Abstract:
A calibrated real-time, high energy X-ray imaging system is disclosed which incorporates a direct radiation conversion, X-ray imaging camera and a high speed image processing module. The high energy imaging camera utilizes a Cd—Te or a Cd—Zn—Te direct conversion detector substrate. The image processor includes a software driven calibration module that uses an algorithm to analyze time dependent raw digital pixel data to provide a time related series of correction factors for each pixel in an image frame. Additionally, the image processor includes a high speed image frame processing module capable of generating image frames at frame readout rates of greater than ten frames per second to over 100 frames per second. The image processor can provide normalized image frames in real-time or can accumulate static frame data for substantially very long periods of time without the typical concomitant degradation of the signal-to-noise ratio.

Description:
FIELD OF THE INVENTION  
       [0001]     The present invention is in the field of semiconductor imaging systems for imaging x-ray and gamma ray radiant energy. More specifically, the invention relates to a high energy charge-integrating imaging devices utilizing Cd—Te or Cd—Zn—Te based detector substrates in combination with CMOS readout substrates. Additionally, the invention relates to a process for calibrating such high energy radiation imaging systems.  
       BACKGROUND OF THE INVENTION  
       [0002]     Over the past ten years digital radiation imaging has gradually been replacing conventional radiation imaging for certain applications. In conventional radiation imaging applications, the detecting or recording means is a photosensitive film or an analog device such as an Image Intensifier. Digital radiation imaging is performed by converting radiation impinging on the imaging device (or camera) to an electronic signal and subsequently digitizing the electronic signal to produce a digital image.  
         [0003]     Digital imaging systems for producing x-ray radiation images currently exist. In some such devices, the impinging or incident radiation is converted locally, within the semiconductor material of the detector, into electrical charge which is then collected at collection contacts/pixels, and then communicated as electronic signals to signal processing circuits. The signal circuits perform various functions, such as analog charge storing, amplification, discrimination and digitization of the electronic signal for use to produce an digital image representation of the impinging radiation&#39;s field strength at the imaging device or camera. These types of imaging systems are referred to as “direct radiation detection” devices.  
         [0004]     In other devices, the impinging radiation is first converted into light in the optical or near optical part of the visible light spectrum. The light is subsequently converted to an electronic signal using photo detector diodes or the like, and the resultant electronic signal is then digitized and used to produce a digital image representation of the impinging radiation&#39;s field strength at the imaging device or camera. This type of imaging system is referred to as an “indirect radiation detection” device.  
         [0005]     Currently, operation of a flat panel imaging device/camera (of either the direct type or indirect type of detector) typically involves collecting and integrating a pixel&#39;s charge over a period of time and outputting the resultant analog signal which is then digitized. Present charge integration times are typically from 100 msec to several seconds. Devices presently available in the field are suitable for single exposure digital x/gamma-ray images, or for slow multi-frame operation at rates of up to 10 fps (frames per second). The digitization accuracy typically is only about 10 bits, but can be 14 to 16 bits if the charge integration time is sufficiently long. The high end of digitization accuracy currently is accomplished in imaging systems wherein the typical charge integration times range from several hundred milliseconds up to a few seconds. Therefore, in these current imaging systems, increasing accuracy requires increasing the pixel charge integration time. Unfortunately, errors inherent in current imaging systems limit the length of a charge integration cycle to just a few seconds at most, before the signal-to-noise ratio first “saturates” and then becomes so bad as to preclude any increase in accuracy with increasing charge integration time.  
         [0006]     In any event, it is the cumulative integrated analog signal that is readout from the camera and digitized. Then calibration is applied to correct the non-uniformities inherent in flat panel imaging device, and more rarely to correct the non-linear behavior of the imaging system itself.  
         [0007]     Designing and manufacturing a sensitive, high energy radiation-imaging device is a very complex task. All the device&#39;s structural modules and performance features must be carefully designed, validated, assembled and tested before a fully functioning camera can be constructed. Although great progress has been made in the research and development of semiconductor radiation imaging devices, a large number of old performance issues remain and certain new performance issues have developed. Some of the new performance issues result from solving other even more severe performance problems, while some are intrinsic to the operating principle of such devices.  
