Abstract:
The disclosure relates to Method and Apparatus for Super Montage Large area Spectroscopic Imaging. In one embodiment of the disclosure, a method for producing a spectroscopic image of an object includes the steps of (a) irradiating the object with light to thereby produce from the object scattered and/or emitted (interchangeably “scattered”) light for each of a plurality of wavelengths; (b) producing separately for each of the plurality of wavelengths a plurality of substantially contiguous sub-images of the object; (c) compensating for spatial aberrations in ones of the sub-images for a select one of the plurality of wavelengths; (d) compensating for intensity aberrations between ones of the substantially contiguous sub-images for one of the plurality of wavelengths; and (e) combining the sub-images for the select one wavelength to thereby produce said spectroscopic image of the object.

Description:
The instant application claims the filing-date benefit of Provisional Application No. 60/575,090 filed May 5, 2004, the specifications of which is incorporated herein in its entirety. 

   BACKGROUND 
   Spectroscopic imaging combines digital imaging and molecular spectroscopy techniques, which can include Raman scattering, fluorescence, photoluminescence, ultraviolet; visible and infrared absorption spectroscopies. When applied to the chemical analysis of materials, spectroscopic imaging is commonly referred to as chemical imaging. Instruments for performing spectroscopic (i.e. chemical) imaging typically comprise image gathering optics, focal plane array imaging detectors and imaging spectrometers. 
   In general, the sample size determines the choice of image gathering optic. For example, a microscope is typically employed for the analysis of sub micron to millimeter spatial dimension samples. For larger objects, in the range of millimeter to meter dimensions, macro lens optics are appropriate. For samples located within relatively inaccessible environments, flexible fiberscopes or rigid borescopes can be employed. For very large scale objects, such as planetary objects, telescopes are appropriate image gathering optics. 
   For detection of images formed by the various optical systems, two-dimensional, imaging focal plane array (FPA) detectors are typically employed. The choice of FPA detector is governed by the spectroscopic technique employed to characterize the sample of interest. For example, silicon (Si) charge-coupled device (CCD) detectors or CMOS detectors are typically employed with visible wavelength fluorescence and Raman spectroscopic imaging systems, while indium gallium arsenide (InGaAs) FPA detectors are typically employed with near-infrared spectroscopic imaging systems. 
   Spectroscopic imaging can be implemented by one of several methods. First, dispersive point or line illumination spectrometer can be raster-scanned over the sample area to create a map of the spectroscopic content. Second, spectra can be collected over the entire area simultaneously, using an active optical imaging filter such as AOTF or LCTF. Here the materials in these optical filters are actively aligned by applied voltages or acoustic fields to produce the desired bandpass and transmission function. 
   Raster scanning the sample with a dispersive spectrometer having a stable transmission is rather time consuming. The second approach, while providing a more expeditious scanning method, can introduce inaccuracies caused by temperature variation and device-specific characteristics. Any inconsistency in the material and its orientation over the optical surface will modify the transmission function both spatially and over time. Accordingly, there is a need for a method and apparatus for super montage rapid imaging system to provide spectroscopic image of a large area. 
   SUMMARY OF THE DISCLOSURE 
   The disclosure relates to a method and apparatus for super montage rapid imaging system. In one embodiment of the disclosure, a method for producing a spectroscopic image of an object includes the steps of (a) irradiating the object with light to thereby produce from the object scattered and/or emitted (interchangeably “scattered”) light for each of a plurality of wavelengths; (b) producing separately for each of the plurality of wavelengths a plurality of substantially contiguous sub-images of the object; (c) compensating for spatial aberrations in ones of the sub-images for a select one of the plurality of wavelengths; (d) compensating for intensity aberrations between ones of the substantially contiguous sub-images for one of the plurality of wavelengths; and (e) combining the sub-images for the select one wavelength to thereby produce said spectroscopic image of the object. 
   In another embodiment, the disclosure relates to an improved method for reducing aberrations in a spectroscopic image comprising plural substantially contiguous sub-images having plural pixels each pixel having an initial intensity value, where the plural sub-images are each produced using an active optical imaging filter, the improvement comprising the steps of (a) compensating for spatial aberrations; and (b) compensating for intensity aberrations. 
