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Shudong Jiang

Publications and source records attributed to Shudong Jiang.

21 records · Page 2Linked to original sources

Approximation of Mie scattering parameters in near-infrared tomography of normal breast tissue in vivo.

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).

Breast↗

Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure.

An imaging system that simultaneously performs near infrared (NIR) tomography and magnetic resonance imaging (MRI) is used to study breast tissue phantoms and a healthy woman in vivo. An NIR image reconstruction that exploits the combined data set is presented that implements the MR structure as a soft-constraint in the NIR property estimation. The algorithm incorporates the MR spatially segmented regions into a regularization matrix that links locations with similar MR properties, and applies a Laplacian-type filter to minimize variation within each region. When prior knowledge of the structure of phantoms is used to guide NIR property estimation, root mean square (rms) image error decreases from 26 to 58%. For a representative in vivo case, images of hemoglobin concentration, oxygen saturation, water fraction, scattering power, and scattering amplitude are derived and the properties of adipose and fibroglandular breast tissue types, identified from MRI, are quantified. Fibroglandular tissue is observed to have more than four times as much water content as adipose tissue, almost twice as much blood volume, and slightly reduced oxygen saturation. This approach is expected to improve recovery of abnormalities within the breast, as the inclusion of structural information increases the accuracy of recovery of embedded heterogeneities, at least in phantom studies.

Breast↗

Image reconstruction of effective Mie scattering parameters of breast tissue in vivo with near-infrared tomography.

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.

Algorithms↗