System and method for functional brain mapping and an oxygen saturation difference map algorithm for effecting same

A method of functional brain mapping of a subject is disclosed. The method is effected by (a) illuminating an exposed cortex of a brain or portion thereof of the subject with incident light; (b) acquiring a reflectance spectrum of each picture element of at least a portion of the exposed cortex of the subject; (c) stimulating the brain of the subject; (d) during or after step (c) acquiring at least one additional reflectance spectrum of each picture element of at least the portion of the exposed cortex of the subject; and (e) generating an image highlighting differences among spectra of the exposed cortex acquired in steps (b) and (d), so as to highlight functional brain regions. Algorithms for calculating oxygen saturation and blood volume maps which can be used to practice the method are also disclosed. Systems for practicing the method are also disclosed.

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention is of systems and methods for functional brain mapping which can be used during neurosurgeries and further of novel oxygen saturation and/or blood volume maps and novel oxygen saturation and/or blood volume difference maps which can be used for effecting same. Specifically, the present invention can be used to acquire high spectral and spatial resolution spectral images of an exposed cortex during a neurosurgery, while using peripheral or direct, voluntary or passive, brain stimulation protocols for mapping functional cortical regions and thereby deducing cortical anatomy in real time, especially in cases of distorted anatomy, as is typically the case when a space-occupying lesion, e.g., a brain tumor, distorts neighboring brain tissue. Further specifically, the present invention can be used to generate and display oxygen saturation and/or blood volume maps and oxygen saturation and/or blood volume difference maps of any tissue, highlighting differences in the oxygen saturation and/or blood volume characterizing the tissue between two or more time points. Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting. According to one aspect of the present invention there is provided a method of functional brain mapping of a subject. The method according to this aspect of the invention is effected by implementing the following method steps. Throughout steps of spectral data acquisition, the cortex or a portion thereof is illuminated with incident light. The light can be white light or filtered light. The light is preferably a cold light. If hot light is employed, lighting durations should be minimized, so as to avoid heat induced damage to the exposed brain. Presently, without limitation, regulated halogen light is preferred. While lighting the cortex as described, a reflectance spectrum of each picture element of at least the portion of the exposed cortex is acquired. Thereafter, the brain is stimulated through, for example, the peripheral nervous system of the subject, and during and/or after the stimulation, at least one additional reflectance spectrum of each picture element of at least the portion of the exposed cortex is acquired. Finally, an image highlighting differences among spectra of the exposed cortex so as to highlight functional brain regions is generated. The above method can be implemented while performing a neurosurgery for the removal of a mass (of tissue) from a brain of a subject while minimizing damage to a neighboring brain tissue. To this end, a craniotomy is performed so as to expose at least a portion of a cortex of the subject. Thereafter, functional brain mapping of the subject is performed essentially as described above, i.e., by (i) illuminating the exposed portion of the cortex with incident light; (ii) acquiring a reflectance spectrum of each picture element of at least a portion of the exposed cortex of the subject; (iii) stimulating the neighboring brain tissue of the subject (e.g., inducing brain activity); (iv) during or after step (iii) acquiring at least one additional reflectance spectrum of each picture element of at least the portion of the exposed cortex of the subject; and (v) generating an image highlighting differences among spectra of the exposed cortex acquired in steps (ii) and (iv), so as to highlight the functional brain regions of the neighboring brain tissue. Finally, assisted by the image, the mass is surgically removed while minimizing damage to the neighboring brain tissue. The brain mass can be a brain tumor, either benign or malignant tumor, or the brain mass can be a brain tissue removed in order to treat neurologic (e.g., epilepsy) or phsichotic disorders (e.g., lobotomy). As is shown in FIG. 6 , according to another aspect of the present invention there is provided a system 400 for functional brain mapping of a subject. The system includes an illumination device 402 which serves for illuminating an exposed cortex or portion thereof with incident light. The illumination device may form an integral part of the system or may be a stand-alone device). Illumination device 402 preferably includes a plurality of individual light sources 404 arranged and directed so as to provide substantially homogenous lighting of the exposed cortex, each of which may include a wide band filter 416 , which serves for restricting light wavelengths to a predetermined range, so as to reduce noise. System 400 further includes a spectral imaging device 406 which serves for acquiring reflectance spectra of each picture element of at least a portion of the exposed cortex before and during and/or after stimulating the brain (e.g., inducing brain activity). The optics of device 406 may vary as is further detailed hereinunder, however, in all of its configurations, device 406 includes an objective lens or other type of fore optics 408 which serves to direct light into device 406 and a light intensity recording device 410 , such as a charge coupled device (CCD), which serves for data acquisition. Depending on the specific application, a wide band filter 418 can be used to restrict the wavelength of the incoming light as desired. System 400 further includes an image generating device 412 which serves for generating an image highlighting differences among spectra of the exposed cortex acquired before and during and/or after stimulating the brain of the subject, so as to highlight functional brain regions. Device 412 is typically connected to a display 414 which serves to display the results. Device 412 can be a suitable computer such as a personal computer equipped and designed to execute certain algorithms, which would result in generating and displaying an image highlighting differences among spectra of the exposed cortex acquired before and during and/or after stimulating the brain of the subject, so as to highlight functional brain regions. Being a computer, certain functions of device 406 , which functions are related to data acquisition are also executed by device 412 , although a separate computational platform can be used to this end. Thus, device 412 , is preferably an integrated device which is used for performing a number of tasks related to both spectral imaging data acquisition per se and to the analysis and presentation of the results thereof. The following provides several alternative configurations for spectral imaging device 406 , one alternative relates to interferometer-based spectral imaging devices, whereas the other relates to filters-based spectral imaging devices. 
 Interferometer-based Spectral Imaging Devices FIG. 7 is a block diagram illustrating the main components of a prior art imaging spectrometer disclosed in U.S. Pat. No. 5,539,517, which is incorporated by reference as if fully set forth herein. This imaging spectrometer is constructed highly suitable to implement the method of the present invention as it has high spectral (Ca. 4-14 nm depending on wavelength) and spatial (Ca. system MTF (modulation transfer function, e.g., 30)/M &mgr;m, where M is the effective fore optics magnification) resolutions. Thus, the prior art imaging spectrometer of FIG. 6 includes: a collection optical system, generally designated 20 ; a one-dimensional scanner, as indicated by block 22 ; an optical path difference (OPD) generator or interferometer, as indicated by block 24 ; a one-dimensional or two-dimensional detector array, as indicated by block 26 ; and a signal processor and display, as indicated by block 28 . A critical element in system 20 is the OPD generator or interferometer 24 , which outputs modulated light corresponding to a predetermined set of linear combinations of the spectral intensity of the light emitted from each picture element of the scene to be analyzed. The output of the interferometer is focused onto the detector array 26 . Thus, all the required optical phase differences are scanned simultaneously for all the picture elements of the field of view, in order to obtain all the information required to reconstruct the spectrum. The spectra of all the picture elements in the scene are thus collected simultaneously with the imaging information, thereby permitting analysis of the image in a real-time manner. The apparatus according to U.S. Pat. No. 5,539,517 may be practiced in a large variety of configurations. Specifically, the interferometer used may be combined with other mirrors as described in the relevant Figures of U.S. Pat. No. 5,539,517. Thus, alternative types of interferometers may be employed. These include (i) a moving type interferometer in which the OPD is varied to modulate the light, namely, a Fabry-Perot interferometer with scanned thickness; (ii) a Michelson type interferometer which includes a beamsplitter receiving the beam from an optical collection system and a scanner, and splitting the beam into two paths; (iii) a Sagnac interferometer optionally combined with other optical means in which interferometer the OPD varies with the angle of incidence of the incoming radiation, such as the four-mirror plus beamsplitter interferometer as further described in the cited U.S. patent (see FIG. 14 there). FIG. 8 illustrates an imaging spectrometer constructed in accordance with U.S. Pat. No. 5,539,517, utilizing an interferometer in which the OPD varies with the angle of incidence of the incoming radiation. A beam entering the interferometer at a small angle to the optical axis undergoes an OPD which varies substantially linearly with this angle. In the interferometer of FIG. 8 , all the radiation from source 30 in all the picture elements, after being collimated by an optical collection system 31 , is scanned by a mechanical scanner 32 . The light is then passed through a beamsplitter 33 to a first reflector 34 and then to a second reflector 35 , which reflects the light back through the beamsplitter 33 and then through a focusing lens 36 to an array of detectors 37 (e.g., a CCD). This beam interferes with the beam which is reflected by 33 , then by second reflector 35 , and finally by first reflector 34 . At the end of one scan, every picture element has been measured through all the OPD's, and therefore the spectrum of each picture element of the scene can be reconstructed by Fourier transformation. A beam parallel to the optical axis is compensated, and a beam at an angle (&thgr;) to the optical axis undergoes an OPD correction, which is a function of the thickness of the beamsplitter 33 , its index of refraction, and the angle &thgr;. The OPD is proportional to &thgr; for small angles. By applying the appropriate inversion, and by careful bookkeeping, the spectrum of every picture element is calculated. In the configuration of FIG. 8 the ray which is incident on the beamsplitter at an angle &bgr; (&bgr;&equals;45° in FIG. 8 ) goes through the interferometer with an OPD&equals;0, whereas a ray which is incident at a general angle &bgr;-&thgr; undergoes an OPD given by Equation (1): OPD (&bgr;,&thgr;, t,n )&equals; t &lsqb;( n 2 −sin 2 (&bgr;&plus;&thgr;)) 0.5 −( n 2 −sin 2 (&bgr;−&thgr;)) 0.5&plus;2 sin &bgr; sin &thgr;&rsqb;  (1) where &thgr; is the angular distance of a ray from the optical axis or interferometer rotation angle with respect to the central position; t is the thickness of the beamsplitter; and n is the index of refraction of the beamsplitter. It follows from the above equation that by scanning both positive and negative angles with respect to the central position, one gets a double-sided interferogram for every picture element, which helps eliminate phase errors giving more accurate results in the Fourier transform calculation. The scanning amplitude determines the maximum OPD reached, which is related to the spectral resolution of the measurement. The size of the angular steps determines the OPD step which is, in turn, dictated by the shortest wavelength to which the system is sensitive. In fact, according to the sampling theorem &lsqb;see, Chamberlain (1979) The principles of interferometric spectroscopy, John Wiley and Sons, pp. 53-55&rsqb;, this OPD step must be smaller than half the shortest wavelength to which the system is sensitive. Another parameter which should be taken into account is the finite size of a detector element in the matrix. Through the focusing optics, the element subtends a finite OPD in the interferometer which has the effect of convolving the interferogram with a rectangular function. This brings about, as a consequence, a reduction of system sensitivity at short wavelengths, which drops to zero for wavelengths equal to or below the OPD subtended by the element. For this reason, one must ensure that the modulation transfer function (MTF) condition is satisfied, i.e., that the OPD subtended by a detector element in the interferometer must be smaller than the shortest wavelength at which the instrument is sensitive. Thus, imaging spectrometers constructed in accordance with the invention disclosed in U.S. Pat. No. 5,539,517 do not merely measure the intensity of light coming from every picture element in the field of view, but also measure the spectrum of each picture element in a predefined wavelength range. They also better utilize all the radiation emitted by each picture element in the field of view at any given time, and therefore permit, as explained above, a significant decrease in the frame time and/or a significant increase in the sensitivity of the spectrometer. Such imaging spectrometers may include various types of interferometers and optical collection and focusing systems, and may therefore be used in a wide variety of applications. An imaging spectrometer in accordance with the invention disclosed in U.S. Pat. No. 5,539,517 was developed by Applied Spectral Imaging Ltd., Industrial Park, Migdal Haemek, Israel and is referred to herein as SPECTRACUBE. This spectral imaging device was used to reduce the present invention to practice, yielding unexpected results as is further demonstrated in the Examples section that follows. The SPECTRACUBE system has the following or better characteristics, listed hereinbelow in Table 1: 1 TABLE 1 Parameter Performance Spatial resolution: MTF/M &mgr;m (M &equals; effective fore optics magnification) Field of View: 8.3/M millimeters Sensitivity: 20 milliLux (for 100 msec integration time, increases for longer integration times linearly with {square root}{square root over (T)}) Spectral range: 400-1000 nm Spectral resolution: 4 nm at 400 nm (16 nm at 800 nm) Acquisition time: 5-50 sec, typical 20 seconds FFT processing time: 5-60 sec, typical 20 seconds 
 Other Spectral Imaging Devices The SPECTRACUBE system optically connected to a suitable fore optics is preferably used to analyze tissue, such as brain tissue, according to the methods of the present invention. However, any spectral imaging device, i.e., an instrument that measures and stores in memory for later retrieval and analysis the spectrum of light emitted by every point of an object which is placed in its field of view, including filter (e.g., conventional interference filters, acousto-optic tunable filters (AOTF) or liquid-crystal tunable filter (LCTF)) and dispersive (monochromator) element (e.g., grating or prism) based spectral imaging devices, or other spectral data or multi-band light collection devices (e.g., a device in accordance with the disclosure in Speicher R. M., Ballard S. G. and Ward C. D. (1996) Karyotyping human chromosomes by combinatorial multi-flour FISH. Nature Genetics, 12:368-375) can potentially be used to acquire the required spectral data. Also a device including a plurality of wide-band of (fixed or tunable) filters, as described in U.S. Pat. No. 5,834,203, and is incorporated by reference as if fully set forth herein, can be used as the spectral data collection device according to the present invention. Therefore, it is intended not to limit the scope of the present invention for use of any specific type of spectral imaging device. 
 Interference Filters-based Spectral Imaging Devices With reference now to FIG. 9 . A filters-based spectral imaging device is referred to herein as apparatus 100 and includes an objective or fore optics 101 . Apparatus 100 further includes a plurality of interference filters 114 , five are shown. The filters are selected according to the features described hereinunder. Illumination filters 116 may also be employed, so as to restrict the illumination provided by a light beam 112 to specific wavelengths. Apparatus 100 further includes an automatic, manual or semimanual control device 120 . Device 120 serves for selecting among filters 114 and/or 116 . Apparatus 100 further includes a light intensity recording device 122 (e.g., a CCD) which serves for recording reflected light intensity as retrieved after passing through any one of filter 114 . As a result, each of the picture elements in the analyzed sample is representable by a vector of a plurality of dimensions, the number of dimensions being equal to the number of filters 114 . In a preferred embodiment apparatus 100 further includes a collimating lens 119 to ensure full collimation of the light before reaching recording device 122 . In a preferred embodiment apparatus 100 further includes a focusing lens 121 for focusing light reaching recording device 122 . The following provides considerations relating to filters 114 employed with apparatus 100 . Thus, according to a preferred embodiment of the present invention the filters are selected so as to collect spectral data of intensity peaks and/or steeps characterizing one or more spectrally monitored substances, such as, but not limited to, peaks or steeps characterizing deoxy-hemoglobin, oxy-hemoglobin or deoxy-hemoglobin and oxy-hemoglobin. The different hemoglobin absorption spectra are shown in FIG. 11 . Alternatively, filters are selected so as to collect spectral data of intensity peaks and/or steeps characterizing a single or an averaged picture element of the sample analyzed, e.g., the cortex. In any case, the normalized intensities measured using each of the discrete filters can be used as input for the algorithm of the present invention which is further described hereinunder. Thus, choice of filters is dictated by the spectral qualities one wishes to capture. The exact wavelength in which these phenomena will be detected (such as the double-peak of oxy-hemoglobin absorption, see FIG. 11 ) will differ from system to system as a function of the system response. The response is composed of the CCD quantum efficiency curve, the illumination curve and the transmittance curve of the system optics. Herein, as shown in FIG. 12 , five filters were selected based on spectral qualities of a representative spectrum of a picture element of the human cortex. Each filter is a 10 nm full-width-at-half-maximum (FWHM) filter, with high transmittance properties. Filters 1 and 5 are selected to collect spectral data from the peaks of the representative spectrum of the human cortex which peaks are at 513 nm and 600 nm. Filters 2 and 4 are selected to collect spectral data from the steeps of the representative spectrum of the human cortex which steeps are at 540 nm and 575 nm. Filter 3 is selected to collect spectral data from the minor peak of the representative spectrum of the human cortex which is attributed to oxy-hemoglobin and is at 555 nm. Thus, according to preferred embodiments of the invention, each of the filters is individually about 5 to about 15 nm, preferably about 10 nm, full-width-at-half-maximum filter. The filters-based spectral imaging device of the invention may thus include N filters selected from the group consisting of an about 540 nm maximal transmittance filter, an about 575 nm maximal transmittance filter, an about 555 nm maximal transmittance filter, an about 513 nm maximal transmittance filter and an about 600 nm maximal transmittance filter, whereas N is an integer selected from the group consisting two, three, four and five. It will be appreciated that multiple chroic filter, such as dichroic filter or trichroic filter can replace a pair or triad of monochroic filters. It will further be appreciated that different choices of filters are reasonable as well. Thus, another optional choice for selecting filters for the spectral imaging device of the invention is setting two filters on two isosbasthic points (wavelengths where the absorption coefficients of oxy- and deoxy-hemoglobin coincide) and setting an additional (or more) filter(s) on a point showing great difference in the absorption values (see FIG. 11 ). Using this method requires performing a calibration for correlating the changes observed by the system to changes in oxygen saturation values. Thus, according to this embodiment the filters include at least one, preferably several, say 2-5, filters of maximal transmittance at a wavelength which corresponds to at least one isosbasthic point of deoxy-hemoglobin and oxy-hemoglobin and at least one additional filter of maximal transmittance at a wavelength which corresponds to at least one, preferably several, say 2-5, non-isosbasthic points of deoxy-hemoglobin and oxy-hemoglobin. FIG. 13 a is a graph showing a typical normalized measured reflectance spectrum of a picture element of the cortex. FIG. 13 b shows intensity results calculated using the mathematical filters shown in FIG. 12 to mathematically filter light derived from the representative picture element of the human cortex shown in FIG. 13 a. FIG. 13 c is a graph showing an interpolation of the discrete spectrum using the spline method. FIG. 13 d demonstrates the optical density of the curve of FIG. 13 a (in blue) and next to it (in red) the graph created by reconstructing a spectrum using the results obtained by mathematical manipulation using the method described below for determining oxygen saturation. FIG. 13 e shows, in blue, the optical density of the curve obtained by interpolating on filter-measured data ( FIG. 13 c ) along with the fit (in red) calculated for it by reconstructing a spectrum using the results obtained by mathematical manipulation using the method described below for determining oxygen saturation. FIG. 13 f shows the calculated curves obtained by using the OS calculation method described below when applied to the spectrum measured by the interferometer system ( FIG. 13 a ) and by mathematically extracting a filter-based spectrum ( FIG. 13 c ). The fits correlate with OS calculated values of 93% (when measured with spectral-imaging) and 88% (when measured with filters). 