   In still another embodiment, the disclosure relates to a spectroscope for irradiating an object with light to thereby produce from the object scattered light for each of a plurality of wavelengths and to produce separately for each of the plurality of wavelengths a plurality of substantially contiguous sub-images of the object. The spectroscope includes a processor programmed to perform a plurality of executable instructions, the instructions comprising: (a) compensating for spatial aberrations in ones of the sub-images for a select one of the plurality of wavelengths; (b) compensating for intensity aberrations between ones of the substantially contiguous sub-images for one of the plurality of wavelengths; and (c) combining the sub-images for the select one wavelength to thereby produce said spectroscopic image of the object. 
   In another embodiment, the disclosure relates to a system for reducing aberrations in a spectroscopic image comprising plural substantially contiguous sub-images having plural pixels each pixel having an initial intensity value, where the plural sub-images are each produced using an active optical imaging filter, and where the system includes a processor programmed to perform a plurality of executable instructions. The instructions including: (a) compensating for spatial aberrations; and (b) compensating for intensity aberrations. 
   In still another embodiment, the disclosure relates to a method for producing a corrected spectroscopic sub-image of an object comprising the steps of (a) irradiating the object with light to thereby produce from the object scattered light for each of a plurality of wavelengths; (b) producing separately for each of the plurality of wavelengths a plurality of substantially contiguous sub-images of the object; (c) compensating for spatial aberrations in ones of the sub-images for a select one of the plurality of wavelengths; and (d) compensating for intensity aberrations between ones of the substantially contiguous sub-images for one of the plurality of wavelengths. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a schematic representation of a super montage or large area representation of a Raman image; 
       FIGS. 2A-2C  show a super montage image as processed through LISAR and NMC according to an embodiment of the disclosure; 
       FIG. 3  shows the transmission spectrum of an exemplary LCTF device; 
       FIG. 4  shows the effect of temperature on LCTF performance; and 
       FIG. 5  schematically illustrates the normalization process according to one embodiment of the disclosure. 
   

   DETAILED DESCRIPTION 
   The disclosure generally relates to addressing different aspects of LCTF transmission aberrations. Applicants have identified at least two processes for addressing different aspects of LCTF transmission aberrations. A first process is directed to the spatial aberration of the LCTF (the LISAR process) and the second process is directed to the discontinuous transmission pattern of the LCTF as a function of wavelength (the NMC process). 
     FIG. 1  is a schematic representation of a super montage or large area representation of a Raman image. In a super montage a number of large area images are combined to form a collection or a montage. The montage can cover an extensive area of the sample in the X and Y coordinates as shown in  FIG. 1 . In addition, the montage can include images of the sample collected at different wavelengths (λ). Thus, a combination of images  10 ,  20 ,  30  and  40  over a sample occupying a space forms the montage. Each image  10  contains a combination of several sub-images  12 ,  14  at a given wavelength. Sub-images  12 ,  14  comprise a plurality of pixels  16 . Each pixel  16  identifies a wavelength λ having a particular intensity corresponding to the Raman properties of the underlying sample. 
     FIGS. 2A-2C  show a super montage image of a sample as processed through LISAR and NMC according to an embodiment of the disclosure. Particularly,  FIG. 2A  shows bias normalized super montage image  200 . Image  200  includes 16 sub-images. Each sub-image is shown as an integrated square. Each sub-image further comprises a multitude of pixels each having a different intensity. By examining the bias normalized montage of  FIG. 2A  several aberrations or patterns of aberrations emerge. 
   Generally, the aberrations can be categorized into one of two categories. The first pattern of aberration is a repeating pattern that seems imprinted on each field of view (“FOV”) of each sub-image. Moreover, it appears differently at different LCTF set points of the LCTF. Applicants have identified aberration patterns of the first type as LCTF spatial aberration. The second type aberration pattern is the variable brightness of the FOV within the montage. Thus, in one embodiment, the disclosure relates to identifying and compensating for each of the first- and second-type aberration patterns. 
   Applicants have observed that in a montage formed from multiple FOV images, each FOV had a characteristic spatial pattern that seemed to repeat in every sub-frame of the montage. Moreover, the aberration appeared different at different LCTF set points. This aberration can be seen with reference to  FIGS. 2A and 2B . In  FIG. 2A  the montage Raman image  200  is bias normalized. Each of the sub-images (for example, sub-images  210  and  220 ) includes fading on the top left corner of the image. This aberration appears in virtually every sub-image and, as stated, it is considered to be a characteristic of the LCTF. 
