RI is dependent on the interaction between electromagnetic waves and tissue. The scattering properties, color and image quality depend on the differences in RI among the components of the tissue (3). In disease states, such as neoplasm or inflammation, color of tissue changes due to the change in RI, which in turn is related to the dielectric constant (ɛ_r) or relative permittivity of the tissue (4). The dielectric constant changes as a result of changes in electron density of the tissue. The electron density of the tissue changes due to the derangement of the electron transport system of the inner mitochondrial membrane (5). Several studies have demonstrated that cells exposed to hypoxic conditions (1.5% O₂) produce increased levels of reactive O₂ species (ROS) derived from the mitochondrial electron transport chain (mt ROS). The generation of mt ROS under hypoxia is likely to alter intracellular redox status and glutathione (GSH) levels (6).

The role of oxidative stress in the genesis of various types of tumors, particularly in malignancy, is well-established. Several chemical, cell culture, and animal studies indicate that antioxidants such as GSH may slow or even prevent the development of malignant tumors (7). The brain is considered abnormally sensitive to oxidative damage, since brain tissue has a high rate of oxygen consumption, high lipid content, and relatively low antioxidant defense, compared to other tissues. GSH is a tripeptide (L glutamyl Lcysteinyl glycine) and a potent antioxidant (8). Selenium-dependent glutathione peroxidase (GPx) plays a protective role in oxidative stress-induced apoptosis.

Particularly with the help of menadione (VitK3), it is believed that GPx activates the electron transport system and increases intracellular superoxide radicals (9). In the white matter of

normal brain tissue, the GSH content is 1.2+0.15 mM (910)). An increase in GSH levels is found in meningioma (1.5 to 3mM) (11). However diminished amounts of GSH are found in gliomas and astrocytomas (0.78 to 1 mM). It is found the degree of malignancy is inversely proportional to GSH levels. GSH is important in maintaining a normal value of RI, as shown in the lens of the eye (12). Normal RI value of gray matter is 1.395 and that of white matter is 1.412 (13). In our study we have found that tissues whose RI value deviated from the above normal values were found to be abnormal. Thus, in vivo measurements of the RI of a brain lesion may replace the hazards of stereotactic biopsy.

RI is related to the dielectric constant of tissue, which in turn depends upon electron density (ED) of the tissue and wavelength or optical frequency (f) of its color (14). We have developed a novel, noninvasive in vivo technique to determine in human the RI value of gray matter, white matter, brain tumors, and other brain lesions from colored CT-MR fusion images. Using image registration and histogram-matching techniques, computer-generated color images can be produced from the MRI (15). MR images are superior to CT images in showing anatomical features, while CT provides important information about the electron density of the tissue. CT Number is also incorporated in the calculation of CT-MR fusion image pixel by registration technique. The incorporation of data of multiple image modalities offers the potential to improve diagnosis. This multi-parametric approach is explained in the image processing section.

According to Richard Feynman (16), RI can be calculated using the following equation:

Where,

Figure 1

ɷ^>2 = 4π2f² (Hz²)

f = optical frequency (Hz)

ɷ =angular frequency of the electromagnetic radiation = 2 π f (Hz)

ED = Electron density (number of electrons per unit volume of the material /m³)

e= Charge of electron =1.602X10^-19 coulomb (c),

m=mass of electron = 9.10938188 × 10^-31 kg,

ɛ₀ = dielectric constant in relation to vacuum = 8.85X10^-12c²s²/kgm³

ɷ₀ = resonant frequency of an electron bound in an atom of the tissue =3.74 × 10¹⁶ Hz.

The altered RI value of a tumor or lesion can be mathematically predicted from different datasets, such as the electron density of the lesion, derived from the CT Number of the image pixel, and optical frequency, determined by the color of the lesion. From the predicted RI value, the dielectric constant of the tissue (ɛ_r) can also be calculated, which is the square of RI value (ɛ _r=RI² )

Relationship of refractive index to mass density of tissue (RI–density relationship) and consistency of the tissue are also important. Combined empirical and laboratory analyses show that a denser material (tissue) generally tends to have a larger RI because the applied electric field induces a greater number of electric dipoles, and that the RI–density relationship can be described reasonably well by the Lorentz–Lorenz relation (17).