 Analyzing and Displaying Spectral Imaging Data 
 General Considerations and Approaches General: A spectral image is a three dimensional array of data, I(x,y,&lgr;), that combines spectral information with spatial organization of the image. As such, a spectral image is a set of data called a spectral cube, due to its dimensionality, which enables the extraction of features and the evaluation of quantities that are difficult, and in some cases even impossible, to obtain otherwise. Since both spectroscopy and digital image analysis are well known fields that are covered by an enormous amount of literature &lsqb;see, for example, Jain (1989) Fundamentals of Digital Image Processing, Prentice-Hall International&rsqb;, the following discussion will focus primarily on the benefit of combining spectroscopic and imaging information in a single data set, i.e., a spectral cube. Such a spectral cube of data can be collected by any spectral imaging device as is further delineated hereinabove. One possible type of analysis of a spectral cube is to use spectral and spatial data separately, i.e., to apply spectral algorithms to the spectral data and two-dimensional image processing algorithms to the spatial data. As an example of a spectral algorithm, consider an algorithm computing the similarity between a reference spectrum and the spectra of all pixels (i.e., similarity mapping) resulting in a gray (or other color) scale image (i.e., a similarity map) in which the intensity at each pixel is proportional to the degree of ‘similarity’. This gray scale image can then be further analyzed using image processing and computer vision techniques (e.g., image enhancement, pattern recognition, etc.) to extract the desired features and parameters. In other words, similarity mapping involves computing the integral of the absolute value of the difference between the spectrum of each pixel of the spectral image with respect to a reference spectrum (either previously memorized in a library, or belonging to a pixel of the same or other spectral image), and displaying a gray level or pseudocolor (black and white or color) image, in which the bright pixels correspond to a small spectral difference, and dark pixels correspond to a large spectral difference, or vice versa. Similarly, classification mapping perform the same calculation as described for similarity mapping, yet takes several spectra as reference spectra, and paints each pixel of the displayed image with a different predetermined pseudocolor, according to its classification as being most similar to one of the several reference spectra. It is also possible to apply spectral image algorithms based on non-separable operations; i.e., algorithms that include both local spectral information and spatial correlation between adjacent pixels (one of these algorithms is, as will be seen below, a principal component analysis). One of the basic needs that arise naturally when dealing with any three-dimensional (3D) data structure such as a spectral cube (i.e., I(x,y,&lgr;)), is visualizing that data structure in a meaningful way. Unlike other types of 3D data such as topographic data, D(x,y,z), obtained for example by a confocal microscope, where each point represents, in general, the intensity at a different location (x,y,z) in a tree-dimensional space, a spectral image is a sequence of images representing the intensity of the same two-dimensional plane (i.e., the sample) at different wavelengths. For this reason, the two most intuitive ways to view a spectral cube of data is to either view the image plane (spatial data) or the intensity of one pixel or a set of pixels as function of wavelength in a three-dimensional mountain-valley display. In general, the image plane can be used for displaying either the intensity measured at any single wavelength or the gray scale image that results after applying a spectral analysis algorithm, over a desired spectral region, at every image pixel. The spectral axis can, in general, be used to present the resultant spectrum of some spatial operation performed in the vicinity of any desired pixel (e.g., averaging the spectrum). It is possible, for example, to display the spectral image as a gray scale image, similar to the image that might be obtained from a simple monochrome camera, or as a multicolor image utilizing one or several artificial colors to highlight and map important features. Since such a camera simply integrates the optical signal over the spectral range (e.g., 400 nm to 760 mn) of the CCD array, the ‘equivalent’ monochrome CCD camera image can be computed from the 3D spectral image data base by integrating along the spectral axis, as follows: 1 gray_scale &it; ( x , y ) = &Integral; λ &it; &it; 1 λ2 &it; w &af; ( λ ) &CenterDot; I &af; ( x , y , λ ) &it; &dd; λ ( 2 ) In equation 2, w(&lgr;) is a general weighting response function that provides maximum flexibility in computing a variety of gray scale images, all based on the integration of an appropriately weighted spectral image over some spectral range. For example, by evaluating equation 2 with three different weighting functions, &lcub;w r (&lgr;), w g (&lgr;), w b (&lgr;)&rcub;, corresponding to the tristimulus response functions for red (R), green (G) and blue (B), respectively, it is possible to display a conventional RGB color image. It is also possible to display meaningful non-conventional (pseudo) color images. FIG. 10 presents an example of the power of this simple algorithm. Consider choosing &lcub;w r , w g , w b &rcub; to be Gaussian functions distributed “inside” a spectrum of interest, the resulting pseudo-color image that is displayed in this case emphasizes only data in the spectral regions corresponding to the weighting functions, enabling spectral differences in these three regions to be detected more clearly. Point Operations: Point operations are defined as those that are performed on single pixels, (i.e., do not involve more than one pixel at a time). For example, in a gray scale image, a point operation can be one that maps the intensity of each pixel (intensity function) into another intensity according to a predetermined transformation function. A particular case of this type of transformation is the multiplication of the intensity of each pixel by a constant. Additional examples include similarity and classification mapping as described hereinabove. The concept of point operations can also be extended to spectral images: here each pixel has its own intensity function (spectrum), i.e., an n-dimensional vector V 1 (&lgr;); &lgr;&egr;&lsqb;&lgr; 1 , &lgr; n &rsqb;. A point operation applied to a spectral image can be defined as one that maps the spectrum of each pixel into a scalar (i.e., an intensity value) according to a transformation function: &ngr; 2 &equals;g ( V 1 (&lgr;)); &lgr;&egr;&lsqb;&lgr; 1 , &lgr; n &rsqb;  (3) Building a gray scale image according to Equation 3 is an example of this type of point operation. In the more general case, a point operation maps the spectrum (vector) of each pixel into another vector according to a transformation function: V 2 ( l )&equals; g ( V 1 (&lgr;)); 1 &egr;&lsqb; 1, N&rsqb;, &lgr;&egr;&lsqb;&lgr; 1 , &lgr; n &rsqb;  (4), where N&lE;n. In this case a spectral image is transformed into another spectral image. One can now extend the definition of point operations to include operations between corresponding pixels of different spectral images. An important example of this type of algorithm is optical density analysis. Optical density is employed to highlight and graphically represent regions of an object being studied spectroscopically with higher dynamic range than the transmission spectrum. The optical density is related to transmission by a logarithmic operation and is therefore always a positive function. The relation between the optical density and the measured spectra is given by Lambert Beer law: 2 OD &af; ( λ ) = - log 10 &it; I &af; ( λ ) I 0 &af; ( λ ) = - log 10 &it; τ &af; ( λ ) ( 5 ) where OD(&lgr;) is the optical density as a function of wavelength, I(&lgr;) is the measured spectrum, I o (&lgr;) is a measured reference spectrum, and &tgr;(&lgr;) is the spectral transmittance of the sample. Equation 5 is calculated for every pixel for every wavelength where I o (&lgr;) is selected from (1) a pixel in the same spectral cube for which OD is calculated; (2) a corresponding pixel in a second cube; and (3) a spectrum from a library. Note that the optical density does not depend on either the spectral response of the measuring system or the non-uniformity of the CCD detector. This algorithm is useful to map the relative concentration, and in some cases the absolute concentration of absorbers in a sample, when their absorption coefficients and the sample thickness are known. Additional examples include various linear combination analysis, such as, but not limited to, (i) applying a given spectrum to the spectrum of each of the pixels in a spectral image by an arithmetical function such as addition, subtraction, multiplication division and combinations thereof to yield a new spectral cube, in which the resulting spectrum of each pixel is the sum, difference, product ratio or combination between each spectrum of the first cube and the selected spectrum; and (ii) applying a given scalar to the spectra of each of the pixels of the spectral image by an arithmetical function as described above. Such linear combinations may be used, for example, for