         [0008]     High energy “direct radiation detector” type x-ray imaging systems typically utilize semiconductor detector substrate composed of Cd—Te or Cd—Zn—Te compositions. The Cd—Te or the Cd——Zn—Te detector substrate is typically bump-bonded to a CMOS readout (signal processing) substrate. It can also be electronically connected to the CMOS readout with the use of conductive adhesives (see US Patent Publication No. 2003/0215056 to Vuorela). Each pixel on the CMOS readout substrate integrates the charge generated from the absorption the impinging x/gamma rays in the thickness of material of the detector substrate. The known performance impacting issues with Cd—Te or Cd—Zn—Te/CMOS based charge-integration devices can be divided into two major areas: electrical performance problems and materials/manufacturing defects. Electrical performance problems can be further subdivided into six different though partially overlapping problems: leakage current, polarization or charge trapping, temporal variation, temperature dependency, X-ray field non-uniformity, and spectrum dependency. Materials/manufacturing defects problems can also be further subdivided into: Cd—Te or Cd——Zn—Te detector material issues, CMOS-ASIC production issues, and overall device manufacturing issues.  
         [0009]     The main reasons for use of crystalline compound semiconductors such as CdTe and CdZnTe in the detector substrate of a charge-integrating imaging device is their superb sensitivity, excellent pixel resolution, and quick response (very little afterglow) to incoming radiation. On the other hand, current methods of producing Cd—Te and Cd—Zn—Te flat panel substrates limits their uniformity and impacts the crystal defect rate of these materials, which as can cause some of the problems mentioned above. In addition, due to the use of an electric field of the order of 100V/mm or higher, a considerable leakage current (or dark current) results, causing image degradation.  
         [0010]     Prior descriptions of Cd—Te or Cd——Zn—Te based x-ray/gamma ray imaging devices exist. For example, U.S. Pat. No. 5,379,336 to Kramer et al. and U.S. Pat. No. 5,812,191 to Orava et al. describe generally the use of Cd—Te or Cd—Zn—Te semiconductor detector substrates bump-bonded to ASICs substrates of a charge-integration type digital imaging camera. However, these documents make no mention of and do not address the issues arising when a device of this type operates at high frame rates exceeding 10 fps, or how to calibrate, or even the need to calibrate in the case of such an application. Another example is European Patent EP0904655, which describes an algorithm for correcting pixel values of a Cd—Te or Cd—Zn—Te imaging device. However the issue of operating the device at high rates and how to compose an image from many uncorrected individual frames is not addressed. EP0904655 simply provides a correction algorithm for correcting pixel values from a single exposure and consequently displaying such pixel values.  
         [0011]     Although these prior devices and methods may be useful each for its intended purpose, it would be beneficial in the field to have a high energy x-ray, real time imaging system that provides both increased image frame readout rates of substantially greater than 1 Ofps and greater than 16 bit accuracy. For example, it would be useful in the fields of panoramic dental imaging, cephalometry, and computerized tomography to have high energy X-ray imaging systems with both increase frame readout rates and high accuracy. Even static imaging applications, where the exposure time is a multiple of the single frame duration, it would be useful to have such an imaging system.  
       SUMMARY OF THE INVENTION  
       [0012]     The present invention is a high energy, direct radiation conversion, real time X-ray imaging system. More specifically, the present real time X-ray imaging system is in tended for use with Cd—Te and Cd—Zn—Te based cameras. The present invention is particularly useful in X-ray imaging systems requiring high image frame acquisitions rates in the presence of non linear pixel performance. The present invention is “high energy” in that it is intended for use with X-ray and gamma ray radiation imaging systems having a field strength of 1 Kev and greater. The high energy capability of the present X-ray imaging system is derived from its utilization of detector substrate compositions comprising Cadmium and Telluride (e.g., Cd—Te and Cd——Zn—Te based radiation detector substrates) in the imaging camera. Cd—Te and Cd——Zn—Te based detector substrates define the present invention as being a direct radiation conversion type detector, because the impinging radiation is directly converted to electrical charge in the detector material itself.  
