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.
Three major analytical tools in imaging science are summarized and demonstrated relative to optical imaging in vivo. Standard resolution testing is optimal when infinite contrast is used and hardware evaluation is the goal. However, deep tissue imaging of absorption or fluorescent contrast agents in vivo often presents a different problem, which requires contrast-detail analysis. This analysis shows that the minimum detectable sizes are in the range of 1/10 the outer diameter, whereas minimum detectable contrast values are in the range of 10 to 20% relative to the continuous background values. This is estimated for objects being in the center of the domain being imaged, and as the heterogeneous region becomes closer to the surface, the lower limit on size and contrast can become arbitrarily low and more dictated by hardware specifications. Finally, if human observer detection of abnormalities in the images is the goal, as is standard in most radiological practice, receiver operating characteristic (ROC) curve and location receiver operating characteristic curve (LROC) are used. Each of these three major areas of image interpretation and analysis are reviewed in the context of medical imaging as well as how they are used to quantify the performance of diffuse optical imaging of tissue.
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.
Hybrid NIR-MRI imaging has been used in a clinical breast imaging system to characterize breast tissue properties. The multi-spectral, frequency-domain tomography system operates inside a clinical scanner via long silica-glass optical fiber bundles and using a non-magnetic fiber-patient interface attached to a high resolution MR breast coil. Sixteen fiber bundles are positioned around the circumference of the female breast yielding 240 measurements of light transmission (amplitude and phase) at six optical wavelengths from 660-850nm through up to 12 cm of tissue. From optical measurements, we use a Newton-type algorithm to reconstruct images of tissue optical properties (absorption and scattering), and physiological tissue features such as oxy-hemoglobin [Hb-O2], deoxy-hemoglobin concentrations [Hb-R], water concentration [water], scattering amplitude, and scattering power. We are exploring the synergistic benefits of a combined NIR-MRI data set, specifically the ways in which MRI (i.e. high spatial resolution) can be used to enhance NIR (i.e. high contrast resolution) image reconstruction. A priori knowledge can be applied to image reconstruction in the form of spatial and spectral constraints to improve spatial resolution, contrast, and quantitative accuracy of NIR images. In vivo results suggest that this combined system can accurately quantify contrast between the properties of tissues present in the breast (i.e. adipose and fibroglandular) regardless of their varied and complex spatial organization. For a group of healthy female volunteers, we observe greater contrast between the properties of adipose and glandular tissues when we use MR-guidance than when we do not, and values of total hemoglobin and water content are more consistent with what is physiologically expected.
This investigation explores the effect of index of refraction, as an optical property, on light transport through optically turbid media. We describe a model of light propagation that incorporates the influence of refractive index mismatch at boundaries within a domain. We measure light transmission through turbid cylindrical phantoms with different distributions of refractive index. These distributions approximate the heterogeneous, layered nature of biological tissue. Finite element method model calculations of diffuse transmittance through these phantoms show good agreement with the trends observed experimentally. We see that phase measurements of light that propagates through approximately 90 (mm) of scatter-dominated media may vary by 10 degrees depending upon the values of refractive index of the medium. Amplitude measurements are not as sensitive to this parameter as phase. Model calculations of diffuse reflectance from a semi-infinite slab geometry with different layers also shows good agreement with Monte Carlo simulations. We conclude that incorporating refractive index into light transport models may be worthwhile. Applying such a model in tomographic image reconstruction may improve the estimation of optical properties of biological tissues.
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