background subtraction in which a spectrum of a pixel located in the background region is subtracted from the spectrum of each of the pixels; and for a calibration procedure in which a spectrum measured prior to sample analysis is used to divide the spectrum of each of the pixels in the spectral image. Another example includes a ratio image computation and display as a gray level image. This algorithm computes the ratio between the intensities at two different wavelengths for every pixel of the spectral image and paints each of the pixels in a lighter or darker artificial color accordingly. For example, it paints the pixel bright for high ratio, and dark for low ratio (or the opposite), to display distributions of spectrally sensitive materials. Spatial-spectral Combined Operations: In all of the spectral image analysis methods mentioned above, algorithms are applied to the spectral data. The importance of displaying the spectrally processed data as an image is mostly qualitative, providing the user with a useful image. It is also possible, however, depending on the application, to use the available imaging data in even more meaningful ways by applying algorithms that utilize the spatial-spectral correlation that is inherent in a spectral image. Spatial-spectral operations represent the most powerful types of spectral image analysis algorithms. As an example, consider the following situation: A sample contains k cell types stained with k different stains (the term ‘cell’ here is used both for a biological cell, and also as ‘a region in the field of view of the instrument’). Each stain has a distinct spectrum and binds to only one of the k cell types. It is important to find the average intensity per cell for each one of the k cell types. To achieve this task the following procedure can be used: (i) classify each pixel in the image as belonging to one of k&plus;1 classes (k cell types plus a background) according to its spectrum; (ii) segment the image into the various cell types and count the number of cells from each type; and (iii) sum the spectral energy contributed by each class, and divide it by the total number of cells from the corresponding class. This procedure makes use of both spectral and spatial data. The relevant spectral data takes the form of characteristic cell spectra (i.e., spectral “signatures”), while the spatial data consists of data about various types of cells (i.e., cell blobs) many of which appear similar to the eye. In the above situation, cells can be differentiated by their characteristic spectral signature. Hence, a suitable point operation will be performed to generate a synthetic image in which each pixel is assigned one of k&plus;1 values. Assuming that the spectra of the different cell types are known to be s i (&lgr;); i&equals;1, 2 . . . , k, &lgr;&egr;&lsqb;&lgr; 1 , &lgr; n &rsqb;, and the measured spectrum at each pixel (x, y) is S x,y (&lgr;), &lgr;&egr;&lsqb;&lgr; 1 , &lgr; n &rsqb;, then the following algorithm is a possible method of classification (step 1 above): Let e 2 i be the deviation of the measured spectrum from the known spectrum of the stain attached to cell type i. Then, adopting a least-squares “distance” definition, one can write: 3 e i 2 = &Sum; λ &Element; R λ &it; ( s &af; ( λ ) - s i &af; ( λ ) ) 2 ( 6 ) where R &lgr; is the spectral region of interest. Each point &lsqb;pixel (x, y)&rsqb; in the image can then be classified into one of the k&plus;1 classes using the following criterion: point(x,y) &egr; class k&plus;1 if e 2 i > threshold for all i &egr;&lsqb;1,k&rsqb;, whereas  (7) point(x,y) &egr; class &rgr; if e 2 i < threshold, and &rgr; is such that min&lsqb;e 2 i &rsqb;&equals;e 2 &rgr; . Steps ii and iii above (image segmentation and calculation of average intensity) are now straight-forward using standard computer vision operations on the synthetic image created in accordance with the algorithm described in equations 6 and 7. Another approach is to express the measured spectrum s x,y (&lgr;) at each pixel as a linear combination of the k known fluorescence spectra s i (&lgr;); i&equals;1, 2, . . . , k. In this case one would find the coefficient vector C&equals;&lsqb;c 1 , c 2 , . . . c k &rsqb; that solves: 4 F = min &it; &Sum; λ &Element; R λ &it; ( s &af; ( λ ) - s ^ &af; ( λ ) ) 2 &it; &NewLine; &it; where &it; &it; s ^ &af; ( λ ) = &Sum; i = 1 k &it; c i &CenterDot; s i &af; ( λ ) , ( 8 ) Solving for 5 &dd; F &dd; c i = 0 ; for i&equals;1, 2, . . . , k (i.e., find values of c i which minimize F) yields the matrix equation C&equals;A −1 B (9), where A is a square matrix of dimension k with elements 6 a m , n = [ &Sum; λ &Element; R λ &it; s m &af; ( λ ) &CenterDot; s n &af; ( λ ) ] , ( 10 ) and B is a vector defined as 7 b m = [ &Sum; λ &Element; R λ &it; s m &af; ( λ ) &CenterDot; s &af; ( λ ) ] , m , n = 1 , 2 , … , k . ( 11 ) Arithmetic operations may similarly be applied to two or more spectral cubes and/or spectra of given pixels or from a library. For example consider applying an arithmetic operations between corresponding wavelengths of corresponding pairs of pixels belonging to a first spectral cube of data and a second spectral cube of data to obtain a resulting third spectral cube of data for the purpose of, for example, averaging two spectral cubes of data, time changes follow-up, spectral normalization, etc. In many cases objects (e.g., cells) present in a spectral image differ from one another in chemical constituents and/or structure to some degree, especially when stained. Using a decorrelation analysis, such as a principal component analysis, by producing covariance or a correlation matrix, enhances these differences. Decorrelation statistical analysis is directed at extracting decorrelated data out of a greater amount of data, and average over the correlated portions thereof There are a number of related statistical decorrelation methods. Examples include but not limited to principal component analysis (PCA), canonical variable analysis and singular value decomposition, etc., of these methods PCA is perhaps the more common one, and is used according to the present invention for decorrelation of spectral data, as this term is defined above. However, considering the fact that all decorrelation statistical methods including those listed above are related to one another, there is no intention to limit the scope of the invention to use of any specific decorrelation method. Specifically, there is no intention to limit the scope of the present invention to use of principal component analysis, as any other decorrelation statistical method may be alternatively employed. Information concerning the use and operation of the above listed decorrelation statistical methods is found in R. A. Johnson and D. W. Wichen, “Applied Multivariance Statistical Analysis, third edition, Prentice Hall (1992) and T. W. Anderson, An Introduction to Multivariance Statistical Analysis, second edition, Wiley and Sons (1984), both are incorporated by reference as if fully set forth herein. Furthermore, as will become apparent from the descriptions to follow, the implementation of a decorrelation statistical method may be done using various modifications. As the concept of the present invention is not dependent upon any specific modification, it is the intention that the scope of the present invention will not be limited to any specific modification as described below. A brief description of the principal component analysis using a covariance matrix is given below. For further details regarding the principal component analysis, the reader is referred to Martens and Naes (1989) Multivariate Calibration, John Wiley & Sons, Great Britain; and to Esbensen et al., Eds. (1994) Multi variance analysis—in practice. Computer-aided modeling as CAMO, and the Unscrambler's User's guide, Trondheim, Norway. Thus, the intensities of the pixels of the image at wavelength &lgr; i (i&equals;1, . . . N) are now considered a vector whose length is equal to the number of pixels q. Since there are N of these vectors, one for every wavelength of the measurement, these vectors can be arranged in a matrix B′ with q rows, and N columns: 8 B ′ = No . &it; of &it; &it; pixels &it; No . &it; of &it; &it; wavelengths B 11 ′ &it; … B 1 &it; N ′ &vellip; &vellip; B q1 ′ … B qN ′ ( 12 ) For each of the columns of matrix B′ defined is an average: 9 M i = &it; 1 q &it; &Sum; i = 1 q &it; B ji ′ ; i = 1 &it; &it; … &it; &it; N ( 13 ) and a second normalized matrix B defined as: 10 B = No . &it; of &it; &it; pixels &it; No . &it; of &it; &it; wavelengths B 11 ′ / M 1 … B 1 &it; N ′ / M N &vellip; &vellip; B q1 ′ / M 1 … B qN ′ / M N &it; ( 14 ) A covariance matrix C is defined for the matrix B: C&equals;B T ·B of dimensions N×N. C is diagonalized, and eigenvectors and eigenvalues related by: C·Vi&equals;&mgr; i ·V i where V i are N orthogonal unit vectors and &mgr; i are the eigenvalues representing the variance in the direction of the i-th unit vector V i . In general, the lowest components represent the highest variability as a function of pixels. The products BV i (i&equals;1, . . . N) are the projections of the spectral image onto the elements of the orthogonal basis, They are vectors with q elements (q&equals;number of pixels), and can be displayed separately as black and white images. These images may reveal features not obvious from a regular black and white image filtered at a certain wavelength or wavelength range. 
