Contrast-detail analysis for detection and characterization with near-infrared diffuse tomography.

Near-infrared (NIR) diffuse tomography is emerging as a medical imaging modality for obtaining information related to tissue hemoglobin concentration and oxygen saturation and may be used for characterizing diseased tissues such as breast cancer. The optimal methodology for NIR image reconstruction remains an ongoing research problem with several new approaches being demonstrated in recent years. However, a comparison of reconstruction methods is problematic because tools for the objective assessment of image quality have yet to be clearly defined for this type of nonlinear reconstruction problem. Contrast-detail analysis has become an accepted assessment tool to quantify x-ray mammography image quality, and in this study it has been applied to a prototype NIR diffuse tomography system that is being evaluated for breast cancer characterization. The minimum detectable levels of contrast have been defined for different sizes of objects, and the minimum contrasts which can be accurately reconstructed have also been determined for the same object sizes. In general, objects 8 mm and larger in diameter can be accurately reconstructed and detected for most absorption contrasts which are observed in human tissues (i.e., greater than 1% contrast in absorption). Objects as small as 2 mm can be detected with high contrast (i.e., near 100%), but cannot be accurately reconstructed. Within the size range of 2 mm to 8 mm, there is an inverse correlation between contrast and detail size which is characteristic of the total noise in the system. This analysis provides an objective method for assessing detection and characterization limits and can be applied to future improvements in hardware system architecture as well as reconstruction algorithms.

Confidence maps and confidence intervals for near infrared images in breast cancer.

This paper extends basic concepts of statistical hypothesis testing and confidence intervals to images generated by a new procedure for near infrared spectroscopic tomography being developed for use in breast cancer diagnosis. By estimating the covariance matrix of the pixels of an image from data used in the image reconstruction process, confidence maps for statistical tests on individual pixels and confidence intervals for entire images are displayed as an aid to research and clinical personnel interpreting possibly noisy images. The methods are applied to simulated and phantom-based images.

Spatially variant regularization improves diffuse optical tomography.

Diffuse tomography with near-infrared light has biomedical application for imaging hemoglobin, water, lipids, cytochromes, or exogenous contrast agents and is being investigated for breast cancer diagnosis. A Newton-Raphson inversion algorithm is used for image reconstruction of tissue optical absorption and transport scattering coefficients from frequency-domain measurements of modulated phase shift and light intensity. A variant of Tikhonov regularization is examined in which radial variation is allowed in the value of the regularization parameter. This method minimizes high-frequency noise in the reconstructed image near the source-detector locations and can produce constant image resolution and contrast across the image field.

Initial studies of in vivo absorbing and scattering heterogeneity in near-infrared tomographic breast imaging.

Simultaneously recovered absorption and scattering images that separate these optical property features within the female breast are demonstrated from frequency-domain measurements. A study of known absorbing and scattering objects is presented as a foundation for interpreting these in vivo images once the contrast space has been fully characterized. No measurable influence of absorbing-object contrast appears in the scattering images, whereas localized scattering contrast enhances the corresponding region within the absorption image by approximately 30% (e.g., a 2:1 scatterer also reconstructs as an approximately 1.3:1 absorber). Scattering and absorption images of a female volunteer with a 3.4-cm fibroadenoma show a clear 2:1 localized increase in absorption coefficient with little or no evidence of scattering enhancement in the lesion.

Three-dimensional simulation of near-infrared diffusion in tissue: boundary condition and geometry analysis for finite-element image reconstruction.

Imaging of tissue with near-infrared spectral tomography is emerging as a practicable method to map hemoglobin concentrations within tissue. However, the accurate recovery of images by using modeling methods requires a good match between experiments and the model prediction of light transport in tissue. We illustrate the potential for a match between (i) three-dimensional (3-D) frequency-domain diffusion theory, (ii) two-dimensional diffusion theory, (iii) Monte Carlo simulations, and (iv) experimental data from tissue-simulating phantoms. Robin-type boundary conditions are imposed in the 3-D model, which can be implemented with a scalar coupling coefficient relating the flux through the surface to the diffuse fluence rate at the same location. A comparison of 3-D mesh geometries for breast imaging indicates that relative measurements are sufficiently similar when calculated on either cylindrical or female breast shapes, suggesting that accurate reconstruction may be achieved with the simpler cylindrical mesh. Simulation studies directly assess the effects from objects extending out of the image plane, with results suggesting that spherically shaped objects reconstruct at lower contrast when their diameters are less than 15-20 mm. The algorithm presented here illustrates that a 3-D forward diffusion model can be used with circular tomographic measurements to reconstruct two-dimensional images of the interior absorption coefficient.

Spectroscopic diffuse optical tomography for the quantitative assessment of hemoglobin concentration and oxygen saturation in breast tissue.

Near-infrared (NIR) spectroscopic diffuse tomography has been used to map the hemoglobin concentration and the hemoglobin oxygen saturation quantitatively in tissuelike phantoms and to determine average values in vivo. A series of phantom calibrations were performed to achieve quantitatively accurate images of the absorption and the reduced scattering coefficients at multiple optical wavelengths. A least-squares fit was applied to absorption-coefficient images at multiple NIR wavelengths to obtain hemoglobin images of the concentration and the hemoglobin oxygen saturation. Objects of varying hemoglobin concentration and oxygen saturation within highly scattering media were localized and imaged to within 15% of their actual values. The average hemoglobin concentration and oxygen saturation of breast tissue was measured in vivo for two women volunteers. The potential application for the diagnosis of breast tumors is discussed.

Quantitative hemoglobin tomography with diffuse near-infrared spectroscopy: pilot results in the breast.

The authors describe what is, to the best of their knowledge, the first quantitative hemoglobin concentration images of the female breast that were formed with model-based reconstruction of near-infrared intensity-modulated tomographic data. The results in 11 patients, including two with breast tumors with pathologic correlation, are summarized. Hemoglobin concentration appears to correlate with tumor vascularity without the need for exogenous contrast material and thereby has intrinsic diagnostic value.