         [0013]     The detector substrate is a monolith and has a readout face or surface which is highly pixelized, i.e., it has a high density pattern of pixel charge collectors/electrodes on it. The pattern is high density in that the pitch (distance from center-to-center) of the pixel charge collectors is 0.5 mm or less. Each pixel&#39;s collector/electrode is in electrical communication (e.g., via electrical contacts such as bump-bonds or conductive adhesives) to the input of a pixel readout ASIC on the readout/signal processing substrate. The detector substrate provides for directly converting incident x-rays or gamma radiation to an electrical charge and for communicating the electrical charge signals via the pixel electrical contact to the readout ASIC. The readout/signal processing ASIC provides for processing the electrical signal from its associated pixel as necessary (e.g., digitizing, counting and/or storing the signal) before sending it on for further conditioning and display. The capability of the present invention to be read out at high frame rates enables the real time imaging feature. Real time imaging refers to the capability of the system to generate image frames for display in sufficiently rapid succession to provide a moving picture record in which movement appears to occur substantially real time to the human eye.  
         [0014]     Descriptions of flat panel x-ray imaging cameras substantially analogous to the intended Cd—Te or a Cd——Zn—Te based charge-integrating detector bonded to an ASIC readout/signal processing substrate are known in the art. Examples are disclosed in US Patent Application Publication serial number 2003-0155516 to Spartiotis et al. relating to a Radiation Imaging Device and System, and US Patent Application Publication serial number 2003-0173523 to Vuorela relating to a Low Temperature, Bump-Bonded Radiation Imaging Device, which documents are incorporated herein by reference as if they had been set forth in their entirety.  
         [0015]     In a preferred embodiment of the present imaging system, the imaging device or camera is “readout” at a high frame rate. A high frame rate as used herein means that the accumulation and distribution of electrical charge developed in the detector semiconductor substrate is utilized (“readout”) to produce a digital image frame at a rate greater than about 10 individual image frames per second up to 50 and greater individual image frames per second. An individual image frame is a digital representation of the active area (pixel pattern) of the camera&#39;s detector substrate. An image frame is generated each time the ASIC substrate is readout. The digital representation can be described as a matrix of digitized individual pixel signal values. That is, each pixel value of each pixel in the image frame is a digitized representation of the intensity of the electronic signal level readout for the corresponding specific pixel on the detector substrate.  
         [0016]     Additionally, each pixel value in the image frame includes an individual calibration correction specific to that pixel value, and therefore in fact is a corrected digital pixel value. The specific calibration correction for each image pixel is derived from the present pixel value correction calibration process. The individual corrected digital pixel values of the same specific image pixel from different image frames is processed according to an algorithm of the calibration process over at least some of the collected image frames to provide the pixel value to be displayed in the final image. Therefore, it is a further object of the present invention to provide a calibration (or correction) method to enable the current invention to be implemented. The calibration method is applicable on each pixel of the imaging system and takes into account the offset and gain corrections as well as temporal (time) corrections as this is applied on a frame by frame basis. There maybe no need to have different correction for each pixel and each frame but in accordance with the current invention at least some of the frames have different temporal correction for corresponding pixels.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0017]      FIG. 1  is a block diagram generally illustrating the interconnect relationship of components of the present high energy, direct radiation conversion, real time X-ray imaging system  
         [0018]      FIG. 2  is a schematic representation of an imaging device useful in the camera module of the present invention.  
         [0019]      FIG. 3  is a graphic representation of the output over time of a single pixel circuit of a Cd—Te based direct conversion camera using detector bias voltage switching. The figure illustrates that the output signal from a typical pixel circuit drifts over time as circuit recovers from a bias voltage switching event (pulse).  
         [0020]      FIG. 4  is a graph illustrating the temporal variation in the raw intensity value of the same single image pixel of  FIG. 3  overlaid with a series of image frame capture points generated over time after a bias voltage switching event.  