 Spatial and Spectral Resolutions According to a preferred embodiment of the present invention each spectral data collection step of the methods of the present invention as is effected by the systems of the present invention is independently characterized by spectral resolution ranging between 1 nm and 50 nm, e.g., 2-40 nm, 2-30 nm, 2-20 nm, or 2-10 nm, and spatial resolution ranging between 0.1 mm and 1.0 mm, preferably between 0.2 mm and 0.5 mm. Accordingly, the systems of the present invention are so designed and constructed so as to provide spectral resolution ranging between 1 nm and 50 nm and spatial resolution ranging between 0.1 mm and 1.0 mm. This combination of high spectral and spatial resolutions was, as of yet, never attempted for functional brain mapping and, the quality of results obtained using same, as is further exemplified in the Examples section that follows, is striking. 
 Calculating OS and Blood Thickness (Volume) from Recorded Reflectance Spectra A simple model that describes the reflection I(&lgr;) of white light from the brain surface is obtained by assuming that the illumination signal is reflected from a partially absorbing and partially scattering tissue. The Consequently from the equations above one obtains that: 11 - &dd; &dd; λ &it; ln &af; ( I / W ) &TildeFullEqual; 2 &it; l &af; ( &dd; &varepsilon; HbO 2 &dd; λ &it; [ HbO 2 ] _ + &dd; &varepsilon; Hb &dd; λ &it; [ Hb ] _ ) ( 17 ) Now, the oxygen saturation OS is measured in percent &lsqb;%&rsqb; and is defined as 100×&lsqb;HbO 2 &rsqb;/(&lsqb;HbO 2 &rsqb;&plus;&lsqb;Hb&rsqb;). Defining c&equals;&lsqb;HbO 2 &rsqb;&plus;&lsqb;Hb&rsqb;&equals;const&equals;8.98 &mgr;mole/mliter, which is the typical hemoglobin concentration in human blood, one obtains: 12 - &dd; &dd; λ &it; ln &af; ( I / W ) = 2 &it; lc 100 &af; [ OS &af; ( &dd; &varepsilon; HbO 2 &dd; λ - &dd; &varepsilon; Hb &dd; λ ) + 100 &it; &dd; &varepsilon; Hb &dd; λ ] ( 18 ) This equation is the basic expression of the oxygen saturation model for a blood containing tissue according to the present invention. The left hand side contains only measured quantities (I,W), while the right hand side contains the known quantities from the literature &egr; HbO2 (&lgr;) and &egr; Hb (&lgr;), and the unknowns l and OS. One now has a set of n equations, where n is the number of wavelengths measured (typically about 100 data-points per spectrum), to solve with two unknowns: OS and l. Solving for OS we can now reconstruct the spectrum by applying the effective medium approximation: &mgr;(&lgr;)&equals;&egr; HbO2 (&lgr;)·&boxdr; HbO 2 &boxdl;&plus;&egr; Hb (&lgr;)·&lsqb; Hb&rsqb; (19) attenuation of the signal is described by the Beer-Lambert law, and the measured signal is thus given by: I (&lgr;)&equals; W (&lgr;)· exp&lsqb;−a (&lgr;)·2 l&rsqb; (15) where W(&lgr;) is the intensity of the incident light, I(&lgr;) is the reflection of white light from the tissue, l is the penetration depth of the light beam, and a(&lgr;) is the attenuation coefficient that consists of two contributions—the absorption coefficient &mgr;(&lgr;) and the scattering coefficient s(&lgr;), i.e., a(&lgr;)&equals;s(&lgr;)&plus;&mgr;(&lgr;). In a blood containing tissue the attenuation characteristics are dominated by the absorption characteristics of hemoglobin, which varies non-monotonously with &lgr; in the spectral range of 500 nm and 650 nm, while the scattering coefficient s(&lgr;) is weakly dependent on the wavelength &lgr;. Thus, for the wavelength range of 500 nm-650 nm it is assumed that da(&lgr;)/d&lgr;≅d&eegr;/d&lgr; approximately holds true. For &mgr;(&lgr;) in the case of hemoglobin, the effective medium approximation &mgr;(&lgr;)&equals;&egr; HbO2 (&lgr;)·&boxdr;HbO 2 &boxdl;&plus;&egr; Hb (&lgr;)·&lsqb;Hb&rsqb; (16) is applied, where &lsqb;HbO 2 &rsqb; and &lsqb;Hb&rsqb; are the concentration of the oxygenated and deoxygenated hemoglobin, respectively, and &egr; HbO2 (&lgr;) and &egr; Hb (&lgr;) are the oxygenated and deoxygenated hemoglobin absorption coefficients, respectively. &mgr;(&lgr;) is now compared with the actual optical density of the spectrum to which, in pure theory, it should be identical. This comparison is named “fit” as how well the calculated spectrum actually fits the measured spectrum is determined. Calculating l provides the blood thickness (indicative of volume). 