CT Number is the ratio of difference of linear attenuation coefficients of tissue (μs) and water (μw) to the linear attenuation coefficient of water multiplied by 1000; thus:

Figure 3

The linear attenuation coefficient of a material describes the fraction of a beam of x-rays or gamma rays that is absorbed or scattered per unit thickness of the material.

The linear attenuation coefficient depends on the photon energy, chemical composition and physical density of the material (18).

The CT Numbers for normal brain tissue, as well as for various brain tumors and lesions, were determined in a conventional third generation CT scanner (Wipro-GE, India, Sytec 1800i). These CT Number values were compared with the gray value of the CT images, using a matrix size of 512 × 512, an in plane resolution of 0.5 mm, and a slice thickness of 5 mm. ED of normal and abnormal gray white matter were determined using standard mathematical expressions (19,20)directly from the CT Number of the image pixel of CT image. With an average energy in between 50 and 75keV, approximately 91% of the X-ray interactions are Compton Scatter. Therefore, the CT Numbers and CT images derive their contrast mainly from the physical properties of tissue that influence Compton Scatter ( Density of tissue (gm cm^-3) plays an important role in discriminating property of tissue. The linear attenuation coefficient (μ has a linear relationship with the density of tissue. Relative electron density is the ratio of ED of the tissue to the ED of water. Thus water with a physical density of 1 and ED of 3.340X10¹⁵ /m³ has a relative electron density of 1. For a CT image of a mass or lesion in the brain containing unknown materials, we calculate the relative electron density for each pixel using “relative electron density-CT Number relations curve” (figure1) provided by Matsufuji et al (20) and Watanabe (18). ED of the tissue in question can be obtained by multiplying relative electron density by 3.340X10²³ cm^-3.

Figure 4

MRI scan was performed initially on a 0.3 Tesla Hitachi ARIS II for all the cases using a quadrature head coil. ¹H MRS data were acquired using a 3 Tesla Siemens Magnetom Trio MR Scanner for the suspected pathological cases. The MR imaging sequences of these studies included axial T1 weighted (TR/TE:450/15), T2 weighted (4500/120), FLAIR (8500/120), sagittal FLAIR, and coronal T2 weighted, with a matrix size of 256 × 256 × 48 and a pixel size of 1 × 1 × 3 mm. For identifying regions of active tumor or disease the sensitivity and specificity of these images may not be optimal as the invading margin may remain invisible. Patients underwent single voxel point-resolved MR spectroscopy [PRESS] at short TE (30ms) and long TE (135ms) and MR Spectroscopic Imaging (MRSI) (PRESS) at TR/TE 3000/135) before operation to identify region of abnormal metabolism (22,23,24,25,26,27) that corresponds pathology of the tumor/mass anatomy in the colored CT and MR fusion image. This was also helpful to compare with the RI value determined from the image. We have avoided contrast study as contrast enhancement denotes abnormal blood brain barrier and the region of solid active tumor may not be enhanced. For MRSI the region of tissue studied was chosen to include most of the pathology, surrounding normal tissue avoiding bone and subcutaneous fat, and contra lateral tissue.

Fusion of CT and MR images - Image Processing Technique

Image colorization was described below. The positions, orientations and gray scale values(out of 256 shades) of T1-weighted, inverse T2-weighted and FLAIR images as well as the digital images of the corresponding colored cross-section of brain were aligned using a rigid-body registration technique (figure 2C). Independent component analysis (ICA) basis was then applied to the registered MRI data sets of gray value of tissues as x 1, x 2 x 3 to obtain statistically independent components of corresponding color values , s 1, s 2 and s 3 (figure 2A and B)(28). This operation transformed the dataset of image pixel components (gray value) into color components (red, green, blue value) that characterized the physical features of gray and white matters, blood, CSF with their original color. Table 2 shows the gray value and corresponding color value of the tissue out of 256 shades

Figure 5

TABLE-2.Gray and corresponding color values of various tissue determined by Independent Component Analysis

A radial basis function (RBF) network, a kind of neural network, was then used to generate the mapping function that produced correspondences between these independent components of gray value and the color components of the full color cross-section data (figure 2E). The training samples for the neural network were selected using the K-mean clustering algorithm (28) in the software Color Converter^©.

