Diffuse near-infrared tomography of tissue reveals scattering changes that originate from the submicroscopic features of the tissue; yet the existing tools to use this information to predict which features contribute to the scattering spectrum are limited by the lack of direct data quantifying the particle sizes. Breast tissue was examined with electron microscopy, and analysis showed that the distributions of particle sizes appear in double exponential functions for most cellular tissues. The average particle size histograms of high-grade cancer, low-grade cancer, fibroglandular tissue, and adipose tissue were examined. The particle histograms were progressively decreasing in magnitude for these tissue types, and the average size of the particles increased, for these four tissues, respectively. Typical particle sizes in the range of 10 to 500 nm for these tissue types, with biexponential fitting, gave two particle distributions: one near 20 to 25 nm for the smaller size and one at 110 to 230 nm for the larger distributions. Mie scatter theory was used to take these particle distributions and calculate scattering spectra. The ability to image reduced scattering coefficient spectra of bulk breast tissues exists, and so this data provides insight into how bulk imaging may be mapped over to predict factors related to the tissue ultrastructure.
A method for image reconstruction of the effective size and number density of scattering particles is discussed within the context of interpreting near-infrared (NIR) tomography images of breast tissue. An approach to use Mie theory to estimate the effective scattering parameters is examined and applied, given some assumptions about the index of refraction change expected in lipid membrane-bound scatterers. When using a limited number of NIR wavelengths in the reduced scattering spectra, the parameter extraction technique is limited to representing a continuous distribution of scatterer sizes, which is modeled as a simple exponentially decreasing distribution function. In this paper, image formation of effective scatterer size and number density is presented based on the estimation method. The method was evaluated with Intralipid phantom studies to demonstrate particle size estimation to within 9% of the expected value. Then the method was used in NIR patient images, and it indicates that for a cancer tumor, the effective scatterer size is smaller than the background breast values and the effective number density is higher. In contrast, for benign tumor patients, there is not a significant difference in effective scatterer size or number density between tumor and normal tissues. The method was used to interpret magnetic resonance imaging–coupled NIR images of adipose and fibroglandular tissues, and it indicated that the fibroglandular tissue has smaller effective scatterer size and larger effective number density than the adipose tissue does.
A method for estimating Mie theory scattering parameters from diffuse light tomography measurements in breast tissue is discussed. The approach provides an estimate of the mean particle size and number density given assumptions about the index of refraction change expected in lipid-membrane-bound scatterers. When using a sparse number of wavelengths in the reduced scattering spectra, the parameter extraction technique is limited to representing a continuous distribution of scatterer sizes that appears to be dominated by an exponential decrease with increasing particle size. The fitting method is tested on simulated data and then on Intralipid-based tissue-phantom data, giving a mean particle size of 93±17 nm, which is in excellent agreement with expectations. The approach is also applied retrospectively to breast tissue spectra acquired from normal healthy volunteers, where the average particle size and number density were found to be in the range of 20 to 1400 nm. Grouping of the data based on radiographic breast density, as a surrogate measure of tissue composition yielded values of 20 to 65, 25 to 200, 140 to 1200, and 150 to 1400 nm, respectively, for the four BI-RADS (American College of Radiology Breast Imaging Reporting and Data System) density classifications of extremely dense, heterogeneously dense, scattered, and fatty. These results are consistent with the microscopic characteristics of each breast type given the expected progression from predominantly collagenous connective tissue (extremely dense category) to increasing proportions of glandular epithelium and fat (intermediate density categories) to predominantly fat (fatty category).
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