   According to one embodiment of the disclosure, LCTF Inhomogeneous Spatial Aberration Removal (“LISAR”) filtration process comprises the steps of (1) for each pixel in a group of pixels, defining a mean intensity value and a spatially-filtered mean intensity value; (2) determining an overall average of the spatially-filtered pixel intensity values; (3) for each pixel in the selected group of pixels, determining the difference in intensity value between the mean intensity value and the overall average intensity value; and (4) for each pixel in a sub-image subtracting the intensity value of step (3) from the original intensity value. 
   Referring to  FIG. 1 , the exemplary LISAR steps can be illustrated as follows. First, for each pixel  16  in a group of pixels  14 , a mean intensity value is determined. In one exemplary embodiment, the mean intensity value can be determined by calculating the intensity value for a group of pixels (e.g., all pixels positioned at position (1, 1) within each sub-image. The first step can be followed by determining a spatially-filtered mean intensity value for each pixel in the selected group of pixels. Referring to  FIG. 1 , the step of determining a spatially-filtered mean intensity value can be accomplished by obtaining a mathematical average of a pixel&#39;s (e.g., pixel  16 ) intensity value as compared with its immediately-adjacent pixels. Using the spatially-filtered mean intensity thus obtained, an overall average of the spatially-filtered pixel intensity values for the sub-image can be determined. Next, the overall average of the spatially-filtered mean intensity for each pixel  16  of the sub-image  14  is subtracted from the spatially-filtered mean intensity value of the corresponding pixel  16  of the sub-image  14  to define an interim intensity value for each pixel  16  in sub-image  14 . Finally, the interim intensity value at each pixel is subtracted from the original intensity value at the corresponding pixel. Replacing the original intensity value for each pixel  16  in sub-image  14  with the final intensity value completes the LISAR filtration process. 
   Referring again to  FIG. 2 ,  FIG. 2A  shows bias normalized super montage image  200 .  FIG. 2B  shows the image of  FIG. 2A  post LISAR filtration process. It can be readily seen from  FIGS. 2A and 2B  that LISAR filtration technique effectively removes spatial aberration that appear in many of the sub-images of  FIG. 2A . 
   Once the problems attributed to spatial aberration are addressed, the montage images may include sub-images having different intensities. For example, referring to  FIG. 2B , it can be seen that certain sub-images are substantially lighter (i.e., have higher intensity) than other sub-images. This aberration can stem from the non-uniform transmission of the LCTF as a function of bandpass setting. The transmission function of the LCTF is considered to have a discontinuous transmission as a function of wavelength.  FIG. 3  shows the transmission spectrum of an exemplary LCTF device. Referring to  FIG. 3 , the sharp transitions in the highlighted regions are considered to contribute to the aberration. 
   Addressing this problem according to one embodiment of the disclosure includes compensating for intensity aberrations between substantially contiguous sub-images for one of several wavelengths and combining the selected wavelengths to produce a spectroscopic image of the object. The discontinuities in transmission do not appear constant in time, in part due to temperature fluctuation of the instrument. 
     FIG. 4  shows the effect of temperature on LCTF performance. Referring to  FIG. 4 , the identical experiment was performed using the same high-resolution instrument at three different occasions under different LCTF temperatures. The overall structure of the curves is similar, with sharp discontinuities in a similar pattern across the experiments&#39; wavelengths. However, the spectrum seems to be translated left to right depending on the temperature. This aberration is of particular importance when a series of contiguous fields of view are imaged in succession over a relatively long period of time (several hours). Over the course of the experiment, temperature fluctuations of the apparatus will lead to shifting of the above-spectrum from right to left. If during the experimental setup one of the chosen bandpass settings is near a discontinuity, the filter may have dramatically different transmission characteristics at different temperatures. 
   A study of the montage experimental data shows that the frames with discontinuous brightness occur in the regions identified in  FIG. 4 . As stated, the discontinuities in the brightness can be attributed to temperature characteristics of the device. The device characteristics are particularly important for sampling and data interpretation. For example, for Raman imaging utilizing a narrow band the device&#39;s temperature dependence can cause a substantial bandpass shift. 
   The abnormalities observed in the data stems from the non-uniformity of the LCTF transmission over the entire area of the filter. Without wishing to be bound to a specific theory, the following aspects are considered to cause the spatial non-uniformity (1) thickness non-uniformity of liquid crystal cells over certain aperture; (2) different material with different thermal and mechanical expansion/contraction characteristics coupled with the effect of mechanical strain on the liquid crystal components which can lead to stress-induced birefringence effect; and (3) the mismatch of refractive indices of the layers which creates high-order interference pattern when the LCTF is tuned to certain wavelengths (e.g., 700 nm). The system used as a prototype showed about 2%±0.5 over the field of view. Although small, the pre-processing steps of bias correction and normalization can amplify the effect such that the spatial variability in transmission dominates the dynamic range of the data after correction. 