         [0021]      FIG. 5  is a graph illustrating normalization of the intensity value of an image pixel by the application of a specific time dependent correction coefficient to the raw intensity value of the particular image pixel&#39;s output in each image frame.  
         [0022]      FIG. 6  is a graph illustrating an asymmetric data sampling feature of the calibration procedure of the present imaging system for ameliorating the problem of excessive data collection and processing load.  
         [0023]      FIG. 7  is a block flow chart illustrating a general overview of the present calibration procedure.  
         [0024]      FIG. 8  is a block flow diagram illustrating a data collection strategy from a single pixel circuit at a specific reference X-ray field intensity.  
         [0025]      FIG. 9  is a block flow diagram illustrating a strategy for calculating correction coefficients for each image pixel in a pixel frame.  
         [0026]      FIG. 10  is a block flow diagram illustrating a strategy for detecting and compensating for bad or uncorrectable pixels.  
         [0027]      FIG. 11  is a block flow diagram illustrating the application of the present calibration process to provide a normalize image frame.  
         [0028]      FIG. 12A  is a graph illustrating the typical prior uniform sampling method wherein an integration by uniform parts type calculation is used to determine correction coefficient for normalizing pixel intensity values at specific times or intensities to fit a curve.  
         [0029]      FIG. 12B  is a graph illustrating an asymmetric sampling method wherein an integration by increasing parts type calculation is used to determine correction coefficients for normalizing pixel intensity values at specific times.  
         [0030]      FIG. 12C  is a graph illustrating an alternative sampling method wherein an asymmetric linear polynomic calculation is used to determine correction coefficients for normalizing pixel intensity values at specific times.  
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0031]     Referring now to the drawings, the details of preferred embodiments of the present invention are graphically and schematically illustrated. Like elements in the drawings are represented by like numbers, and any similar elements are represented by like numbers with a different lower case letter suffix.  
         [0032]     As illustrated in  FIG. 1 , the present invention is a high energy, real-time capable, direct radiation conversion X-ray imaging system  10 . More specifically, the present invention relates to such X-ray imaging systems  10  utilizing a Cd—Te or Cd——Zn—Te based camera. The present real-time capable X-ray imaging system  10 , like imaging systems generally, comprises a camera module, an image processor  14 , and a display means  16 . In the present real-time X-ray imaging system  10 , the camera module  12  includes an X-ray imaging device  28  having a Cd—Te or Cd——Zn—Te based radiation detector substrate  30  in electrical communication with an Application Specific Integrated Circuit (ASIC) readout substrate  32 . Each active pixel  36  on the detector  30  is electrically connected to a corresponding pixel circuit  31  on the ASIC readout substrate  32 .  
         [0033]      FIG. 2 . is a schematic representation of an imaging device  28  useful in the camera module  12  of the present imaging system  10 . In these imaging devices  28  as generally exemplified in  FIG. 2 , the detector semiconductor substrate  30  has electrical connections  35  to an readout ASIC substrate  32  (e.g., bump-bonds in the preferred embodiment illustrated). The detector material  34 , a Cadmium-Telluride based composition in the present invention, of the semiconductor substrate  30  absorbs incoming radiation, and in response to the absorption the radiation energy is directly converted to electrical charges within the thickness of the detector material  34 . The electrical charges are collected at the detector pixel&#39;s collection electrode (pixel contact)  38  of each active or functioning pixel  36 , and electrically communicated through the electrical connections  35  to the pixel circuit contacts  33  on the pixel circuit  31  of the readout ASIC substrate  32 . The electric charge signals are stored and/or processed at a detector pixel&#39;s corresponding pixel circuit  31  on the readout ASIC  32 . Thereafter, the ASIC pixel circuits  31  are usually multiplexed and an analog output is sequenced and digitized either on chip or off-chip.  