Figure 6

In image analysis the purpose of the K-mean clustering algorithm was to classify the objects in data matrix based on some selected attributes. A color shade scale was applied which determines the wavelength in nanometers from the images and in turn optical frequency of the lesion was determined from the equation f = C/λ, where, C = velocity of light =3 × 10¹⁰ cm, λ = wave length in cm.

The RI values of biopsy samples of gray matter, white matter and pathological tissue were (figure 3 F.G.H) independently determined using two methods: 1) by Abbe refractometer and 2) by ellipsometer. RI values of relatively thin tissues of 2 mm were determined by Abbe refractometer and thicker (>2mm) tissues were performed with the ellipsometer.

Figure 7

Postoperative tissue specimens collected in normal saline were preserved in –80°c in the laboratory. After proper thawing of the tissue to room temperature

(20°c), a small sample was sandwiched between the illuminating prism and the refracting prism of an Abbe refractometer (Suprashes Model AAR-33).

The refracting prism of the Abbe refractometer has a high refractive index (RI= 1.75). The samples have a smaller refractive index than the refracting prism. A light source (589.3 D line) is projected through the illuminating prism. A borderline between a dark and light zones was placed over the central point of the “cross wire” of the prism by adjusting the knob of the refractometer. On a calibrating scale, the position of the borderline indicated the refractive index of the tissue. To eliminate any operator bias, five readings were taken to find the mean RI value.

For thicker biopsy material (more than 2mm) RI value and dielectric constant of thick tissue were determined with an ellipsometer, model Alpha-SE^® (USA). It consisted of a laser beam (a 632.8 nm helium/neon laser), a polarizer, and a quarter wave plate, in which the sample was kept. The instrument generated polarized light, which could be set to linearly polarized light to elliptically polarized light or circularly polarized light depending on the angle of the polarizer. The beam was reflected off the layer of interest of the sample and then detected for analysis.

The angle of the polarizer and analyzer were adjusted until a minimal signal was detected. This minimum detected signal was linearly polarized, while the analyzer was set so that only light with a polarization perpendicular to the incoming polarization was allowed to pass. The ellipsometer would then determine the dielectric constant and RI value of the biopsy material.

Identification of low concentration of GSH in ¹H MRS requires spectral editing techniques. Due to the difficulty in quantifying GSH by ¹HMRS, GSH was measured in the biopsy sample using the method of Sedlack and Lindsay (29). Cell homogenates (1 × 10⁶ cells) were prepared from samples of various biopsy materials using 0.15 M KCl and centrifuged at 10,000 rpm for 20 min at 4°C. Then 0.2 ml KCl and 4.8 ml EDTA (0.2M) were added to the mixture. The mixture was kept on ice for 10 minute. Deionised water and 1 ml of 5% trichloroacetic acid (TCA) were then added. The mixture was again kept on ice for 10 minute and centrifuged at 3000 rpm for 15 minute. Two ml of supernatant solution was taken, and 4 ml of 0.4 M tris buffer (pH 8.9) was added. One ml of 0.01 M 5, 5-Dithio bis (2-nitrobenzoic acid) (DTNB) solution was then added and solution vortexed thoroughly. Optical density (OD) was read (within 2 to 3 min after addition of DTNB) at 412 nm against a reagent blank.

GSH concentrations of samples were obtained by plotting OD values against a standard GSH curve (figure 4)

Desiccation method was used for the estimation of water content of biopsy materials. In this method, the tissue samples were first weighed in a digital balance, then cut into small pieces and subsequently dried at 40°C to constant weight (Table3).

Concentration of water: Weight / Weight percent (w/w %) was calculated as follows:

Figure 8

Figure 9

TABLE-3.Various normal and pathological tissues with electron density, optical Frequency, RI values and water content

Physical density of the tissue in question was derived by measuring mass (m) and volume (v). With the help of a digital balance, the weight of the tissue specimen was determined by immersing the specimen into a water filled calibrated cylinder and noting the displaced volume. Mass density (ρ) was calculated as m/v.