 Generation of Oxygen Saturation and/or Blood Volume Difference Maps Generation of oxygen saturation and/or blood volume color or intensity coded maps and color or intensity coded oxygen saturation and/or blood volume difference maps is addressed herein primarily in context of functional brain mapping, however, it will be appreciated that such maps can similarly be constructed for other tissues, including, but not limited to, the heart, liver, kidney, eye, etc., for example, monitoring the renewal of blood vessels in cases of skin flap implants, where this information is important for deciding on when to cut the flap, or for analyzing skin nevos for the purpose of performing an optical biopsy, reducing the need for recession of nevos, a process which at times is associated with complications, or for using in heart open surgery for the purpose of assessing the quality of blood supply, or for use with dye-involving processes, such as, but not limited to, use of ALA in PDT treatments or use of voltage sensitive dyes for monitoring brain activity, following, for example, a stimulus, oxygenation, deoxygenation, blood perfusion, etc. In addition, while the present invention primarily addresses hemoglobin as a monitored substance, concentration and/or difference maps of any other substance, featuring spectral absorption properties in the visual or infrared range to which a spectral-imaging device is sensitive, can be similarly monitored. Thus, according to a preferred embodiment of the present invention at least one threshold is used while generating the image highlighting differences among spectra of a tissue, such as the exposed cortex, as is further delineated below. Preferably the image highlighting differences among spectra of the tissue is highlighting oxygen saturation and/or blood volume differences. However, other substances may be monitored, including certain metabolites and other cellular components which are mentioned in the background section hereinabove. Still preferably, at least one threshold is used while generating the image highlighting oxygen saturation and/or blood volume differences. According to one embodiment, the threshold is effected by taking into account only picture elements in which an absolute oxygen saturation and/or blood volume is above a predetermined threshold, say above 30% of maximal value, above 40% of maximal value, above 50% of maximal value, above 60%, above 70%, above 80% or above 90% of maximal value, typically in the range of 70% -100% of maximal value. Different thresholds can be applied to data acquired prior to, during or following stimulation. According to another embodiment, the threshold, alternatively or further includes taking into account only picture elements in which a difference in oxygen saturation and/or blood volume before and during or after the stimulation is above a predetermined second threshold, say about 1%, about 2%, about 3%, about 4%, about 5%, about 10% or about 20%. According to another embodiment, the threshold, alternatively or further includes taking into account only picture elements for which the total intensity is above a predetermined threshold and painting black all other pixels. This serves as a means for eliminating picture elements that are included in the image but which are not part of the exposed cortex (pixels around the exposed cortex are typically of lower reflection energy and are thus eliminated). According to still another preferred embodiment of the invention clusters of neighboring picture elements (above the first and the second thresholds) which include less than a predetermined number picture elements, say 1-5 picture elements, depending on the spatial resolution, are discarded. Based on the data collected and the thresholds as described above the image highlighting differences among spectra of the exposed cortex collected before and during or after stimulation is constructed. In addition, spectral images can be constructed based on the data collected before and/or during of after stimulation, highlighting absolute or relative values. In any case the images can be color or intensity coded. As shown in FIG. 46 , according to the present invention, published hemoglobin absorption spectra are used to calculate the oxygen saturation value of each picture element, so as to create a color or intensity coded map. Thus, as shown in FIG. 47 , each pixel is assigned a color according to its absolute or relative oxygen saturation value. As shown in FIG. 48 , subtraction of an oxygen saturation map acquired pre stimulation from an oxygen saturation map acquired post stimulation, applying thresholds as described herein and overlaying the results on a grayscale image results in a comprehensive oxygen saturation difference map. As shown in FIG. 49 a color or intensity coded map (oxygen saturation map in this case) can be overlaid on a grayscale image so as to obtain a composite image highlighting both anatomical features as well as functional features. Thus, when highlighting differences, coding refers to the degree of difference, e.g., coded saturation and/or blood volume difference maps and is effected by one or more difference threshold. When highlighting absolute or relative values, coding refers to their absolute or relative levels, e.g., coded saturation and/or blood volume maps, and is effected by suitable one or more absolute or relative thresholds. In the latter case, the images are generated by attributing each of the pixels in the images a distinctive color or intensity according to, for example, oxygen saturation and/or blood volume characterizing its respective picture element in the cortex. In a preferred embodiment of the invention coded images or maps are co-displayed either side by side with respect to, and/or overlaid (e.g., superimposed) over, an anatomical image of the examined tissue, e.g., the cortex. The anatomical image, which is constructed from the spectral data collected before or during and/or after the stimulation can be an RGB image or a monochromatic (e.g., gray scale) image. Thus, in a specific embodiment, the present invention provides a method of generating an oxygen saturation and/or blood volume difference map of a tissue of a subject. The method is effected by (a) illuminating the tissue of the subject with incident light; (b) at a first time point, acquiring a spectrum of each picture element of the tissue of the subject; (c) at a second time point, acquiring at least one additional spectrum of each picture element of the tissue of the subject; and (d) generating an image highlighting differences among spectra of the tissue acquired in steps (b) and (c), so as to generate the oxygen saturation and/or blood volume difference map of the tissue. Thresholds and other features as described above with respect to functional brain mapping are preferably applied in a similar manner. According to this embodiment, the present invention also provides a system for monitoring oxygen saturation in a tissue. The system comprises a spectral imaging device and an image generating device, the spectral imaging device and the image generating device acting in synergy to produce an oxygen saturation difference map by highlighting tissue regions characterized by (a) having an absolute or relative level of oxygen saturation above a predetermined first threshold; (b) having an oxygen saturation difference above a predetermined second threshold; and/or (c) having a cluster size above a predetermined size. Further according to this embodiment, the present invention also provides a system for monitoring blood volume in a tissue. The system comprises a spectral imaging device and an image generating device, the spectral imaging device and the image generating device acting in synergy to produce a blood volume difference map by highlighting tissue regions characterized by (a) having an absolute or relative level of blood volume above a predetermined first threshold; (b) having a blood volume difference above a predetermined second threshold; and (c) having a cluster size above a predetermined size. FIG. 44 shows a color coded oxygen saturation map of a human cortex overlaid on a monochromatic anatomical image of the cortex. FIG. 45 shows a color coded blood volume map of a human cortex overlaid on a monochromatic anatomical image of the cortex. A pair of such maps is used according to the present invention to calculate and display color or intensity coded oxygen saturation and/or blood volume difference maps. According to a presently preferred embodiment of the invention, a plurality of images highlighting differences among spectra are displayed either superimposed, overlaid or integrated into a cumulative difference image or map, as is further described and exemplified in the Examples section that follows. 
 Spectral Data Acquisition Time Considerations Different total acquisition times can be considered when monitoring different biological process. The scheme used by the spectral imaging related publications cited in the background section employed measurements effected about two seconds post stimulation of the brain. Measurements occurring within this time period detect the very early and highly dynamic changes of blood-flow which is coupled to neural activation. However, obtaining high-quality maps within about two seconds is an impossible task due to the beating of the brain and the low signal-to-noise ratio of the acquired images. The following provides considerations for measurements executed at longer time periods, say 10-15 seconds, post stimulation, which were used while reducing the present invention to practice, yielding unexpected results. Ten-15 seconds post stimulation the hemodynamic changes are not as dramatic as shortly after the stimulation and are probably more diffuse. However, the advantage of measuring in this delayed interval is that one is able to construct high-quality oxygen saturation maps that overcome the problem of brain beating by averaging over a large (e.g., >10) number of beat cycles and obtaining high signal-to-noise ratio images by collecting a large number of photons (and so reduce the effect of “shot noise”, the major noise contributor in this kind of setup). The measurements performed by the inventors of the present invention prove that the oxygen saturation changes, resulting from neuronal activation, are still evident during this time span. The upper limit for measurement is probably 20-30 seconds post stimulation because (i) in the operation room, when operating awake patients, a task should not exceed 10-20 seconds; and (ii) post 20-30 seconds one risks measuring other, not-anticipated, stimuli of the brain. Thus, according to a preferred embodiment of the present invention spectral data collection is performed during at least N brain beats of the subject, wherein N is an integer selected from the group consisting of two, three, four, five, six, seven, eight, nine, ten and an integer between and including eleven and forty. Preferably, the step of spectral data collection post stimulation is effected more than about 3-5 seconds and preferably between about 3-5 and about 30 seconds following initiation of stimulation. According to a preferred embodiment the stimulation prolongs about 3-5 to about 30 seconds, preferably about 10 to about 20 seconds. Preferably, stimulation prolongs throughout the entire second measurement period. According to a preferred embodiment the reflectance spectrum for each picture element and for each spectral data collection step is an averaged reference spectrum of N measurements (for filter based system) or N brain-beats (for interferometer based system), wherein N is an integer and equals at least 2 and is preferably between 5 and 20, say about 10. Thus, the goal of the measurement is to provide high-signal-to-noise ratio images within 10-15 seconds of stimulation overcoming the beating problem of the brain. The following translates these considerations to the use of a filters-based spectral imaging device. The following terminology shall apply. An image is one exposure of the CCD through one filter at an exposure time that brings the recorded signal close to the CCD full well. A set is sequential acquisition of images through all the filters used in the system. A layer is a composition of all images, from the different sets, of a certain filter. Using the above terminology a recommended acquisition scheme is described below: For overcoming the beating problem of the exposed cortex at least 10 sets should be acquired at a rate that is not correlated with the beating. The time for acquiring a single set should be 10-20 seconds, say about 15 seconds. When constructing the layers, spatial registration algorithms should be used to fix possible shifts between images. Such algorithms are well known in the art and are therefore not further described herein. Once layers are constructed, a file is created where each picture element is given a discrete spectrum composed of its intensity value in each one of the layers. The points spectrum of each pixel is then interpolated and the interpolation is used as the basis for calculating the oxygen saturation or blood volume value of the picture element represented by the pixel according to the method described herein, or using any other saturation or volume calculation method. Interpolating the discrete spectrum of each pixel is not a necessity, as the calculations can be performed directly on the discrete data (see FIGS. 12 and 13 ). PCT Application US97/08153, which is incorporated herein by reference, teaches a method for spatial registration and spectral correction for interferometer-based spectral imaging devices which can be used to obtain spectral images of a moving object. The method is effected by (a) using an interferometer-based spectral imaging device for acquiring spatial and spectral information of the moving object; and (b) correcting the spatial and spectral information for movements of the moving object via a spatial registration and spectral correction procedures for obtaining corrected spatial and spectral information. The teachings of this PCT application can be integrated with the present invention so as to enable spectral imaging of a beating cortex during shorter acquisition times. 