         [0034]     The camera module  12  and the high speed frame processor module  18  are in communication via a cable link  60 . The camera module  12  provides processed and organized pixel data, representing the individual raw pixel circuit output of each pixel cell  29 , to the frame processor module  18 . The high speed frame processor module  18  includes a frame grabber circuit typical of the field, which captures the pixel circuit data from the camera module  12  further processes the pixel circuit data to provide a raw time-stamped image frame representing the raw pixel circuit output of each pixel cell  29 . The frame processor  18  then communicates the raw time-stamped image frame data via a frame data link  66  to the calibration module  20  if the system is in the calibration mode, or otherwise to the normalization module  24 .  
         [0035]     The calibration module  20  controls the calibration process. The calibration process analyzes the raw time-stamped image frame data and other calibration parameters, such as reference field radiation intensity, and generates the data necessary to load the look-up table of the calibration data structure module  22 . The calibration module  20  writes to the data structure via a database link  68 . Without proper calibration data loaded into the look-up table, any image output from the normalization module  24  to the display module will be inaccurate. Therefore, the calibration process must be run prior to normal imaging operation of the present system.  
         [0036]     When not in calibration mode, the frame processor  18  communicates the time-stamped image frame data to the normalization module  24 . The normalization module  24  operates on each image pixel of the raw time-stamped image frame with the image pixel&#39;s corresponding correction requirement derived from the look-up table via a second database link  70 . The normalization module  24  then provides a normalized image frame to the display module  16  via a display data link  74 . Every image pixel of the normalized image frame represents its corresponding raw image pixel intensity value corrected by its corresponding correction coefficient from the look-up table.  
         [0037]     To obtain a high quality image, several obstacles need to be overcome in relation to Cadmium-Telluride based detector substrates  30 . For example, there is a continuous leakage current (aka: dark current) that must be compensated for. Certain Cd—Te or Cd——Zn—Te detector materials  34  are manufactured having a blocking contact (not shown) to control the level of leakage current. Other manufactures have various amounts of Zn or other dopants in the detector material  34  to suppress leakage current. In any event, the leakage current creates noise and also fills up the charge collection gates  33  on each pixel circuit  31 . Additionally the use of blocking contacts introduces the problem of polarization or charge trapping which becomes evident after few seconds of operation, for example, after 5 sec, 10 sec or 60 sec etc., depending on the device.  
         [0038]     The advantage of using Cadmium-Telluride based compositions (i.e., Cd—Te and Cd——Zn—Te) as the radiation absorption medium  34  in the present detector substrate  30  is their very high radiation absorption efficiency, minimal afterglow and their potential for high image resolution. Therefore, it is valuable to have imaging systems that mitigate or eliminate the above issues. Even in the absence of a blocking contact the issue of the leakage current and crystal defects do not allow long exposures in excess of 100 msec without increasing the size of the charge storage capacitor on each pixel circuit  31  of the ASIC readout substrate  32 . However, this would be to the detriment of sensitivity because the larger the charge storage capacitance is, the lower the sensitivity becomes. For example, the present invention has been successfully practiced using a capacitance of the order  50  fF as charge storage capacitance on each ASIC pixel circuit receiving charge. With this size of capacitance, the practical maximum exposure time given the Cd—Te or Cd——Zn—Te leakage current and other defects would be 100 msec or less.  
         [0039]     A very useful mechanism for preventing excessive polarization (charge trapping) from forming in a direct conversion (charge coupled) radiation detector device is to briefly cycle the high voltage bias off and on, a technique called detector bias voltage switching. To utilize this technique, the detector substrate bias voltage is switched off for a brief period (less than 100 milliseconds) at the end of a data collection cycle. The duration of a data collection cycle is selectable, e.g., from every three to twenty or more seconds. Bias voltage switching prevents polarization or charge trapping from developing in the detector substrate  30 . However, the bias voltage switching technique is new in the field of X-ray imaging systems, and does have certain aspects that can impact image quality if the are not addressed. One such aspect is “dead-time,” and the other is “pixel response drift.” “Dead-time” is the period in a data collection cycle when the detector bias voltage is off and no detector charges can be collected. “Pixel response drift” is a the result of switching the detector bias voltage back on, and is the initial period that the data collection cycle that the that the pixel&#39;s response to a static radiation field has not yet stabilized. Both of these limitations are illustrated in  FIG. 3 .  
