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Magnetic resonance (MR)-guided near-infrared spectral tomography was developed and used to image adipose and fibroglandular breast tissue of 11 normal female subjects, recruited under an institutional review board-approved protocol. Images of hemoglobin, oxygen saturation, water fraction, and subcellular scattering were reconstructed and show that fibroglandular fractions of both blood and water are higher than in adipose tissue. Variation in adipose and fibroglandular tissue composition between individuals was not significantly different across the scattered and dense breast categories. Combined MR and near-infrared tomography provides fundamental molecular information about these tissue types with resolution governed by MR T1 images.

Multi-wavelength Near-Infrared (NIR) Tomography was utilized in this study to non-invasively quantify physiological parameters of breast tumors using direct spectral reconstruction. Frequency domain NIR measurements were incorporated with a new spectrally constrained direct chromophore and scattering image reconstruction algorithm, which was validated in simulations and experimental phantoms. Images of total hemoglobin, oxygen saturation, water, and scatter parameters were obtained with higher accuracy than previously reported. Using this spectral approach, in vivo NIR images are presented and interpreted through a series of case studies (n=6 subjects) having differing abnormalities. The corresponding mammograms and ultrasound images are also evaluated. Three of six cases were malignant (infiltrating ductal carcinomas) and showed higher hemoglobin (34–86% increase), a reduction in oxygen saturation, an increase in water content as well as scatter changes relative to surrounding normal tissue. Three of six cases were benign, two of which were diagnosed with fibrocystic disease and showed a dominant contrast in water, consistent with fluid filled cysts. Scatter amplitude was the main source of contrast in the volunteer with the benign condition fibrosis, which typically contains denser collagen tissue. The changes monitored correspond to physiological changes associated with angiogenesis, hypoxia and cell proliferation anticipated in cancers. These changes represent potential diagnostic indicators, which can be assessed to characterize breast tumors.

The design and implementation of a multispectral, frequency-domain near infrared tomography system is outlined, which operates in a MRI magnet for utilization of MR-guided image reconstruction of tissue optical properties. Using long silica optical fiber bundles, measurements of light transmission through up to 12 cm of female breast tissue can be acquired simultaneously with MRI scans. The NIR system utilizes six optical wavelengths from 660 to 850 nm using intensity modulated diode lasers nominally working at 100 MHz. Photomultiplier tube detector gain levels are electronically controlled on a time scale of 200 ms, thereby allowing rapid switching of the source to locations around the tissue. There are no moving parts in the detection channels and for each source position, 15 PMTs operating in parallel allow sensitivity down to 0.5 pW/cm2 at the tissue surface. Images of breast tissue optical absorption and reduced scattering coefficients are obtained using a Newton-type reconstruction algorithm to solv...

Near-infrared (NIR) tomography is a technique used to measure light propagation through tissue and generate images of internal optical property distributions from boundary measurements. Most popular applications have concentrated on female breast imaging, neonatal and adult head imaging, as well as muscle and small animal studies. In most instances a highly scattering medium with a homogeneous refractive index is assumed throughout the imaging domain. Using these assumptions, it is possible to simplify the model to the diffusion approximation. However, biological tissue contains regions of varying optical absorption and scatter, as well as varying refractive index. In this work, we introduce an internal boundary constraint in the finite element method approach to modelling light propagation through tissue that accounts for regions of different refractive indices. We have compared the results to data from a Monte Carlo simulation and show that for a simple two-layered slab model of varying refractive index, the phase of the measured reflectance data is significantly altered by the variation in internal refractive index, whereas the amplitude data are affected only slightly.

A combined magnetic resonance and near-infrared (MRI-NIR) imaging modality can potentially yield high resolution maps of optical properties from noninvasive simultaneous measurement. The main disadvantage of near-infrared (NIR) tomography lies in the low spatial resolution resulting from the highly scattering nature of tissue for these wavelengths. MRI has achieved high resolution, but suffers from low specificity. In this study, NIR image reconstruction algorithms that incorporate a priori structural information provided by MRI are investigated in an attempt to optimize recovery of a simulated optical property distribution. The effect of high levels of tissue heterogeneity are evaluated to determine the limitations of incorporating prior information into a realistic set of patient breast images. We assume absorption coefficient (/spl mu//sub a/) variations near /spl plusmn/40%, and transport scattering coefficient (/spl mu//sub s//sup //) variations near /spl plusmn/20%, in a coronal breast MRI geometry. Changes in tissue pathology due to tumor growth can be observed with NIR tompgraphy, and so the goal here is to determine how best to quantify these tumor-based contrast regions within the presence of high tissue heterogeneity. By applying knowledge of tissue's layered structure in reconstruction through various constraints in the iterative algorithm, quantitative recovery of the tumor optical properties improves from 69% to 74%, and localization improves as well. However, only when the true heterogeneity of the tissue distribution was included was accurate quantification of the tumor region possible. Using a good initial guess of /spl mu//sub a/ and /spl mu//sub s//sup //, derived from the regional structure of the model, quantification of the region reaches 99% of the true value, and spatial resolution retains a similar value to the original MRI image.