   The above-disclosed principles can also be used to address and remove aberrations appearing in certain fluorescence-illuminated imaging systems. Additionally, the same principles can be used to address aberrations caused by non-uniform illumination of the sample. A baseline operation can, on occasion, be substituted for the LISAR process. 
   According to one embodiment of the disclosure, a method of compensating for intensity aberrations between sub-images of a mosaic image of a sample at a number of wavelengths (“NMC”) includes normalizing the intensity value of the pixels of a sub-image and re-centering a mean intensity value for the select group of pixels in a sub-image. 
   In an exemplary embodiment normalizing the intensity value includes several steps. First, the norm intensity value of all pixels in a sub-image and the norm intensity value for all pixels in each of the corresponding sub-images are defined. For the normalization step, the term corresponding pixels refers to pixels located at the same relative position but at a different wavelength. Second, for each pixel in a select group of pixels, the intensity value can be divided by the norm to obtain a normalized intensity value. Finally, the normalized intensity value can be multiplied by the number of pixels in a sub-image and the number of wavelengths to obtain the global normalized value. 
     FIG. 5  schematically illustrates the normalization process according to one embodiment of the disclosure. Referring to  FIG. 5 , images  200 ,  300  and  400  represent a sample at different wavelengths. In an exemplary embodiment, there may be sixteen sub-images defining the sample at sixteen different wavelengths. In one exemplary embodiment, each image can further comprise at least sixteen sub-images where each sub-image further includes sixteen pixels. By way of example, image  400  is shown with sixteen sub-images. Sub-image  410  also includes sixteen pixels  421 . Each pixel defines a particular pixel intensity I 0 . As a first step of an exemplary normalization process the intensity value for each pixel is normalized with respect to its subimage neighbor pixels and corresponding wavelength pixels. Referring to  FIG. 5 , the intensity value of pixel  421  can be normalized (I norm ) in view of all of its corresponding pixels ( 321 ,  221 , etc.) from the other fifteen sub-images ( 300 ,  200 , etc.) In other words, normalized intensity for a given pixel can be defined in terms of all of its corresponding pixel intensity values: 
   
     
       
         
           
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   Next, the ratio of (I 0 norm =I 0 /I norm ) can be determined. This process can be repeated for each of the sixteen exemplary pixels in each sub-image. Using the values of I 0 norm  a sub-image can be formed having sixteen pixels where the intensity of each pixel is normalized as exemplified by I 0 norm . For each pixel, the value of I 0 norm  can be multiplied by the number of pixels in the sub-image and the wavelengths corresponding to each sub-image. In the exemplary embodiment of sixteen sub-images each having sixteen pixels, this would translated to I 0 norm ×16×16×3. The resulting value is the global normalized value. 
   The process of re-centering a mean intensity value for the select group of pixels in a sub-image includes obtaining the global mean intensity value as a function of the initial intensity (I 0 ) value of pixels in plural sub-images for a predetermined wavelength. The global mean intensity value (I global mean ) can be defined as a function of all sub-images having the same wavelength. Next, a local mean intensity (I local mean ) value can be obtained as a function of initial intensity value (I 0 ) for one sub-image. For each pixel the intensity difference (I Δ ) between the original pixel intensity value (I 0 ) and the local mean intensity value (I local mean ) can be obtained. Finally, the re-centered intensity value (I re-centered ) can be obtained by combining the I Δ  and I global mean . Using the re-centered intensity value (I re-centered ) one or more of the sub-images can be corrected. 
   Referring once again to  FIG. 2 ,  FIGS. 2B and 2C  show a super montage image as processed by the NMC process according to an embodiment of the disclosure. As stated,  FIG. 2B  shows post LISAR image. Applying the NMC filtration process as described above, yields the embodiment shown in  FIG. 2C . A comparison of  FIGS. 2A and 2C  illustrate the advantages of the principles disclosed herein in providing a more coherent image of the sample. The LISAR and NMC processes substantially reduce spatial aberration and compensate for intensity aberrations in a spectroscopic image from plural sub-images each having many pixels. While the LISAR process can compensate for the spatial aberrations, the NMC process can reduce intensity aberrations. 
   While the principles of the disclosure have been described in relation to specific exemplary embodiments, it is noted that Applicants&#39; disclosure is not limited thereto and include any permutation, variation and modification to the principles disclosed herein.