         [0040]     For the purpose of the embodiment illustrated in  FIG. 3 , the data collection cycle time Ct was the time between the initiation of detector bias voltage off/on pulses  50 . The dead-time Dt consists of the actual high voltage down-time Vo plus some stabilization time after the high voltage has been switched back on. The effect of dead-time Dt cannot be less than Vo, and hence cannot be completely eliminated in a switched detector bias voltage imaging system. However, it can be minimized in part by reducing the off-time of the bias voltage to as short a period as is appropriate to allow any polarization (trapped charge) to bleed off and/or to keep the dead-time to a negligibly small portion of the data collection cycle.  
         [0041]     The other potentially limiting aspect of a bias voltage switched detector is pixel response drift Rd, which relates to the non-linear aspect of a pixel circuit&#39;s output signal over time  40  in response to a static radiation field exposure level. See  FIG. 3 . This non-linearity is most pronounced immediately following the voltage-on step of the voltage off-on pulse  50 . Uncorrected, this non-linearity causes pumping of the image&#39;s overall brightness level in a real time image display. The pixel cell non-linear response in a switched bias voltage imaging device is an excellent case for applying the post-image frame generation calibration method of the present imaging system to eliminate this intensity distortion of a real time X-ray image display.  
         [0042]     The present calibration method  10  is especially useful for practice in digital imaging systems utilizing detector bias voltage switching. The camera module  12  of a digital imaging system utilizing detector bias voltage switching typically comprises a detector/CMOS assembly  28  having thousands of pixel cells  29 , each comprising a detector pixel  36  and an associated pixel circuit  31 . Each pixel circuit  31  includes associated circuitry and a pixel circuit signal output (not shown) producing a digitized pixel signal for that pixel circuit  31 . A pixel circuit output signal indicates the intensity of the X-ray/Gamma ray radiation energy impinging on the associated detector pixel  36 . See  FIG. 2 .  
         [0043]     The collected digitized pixel signal outputs are communicated via a camera link  60  to a high speed frame processor module  18  of the image processor  14 . The frame processor module  18  includes a frame grabber circuit which receives the individual pixel circuit output signals from each pixel circuit  31 . The frame processor module  18  organizes the individual digitized pixel signals into an image frame, with each image pixel of the image frame representing the pixel signal of a corresponding to the pixel circuit in the imaging device  28  of the camera module  12 . The intensity of an image pixel in the image frame is representative of the strength of the pixel signal received from the corresponding pixel circuit  31 . However, because of the inherent differences in the mechanical and electrical properties of the individual constituents of each pixel cell  29 , the intensity response of the various pixels comprising an image frame are not uniform, even in response to a uniform x-ray field. Therefore, calibration of the imaging system is necessary before the information represented by the image frame is useful to a user.  
         [0000]     The Calibration Procedure  
         [0044]      FIG. 7  is an overview of the steps of the calibration process of the present imaging system. FIGS.  8  to  10  detail the calibration procedure.  FIG. 11  details the normalization procedure, wherein the raw image pixel data from the frame processor module is normalized. The calibration process uses a software driven calibration module  20  to create and maintain a “look-up table” resident in a data structure module  22 . The look-up table is a set of time dependent, image pixel specific correction coefficients  54  for each pixel of an image frame. The pixel specific correction values  54  are referenced to a target uniform intensity value  52  (see  FIG. 5 ), and are used to correct the raw value of the specific image pixel to a normalized value. Therefore, each image pixel represented in an image frame has a data set of time dependent correction coefficients in the look-up table of the data structure module  22  generated for each of a number of reference x-ray field intensities.  