 Stimulation Protocols and Additional Considerations Related to Brain Stimulation According to a preferred embodiment of the present invention and as is in many cases of neurosurgeries practiced anyway the subject is awake during the procedure. Alternatively, the subject is anesthetized. According to one embodiment of the present invention stimulation is effected by asking the (awake) subject to perform a task, such as, but not limited to, reading, speaking, listening, viewing, memorizing, thinking and executing a voluntary action (e.g., moving a limb, blinking one or both eyes, etc.). According to another embodiment, stimulation is effected by passively stimulating the brain of the (awake or anesthetized) subject (e.g., inducing brain activity) through the peripheral nervous system by, for example, directing light into the eyes, voice into the ears or by electrical stimulation of the skin at different body locations. Direct electrical stimulation of the brain using electrodes is also applicable. Typically, during a neurosurgery, medical lines, e.g., infusion, ECG leads, etc., are connected to the subject. Preferably, the medical lines are connected to the subject on a single side thereof. Preferably, the medical lines are connected to the subject at locations which are less communicating with the exposed portion of the cortex of the subject. Thus, if the right hemisphere of the cortex is exposed, the medical lines should be connected to the left side of the subject, whereas, if the left hemisphere of the cortex is exposed, the medical lines should be connected to the right side of the subject. Occasionally, it may be advantageous to acquire a reflectance spectrum of each picture element of at least the portion of the exposed cortex of the subject when the patient is briefly (for a short time) anesthetized. This data can locate active brain regions and may serve as reference when interpreting the results of images generated when the patient is awake. Briefly anesthetizing the patient can be effected by, for example, propofol, allowing for a 5-10 minute regain of consciousness once administration is stopped. Other manipulations that can be used during open skull surgery include the use of (i) identical paradigms while mapping according to the present invention as when mapping with pre-operational fMRI for the purpose of comparing these two data sets; (ii) medicine for the purpose of reducing the OS of the cortex, so that a task can be repeated, for the purpose of testing for repetition as a means of confirming results; (iii) measures for reducing the sensory input to the patient such as providing the patient with earplugs, eye covers, local anesthesia, etc. Additional objects, advantages, and novel features of the present invention will become apparent to one ordinarily skilled in the art upon examination of the following examples, which are not intended to be limiting. Additionally, each of the various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below finds experimental support in the following examples. 
 Illumination Considerations It is readily appreciated that the use of illumination presently existing in a brain surgery operating room (OR) for illuminating the cortex for the purpose of acquiring spectral-images is desired and is advantageous over using dedicated illumination (direct current, DC, illumination type) as described hereinabove. Operating room illumination is typically of an alternating current (AC) type and is therefore characterized by a frequency and a frequency time corresponding to the electrical network of a geographical region (typically about 50 (frequency time of 20 milliseconds) or about 60 Hz (frequency time of 16.66 milliseconds), depending on the region). Using a filter-based system for acquiring spectral-images reduces the need of a stable DC illumination source, provided that the following two limitations are met: 1. The exposure times used should be the same for all filters, i.e., no single filter uses an exposure time shorter or longer then any other filter in the filter-wheel. 2. The exposure times used are multiplications of the electrical frequency. For example in a network with a frequency of 50 Hz each time cycle should be about 20 milliseconds. The allowed exposure intervals are therefore multiplicities of about 20 milliseconds, e.g., say about 40 milliseconds, about 60 milliseconds, etc. In a 60 Hz network the cycle time is 16.66 milliseconds. This can be rounded to 17 milliseconds and allow for multiplicities of about 17 milliseconds, e.g., about 33 milliseconds, about 50 milliseconds, etc. Failing to take the frequency into consideration, individual filters will sample different phases of the illumination cycle and will therefore show different intensity values, which are actually, an artifact. Thus, according to an embodiment the present invention the spectral data is collected via a filters-based spectral imaging device and the illumination is effected by an illumination device operated with an alternating current characterized by a frequency time wherein (i) an exposure time of all filters of the filters-based spectral imaging device is substantially equal; and (ii) an exposure time of each of the filters is a multiplicity of the frequency time by an integer. 
 Image Orientation According to the present invention a “label” which is placed on the skull, near the exposed cortex portion is used for providing three functions as follows: 1. The label contains textual or symbol information pertaining to the anatomical orientation of the craniotomy and appears in the resulted images, so at to simplify the process of orienting an image with respect to the actual craniotomy, as the imaging device might cause a rotation of the image. 2. The label supplies a “white target” reference spectrum for calculating oxygen saturation as described herein. A “white target” is a target that reflects incident light without altering the spectrum of the light. In other words, the “white target” has a flat (constant) absorption coefficient in a spectral range of interest (say, between the c400-700 nm spectral range). The refraction index of the “white target” can be high or low, resulting in black, gray or white color in the image. 3. The label, either by its size which is known to the user or by the addition thereto of scale marks, also serves as a scale. FIG. 50 shows two labels (marked as “Up” and “Anterior”, respectively) placed around a craniotomy marking the “Up” direction and the “Anterior” direction of the craniotomy (thereby providing a “North”-“South” type orientation). These labels are placed by the neurosurgeon at the beginning of the imaging session and greatly simplify the task of understanding the orientation of the image presented on the monitor. The system's software is designed to automatically find the “white target” area within an acquired image and to extract an average spectrum from it, which average spectrum serves as the W(&lgr;) (see Equation 15) mentioned hereinabove with respect to the oxygen saturation calculation. Having W(&lgr;) so sampled eliminates the necessity to acquire a separate white target spectral-image at the beginning of the imaging session. It further eliminates the need of using an illumination source with known spectral characteristics and allows for using the illumination already present in the operating room without introducing special dedicated illumination for illuminating the cortex. When implementing this embodiment of the present invention, i.e., co-acquiring the white target within the image, it is important to have the refraction index of the white target close to (say ±10% or ±20%) the refraction index of the cortex. If the white target is brighter then the cortex it will become saturated at a light level such that the cortex will not be illuminated sufficiently. 
 EXAMPLES Reference is now made to the following examples, which together with the above descriptions, illustrate the invention in a non limiting fashion. Spectral data presented herein was acquired using SPECTRACUBE 300 spectral imaging device manufactured and distributed by Applied Spectral Imaging Ltd., Migdal Haeemek, Israel. The procedures described herein were approved by a Helsinki Committee. After they were explained of the procedures and their experimental nature, all patients reported herein signed an informed consent prior to operation. 
 Example 1 
 fMRI vs. Exposed Cortex Images Obtained via Spectral Imaging This example demonstrates the difference between preoperational images (be it CT, PET or fMRI) and the way the exposed cortex appears to the operating neurosurgeon during operation. FIG. 14 shows a T1-weighted image acquired to localize anatomy within which evoked function will be imaged. The brain is segmented to create a binary mask for application to the fMRI image. FIG. 15 shows an fMRI image acquired during photic stimulation. FIG. 16 shows the masking of FIG. 15 with the T1 brain mask segments activity localized to the brain. As shown in FIG. 16 , selection of a given threshold reveals areas of evoked response function. These fMRI images were taken from the web site of the Mayo clinic (USA), (http://www.mayo.edu/) and present typical fMRI results. FIG. 10 shows a color (RGB) image reconstituted from spectral data acquired on awake patient undergoing neurosurgery. Comparing the fMRI images of FIGS. 14 - 16 to the color image of FIG. 10 , which is identical to the view seen by the operating surgeon, reveals that interpreting brain anatomy from the fMRI image, is not a trivial task. Furthermore, the anatomy of the brain changes to a great extent post craniotomy due to the inner-cortical pressure, which changes are not at all addressed by preoperational images. 
