Near-infrared Tomography Research Articles

Automated region detection based on the contrast-to-noise ratio in near-infrared tomography.

The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.

The effects of internal refractive index variation in near-infrared optical tomography: a finite element modelling approach

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.

In vivo near-infrared spectral detection of pressure-induced changes in breast tissue.

A diffuse near-infrared tomography system was used to measure dynamic changes in the absolute optical properties of the human breast that were induced through pressure applied to the tissue surface. Results from five subjects show that absorption and scattering coefficients changed measurably when pressure was increased and that these relative changes correlated with the subjects' body-mass index, indicating that the effect depends on tissue composition. Fitting the absolute absorption and scattering coefficients at six wavelengths to the molar absorption spectra of the three predominant chromophores revealed that both the average total hemoglobin and oxygen saturation increased by 10%, while water concentration decreased by more than 12%. These changes indicate that the pressure-induced variation is likely due to water displacement and vascular volume increase in the region being imaged, for mild application of pressure to the breast. These results suggest that the pressure applied during optical measurements of tissue may alter the tissue physiology, and care should be taken to factor this effect into the design of optical medical instrumentation. In addition, the technique provides a unique approach to measuring tissue elastic changes in vivo in the female breast and may offer a new method for dynamic contrast imaging based on elasto-optical measurements.

Near-infrared (nir) tomography breast image reconstruction with a priori structural information from mri: algorithm development for reconstructing heterogeneities

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 (μ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> ) variations near ±40%, and transport scattering coefficient (μ/sub s//sup //) variations near ±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 μ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> and μ/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.

Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results.

Three-dimensional (3D), multiwavelength near-infrared tomography has the potential to provide new physiological information about biological tissue function and pathological transformation. Fast and reliable measurements of multiwavelength data from multiple planes over a region of interest, together with adequate model-based nonlinear image reconstruction, form the major components of successful estimation of internal optical properties of the region. These images can then be used to examine the concentration of chromophores such as hemoglobin, deoxyhemoglobin, water, and lipids that in turn can serve to identify and characterize abnormalities located deep within the domain. We introduce and discuss a 3D modeling method and image reconstruction algorithm that is currently in place. Reconstructed images of optical properties are presented from simulated data, measured phantoms, and clinical data acquired from a breast cancer patient. It is shown that, with a relatively fast 3D inversion algorithm, useful images of optical absorption and scatter can be calculated with good separation and localization in all cases. It is also shown that, by use of the calculated optical absorption over a range of wavelengths, the oxygen saturation distribution of a tissue under investigation can be deduced from oxygenated and deoxygenated hemoglobin maps. With this method the reconstructed tumor from the breast cancer patient was found to have a higher oxy-deoxy hemoglobin concentration and also a higher oxygen saturation level than the background, indicating a ductal carcinoma that corresponds well to histology findings.

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.

Fréchet Derivatives for Some Bilinear Inverse Problems

In many inverse problems a functional of u is given by measurements, where u solves a partial differential equation of the type L(p)u+Su=q. Here q is a known source term, and L(p), S are operators, with p as an unknown parameter of the inverse problem. For the numerical reconstruction of p, the heuristically derived Fréchet derivative R' of the mapping $R:p\rightarrow$ "measurementfunctional of u" is often used. We show for three problems---a transport problem in optical tomography, an elliptic equation governing near-infrared tomography, and the wave equation in moving media---that R' is the derivative in the strict sense. Our method is applicable to more general problems than are established methods for similar inverse problems.

A parallel-detection frequency-domain near-infrared tomography system for hemoglobin imaging of the breast in vivo

A novel near-infrared frequency-domain system designed for tomographic breast imaging is described. The setup utilizes five optical wavelengths, from 660 to 826 nm, and parallel detection with 16 photomultiplier tubes. Direct fiberoptic coupling with the tissue is achieved with a high precision positioning device using 16 motorized actuators (0.5 μm precision) arranged radially in a circular geometry. Images of breast tissue optical absorption and reduced scattering coefficients are obtained using a Newton-type reconstruction algorithm to solve for the optimal fit between the measurement data and predicted data from a finite element solution to the frequency-domain diffusion equation. The design, calibration, and performance of the tomographic imaging system are detailed. Data acquisition from the system requires under 30 s for a single tomographic slice at one optical wavelength with a measurement repeatability for a single phantom on average of 0.5% in ac intensity and 0.4° in phase. Absorbing and scattering phantoms typical of in vivo breast optical properties were imaged over a range of object-to-background contrasts (50%–250%). The reconstructed absorption and scattering images are accurate to within 20% of the expected value.

New instrument for monitoring hemoglobin oxygenation.

A new compact high-performance instrument for monitoring the oxygenation state of the human newborn are developed. First we applied this instrument to the rat head, and next to the human arm. We could confirm that the volume of Oxy-Hb, Deoxy-Hb and total blood volume were well monitored by using our instrument. Now we started to apply this technique to the human newborn. Furthermore, we started the experiments of the near-infrared tomography for the small animals on the basis of this technique.