An imaging system which simultaneously performs near infrared (NIR) tomography and magnetic resonance imaging (MRI) has been developed at Dartmouth College, to study breast tissue of women in vivo. A NIR image reconstruction technique which exploits the combined multi-wavelength data set is presented which implements the MR structure as a soft-constraint in the NIR property estimation. The benefits of spatial and spectral priors, applied independently and together, in NIR diffuse tomography image reconstruction of in vivo measurements are presented. When both spatial and spectral priors are applied in a healthy volunteer, glandular tissue shows higher total blood content, water, and scattering power compared to fatty tissue.

We compare the benefits of spatial and spectral priors in near-infrared diffuse tomography image reconstruction. Although previous studies that incorporated anatomical spatial priors have shown improvement in algorithm convergence and resolution, our results indicate that functional parameter quantification by this approach can be suboptimal. The incorporation of a priori spectral information significantly improves the accuracy observed in recovered images. Specifically, phantom results show that the maximum total hemoglobin concentration ([Hb(T)]) in a region of heterogeneity reached 91% of the true value compared to 63% using spatial priors. The combination of both priors produced results accurate to 98% of the true [Hb(T)]. When both spatial and spectral priors were applied in a healthy volunteer, glandular tissue showed a higher [Hb(T)], water fraction, and scattering power compared to adipose tissue.

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.

A multi-spectral, frequency-domain near infrared tomography system has been constructed and evaluated. Measurements of light transmission through female breast can be acquired simultaneously with MRI scans.

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.

Near infrared (NIR) image reconstruction can be improved using a-priori structural knowledge provided by MRI. In this work we show that combining these modalities and optimizing the NIR reconstruction algorithm improves recovery of internal optical property distributions.

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.

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.

Bioluminescenceimaging is a powerful technique for visualizing gene expression in small animals but it suffers a serious limitation: the absorption and scattering of light in tissue. Several factors influence the image: source strength and depth, effective numerical aperture of the imaging optics, and attenuation by the tissue between the source and the camera. Our overall goal is to account for these effects and to recover the actual strength and spatial location of the bioluminescence sources in vivo. An essential first step in this research is to develop a physical model that accurately predicts the light reaching the surface of the animal for an arbitrary distribution of sources and optical absorption and scattering coefficients. The calculations must be fast, so that the model can be used eventually in an iterative algorithm to solve the inverse problem. We use the diffusion approximation, valid when scattering dominates absorption and when it is not necessary to calculate the light field close to sources. The diffusion equation expresses the light fluence rate as a function of position and the spatially dependent absorption coefficient, scattering coefficient, and source function. A finite element code called NIRFAST has been developed to generate numerical solutions. NIRFAST has been implemented in MATLAB and uses a 2 or 3 dimensional model to represent the object. The absorption and scattering coefficients are specified at each node of the mesh. We have assigned “reasonable” values of the absorption and scattering coefficients to each node based on tissue identification by x‐ray CT.

Near infrared (NIR) optical tomography is an imaging technique in which internal images of optical properties are reconstructed with the boundary measurements of light propagation through the medium. Recent advances in instrumentation and theory have led to the use of this method for the detection and characterization of tumors within the female breast tissue. Most image reconstruction approaches have used the diffusion approximation and have assumed that the refractive index of the breast is constant, with a bulk value of approximately 1.4. We have applied a previously reported modified diffusion approximation, in which the refractive index for different tissues can be modeled. The model was used to generate NIR data from a realistic breast geometry containing a localized anomaly. Using this simulated data, we have reconstructed optical images, both with and without correct knowledge of the refractive-index distribution to show that the modified diffusion approximation can accurately recover the anomaly given a priori knowledge of refractive index. But using a reconstruction algorithm without the use of correct a priori information regarding the refractive-index distribution is shown as recovering the anomaly but with a degraded quality, depending on the degree of refractive index mismatch. The results suggest that provided the refractive index of breast tissue is approximately 1.3-1.4, their exclusion will have minimal effect on the reconstructed images.

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.

Near-infrared (NIR) optical tomography can provide estimates of the internal distribution of optical absorption and transport scattering from boundary measurements of light propagation within biological tissue. Although this is a truly three-dimensional (3D) imaging problem, most research to date has concentrated on two-dimensional modeling and image reconstruction. More recently, 3D imaging algorithms are demonstrating better estimation of the light propagation within the imaging region and are providing the basis of more accurate image reconstruction algorithms. As 3D methods emerge, it will become increasingly important to evaluate their resolution, contrast, and localization of optical property heterogeneity. We present a concise study of 3D reconstructed resolution of a small, low-contrast, absorbing and scattering anomaly as it is placed in different locations within a cylindrical phantom. The object is an 8-mm-diameter cylinder, which represents a typical small target that needs to be resolved in NIR mammographic imaging. The best resolution and contrast is observed when the object is located near the periphery of the imaging region (12–22 mm from the edge) and is also positioned within the multiple measurement planes, with the most accurate results seen for the scatter image when the anomaly is at 17 mm from the edge. Furthermore, the accuracy of quantitative imaging is increased to almost 100% of the target values when a priori information regarding the internal structure of imaging domain is utilized.