 Example 2 
 Calculating Oxygen Saturation Difference Maps by Applying Various Thresholds The images shown herein were derived from a 58 year-old female, diagnosed for a right parietal enhancing tumor (GBM), which underwent tumor resection under general anesthesia. The images shown in FIGS. 17 - 27 are difference maps created by comparing a base image with an image acquired post left palm electrical stimulation and demonstrate the importance of using thresholds when highlighting oxygen saturation differences in accordance with the teachings of the present invention. Overall oxygen saturation values in this patient are low and represent a typical values of a patient under general anesthesia. The patient was respirated and monitored with the following physiological parameters: Respiration Rate—10 per minute Total Volume—0.7 liter Blood Pressure—125/60 End Tidal CO 2 —30 mmHg Medication: Remphentanil 0.18 &mgr;l/kg/minute; Propofol 30 mg/hour. FIG. 17 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels (in red) corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 40% and have risen by more then 1% post left palm electrical stimulation. FIG. 18 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels (in red) corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 45% and have risen by more then 1% post left palm electrical stimulation. FIG. 19 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels (in red) corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 50% and have risen by more then 1% post left palm electrical stimulation. FIG. 20 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels (in red) corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 55% and have risen by more then 1% post left palm electrical stimulation. FIG. 21 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels (in red) corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 60% and have risen by more then 1% post left palm electrical stimulation. FIG. 22 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels (in red) corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 65% and have risen by more then 1% post left palm electrical stimulation. FIG. 23 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels (in red) corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 70% and have risen by more then 1% post left palm electrical stimulation. FIG. 24 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 65% and have risen by more then 1% (red) or less than 1% (yellow) post left palm electrical stimulation. FIG. 25 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 65% and have risen by more then 3% (red) or less than 3% (yellow) post left palm electrical stimulation. FIG. 26 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 65% and have risen by more then 5% (red) or less than 5% (yellow) post left palm electrical stimulation. FIG. 27 shows an oxygen saturation difference map overlaid on a monochromatic gray-scale image of the cortex highlighting pixels corresponding to brain regions (picture elements) that underwent an increase in oxygen saturation (OS) that reached an OS level greater then 65% and have risen by more then 10% (red) or less than 10% (yellow) post left palm electrical stimulation. These Figures demonstrate the importance of using two thresholds, the first relates to the relative (or absolute) value of oxygen saturation, whereas the second relates to the change thereof post stimulation. It will be appreciated that a similar analysis can be made with respect to blood volume, which takes into account the sum value of oxy and deoxy-hemoglobin. 
 Example 3 
 Wernike's Area Mapping During Awake Craniotomy An 80 years old male diagnosed with lung cancer 12 years prior to admission for a left temporal cystic lesion (found to be a metastasis). The patient suffers from cognitive dysfunction (anterograde amnesia) and dysphasia. fMRI imaging showed Wernike's Area to be located adjacent to the tumor on the Superior Temporal Gyros (STG), see FIGS. 28 - 30 . FIG. 28 shows an fMRI image demonstrating the activation of Wernike's area (the orange spot on the right. FIG. 29 is a CT image showing a section of the brain, the tumor is clearly seen on the right-hand side (actually the left hemisphere of the brain). FIG. 30 is a gray-scale orientation image as observed by the spectral imaging device employed. The patient underwent awake craniotomy for tumor resection. Physical parameters during craniotomy: OS 100% —measured using pulse oxymeter on toe. PCO 2 —38 BP 80 systole Medication at this stage: Propofol 70 mg/minute Remiphentanil 01 &mgr;l/kg/minute Post craniotomy the Propofol is stopped and the BP rises to 100 systole. The patient is now awake and asked to start performing different tasks. At 17:14 the patient performs a counting task in his native language (Polish), a task in which he succeeds. At 17:15 he is asked to translate words from Hebrew to Polish, a task in which he succeeds. FIGS. 31 and 32 show color coded oxygen saturation maps of the patient's cortex pre and post translation task. The data represented by these images was used for locating Wernike's area (see FIG. 33 ), which is only activated by the more cognitively complex task of translation. 
 Example 4 
 Motor Cortex and Associated Speech Areas During Awake Craniotomy A 50-year-old male diagnosed one year ago with melanoma. Recent headaches led to diagnosis of metastasis. CT showed a single tumor strand in left temporal area (see FIG. 34 ). fMRI showed dominant Broca (see FIG. 35 ). The patient underwent awake craniotomy for tumor resection. At 11:18 an acquisition is performed while the patient is naming different objects (pen, cigarettes, etc.). At 11:21 an acquisition is performed while the patient is asked to repeat sentences, which he hears. This is called a repetition task. The patient had a hard time repeating the sentences. In any case, FIG. 37 shows an oxygen saturation difference map highlighting speech-associated areas. Later attempts to locate dominant speech area (Broca) using direct cortical stimulation (via electrode placed in contact with the motor cortex), while the patient was performing a speech task, failed to cause any speech disturbances. This leads to the conclusion that the Broca itself was not included in the craniotomy. At 11:23 an acquisition is performed while the patient is touching fingers of his right hand (he performed well). At 11:25 an acquisition is performed during a mouth movement task (the patient was told to open and close his mouth and performed well). These two later acquisitions were used for creating oxygen saturation difference maps highlighting changes in the motor cortex (FIGS. 38 - 39 ) as a result of touching fingers and open and close mouth tasks. 
 Example 5 
 Associated Visual Cortex Mapping During General Anesthesia A 72 years-old female. Progressive dysphasia and headaches led to diagnosis of a large left parieto-occipital enhancing tumor (GBM). Underwent left occipital craniotomy under general anesthesia. Optokinetic stimulation to an open left eye was performed after dural opening. Physical parameters during craniotomy: Patient placed in “park bench” position. 100% OS (pulse oxymeter) PO 2 450 PCO 2 35 Medications: Remphentanil 0.1 &mgr;l/kg/minute Propofol 100 mg/hour Aontopintunine 12 &mgr;l/kg/minute At 11:03 a base image was acquired post craniotomy. The lid of the left eye was opened using a separating tool. The pupil was visible and small. At 11:10 data acquisition was performed while an illuminated Opto-kinetic strip was passed before the patient's left eye. FIG. 42 is a gray-scale orientation image as observed by the spectral imaging device employed. FIGS. 40 and 41 show color coded oxygen saturation maps of the patient's cortex pre and post passive optical left eye stimulation. The data represented by these images was used for locating visual associated cortex regions (see FIG. 43 ). It is assumed that the primary visual cortex is not visible in the craniotomy. The larger red area in the lower portion of the map highlights an area affected by the optical stimulation. The anatomy implies, however, that this area is not the primary visual cortex, rather an associated area. It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. 
 Example 6 
 Cumulative Difference Display FIGS. 51 a - e show a set of difference images calculated during several imaging sessions, where visual stimulation was performed on a patient under full anesthesia conditions by flashing light into an eye and alternatively by passing objects in front of the eye. FIG. 52 shows a cumulative differences display according to the present invention, which integrates, overlays or superimposes the data of FIGS. 51 a - e, to thereby better define brain regions which are active following visual stimulation. FIGS. 51 a - e and as a result also FIG. 52 show the spatial arrangement of increase in oxygen saturation. Decreased oxygen saturation is not presented, yet it can be displayed in a similar fashion. In addition, both regions characterized by increase and decrease of oxygen saturation can be co-displayed in an overlaid fashion as in FIG. 52 . The need for cumulative display arises because during higher-level stimulation protocols (such as those used for detecting speech associated functional cortical areas), the activated brain regions are typically not very well spatially defined, rather different regions, within the exposed imaged cortex are highlighted in different sessions or when employing different stimulation. It is therefore in many cases insufficient for the neurosurgeon to view any one particular difference image (as in FIGS. 51 a - e ) in order to distinctively identify the active cortical areas associated with a function. The cumulative differences display described herein overcomes this problem, by presenting all of the information in a single, superior image. Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims. All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention.