from a homogeneous tissue-simulating phantom. The effect of using calibration phantoms of varying composition, size, and optical properties is examined to determine how it affects the overall image accuracy. All of the calibration phantoms investigated result in accurate reconstruction of absorbing heterogeneities due to increased blood concentration with less than 4% standard deviation. Images from a patient with a biopsy-confirmed ductal carcinoma are also evaluated and found to be insensitive to the choice of calibration object, with only 1% variation between images generated with different calibration objects. The tumor total hemoglobin contrast is approximately 240% higher than the average total hemoglobin concentration in contralateral breast. Soft calibration phantoms, which mimic the elastic properties of human breast tissue, are also considered and found to diminish positioning errors in the fibers relative to the actual breast exam, thereby reducing the artifacts in the periphery of the reconstructed image.

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part II--Experimental interpretation.

Image error analysis of a diffuse near-infrared tomography (NIR) system has been carried out on simulated data using a statistical approach described in Part I of this paper (Pogue et al., 2002). The methodology is used here with experimental data acquired on phantoms with a prototype imaging system intended for characterizing breast tissue. Results show that imaging performance is not limited by random measurement error, but rather by calibration issues. The image error over the entire field of view is generally not minimized when an accurate homogeneous estimate of the phantom properties is available; however, local image error over a target region of interest (ROI) is reduced. The image reconstruction process which includes a Levenberg-Marquardt style regularization provides good minimization of the objective function, yet its reduction is not always correlated with an overall image error decrease. Minimization of the bias in an ROI which contains localized changes in the optical properties can be achieved through five to nine iterations of the algorithm. Precalibration of the algorithm through statistical evaluation of phantom studies may provide a better measure of the image accuracy than that implied by minimization of the standard objective function.

Multispectral near-infrared tomography: a case study in compensating for water and lipid content in hemoglobin imaging of the breast.

Images of hemoglobin concentration and oxygen saturation are presented from multispectral near-infrared tomographic measurements in the breast of a woman with an invasive cancer. Images of the absorption coefficient and reduced scattering coefficient are recovered from the measured data using a finite element reconstruction algorithm based on the frequency-domain diffusion equation. Three methods of recovering the hemoglobin concentration and oxygen saturation images are presented which compensate for water and lipid absorption in different ways: (1) an assumed bulk content of water and lipids is used, (2) four chromophores are imaged, and (3) scattering power data are applied to deduce water and lipid images. In all three cases, a large increase in the hemoglobin concentration (3:1) is observed at the location of the cancer while a maximum of 15% difference is observed in the hemoglobin images between each of these methods for water and lipid compensation.