         [0045]     The time dependency of a set of correction coefficients/values derives from the application of a time-stamp to each image frame processed by the high speed frame module. The time-stamp indicates the time elapsed since the start of the data collection cycle Ct that the image frame was generated. In the preferred embodiment illustrated in  FIG. 4 , the time stamped image frames  44  were captured (grabbed) from the camera module  12  at uniform frame intervals  46  in the data collection cycle Ct. Therefore, the time-stamped image frames  44  always had the same time difference relative to each other. The first frame grabbed after detector bias voltage was switched on was assigned time-stamp=0, second had time-stamp=1, and so on up to time-stamp=N. In practice, a separate calibration data set was calculated for each image pixel and included a correction value for that specific image pixel at each time-stamp in the data collection cycle Ct. Alternatively, the calibration data can be thought of or organized as consisting of N different calibration data sets, one for each image frame of the data collection cycle Ct, each frame data set comprising a separate correction value/coefficient for each image pixel in the frame. For best image quality, N should be selected as the highest number of different time stamps possible N max , or in other words, the highest frame rate possible. However, this would be an extremely data intensive condition and due to current limitations in the technology, e.g., limited computer memory processing times, an N&lt;N max  has to be selected.  
         [0046]     Collecting the data. First step in the calibration method is to collect the relevant data, specifically, the response of the camera&#39;s imaging device  28  to different reference radiation field intensities. The response of each pixel cell  29  of the device  28  is collected for all the time-stamps in the data collection cycle Ct. In the preferred embodiment illustrated, this step was repeated at least 20 times, to reduce the effect of incoming quantum noise. Collecting the relevant data this way corrects for any non-uniformities in the detector or ASIC components, but also intrinsically provides “flat-field” correction. In this embodiment, the calibration method tied the imaging device  28  of the camera module  12  to a specific geometric relationship with the radiation source. Which is to say, the calibration had to be redone whenever the radiation source or the geometry between the imaging device  28  and the radiation source changed. Also, calibration was repeated for each radiation spectrum used.  
         [0047]     Calculation of Pixel Specific Correction Coefficients/Values. The response of a single pixel cell  29  as a function of time and with exposure to different reference radiation field intensities has a characteristic shape. The basic idea behind the present calibration method is uniformity. Each and every pixel cell  29  should give the same pixel output signal if exposed to the same intensity of radiation. This means that the calibration function 
 
 y   out   =f   pix ( x   in )  (1) 
 
 is a mapping from pixel output values x in  to global output values y out . The task is to find suitable functions f pix ( ) for each pixel that gives the same output as all the other pixels. 
 
         [0048]     The choice to use polynomials was made because they are extremely fast to calculate, which was absolutely necessary for real-time operation. The polynomials are not the best basis for regression problems like this, because of their unexpected interpolation and extrapolation behavior. The function f pix ( ) can now be explicitly written as:  
               y   out     =       ∑     i   =   0     M     ⁢       a     i     ,   pix         ⁢     x   in   i                 (   2   )             
 
 where a i,pix  are the coefficients for pixel pix and M is the order of the polynomial. The commonly used linear calibration (gain and offset correction) is a special case when M=1. Use of 3 rd  order polynomial was the basis of the current embodiment, but linear correction was not sufficient to remove all the non-uniformities. 
 
         [0049]     Estimating calibration parameters. A common way of estimating model parameters in a regression problem like this is to use a Maximum Likelihood (ML) estimation. This means that we maximize the likelihood of all the data points for a one pixel at a time given the function and noise model. Assuming normally distributed zero-mean noise, the probability of one data sample x i  is:  
               p   ⁡     (         x   i     |   σ     ,   f     )       =       1         2   ⁢           ⁢   π       ⁢     σ   2         ⁢           ⁢     Exp   ⁡     (     -       (     x   -     f   ⁡     (   x   )         )       2   ⁢           ⁢     σ   2           )                 (   3   )             
 
 and the total likelihood for all the samples assuming they are statistically independent is:  
                   LL   =       ∏     i   =   1       N   data       ⁢     p   ⁡     (         x   i     |   σ     ,   f     )                     =         (     1         2   ⁢           ⁢   π       ⁢     σ   2         )       N   data       ⁢           ⁢     Exp   (     -       ∑     i   =   1       N   data       ⁢       (     x   -     f   ⁡     (   x   )         )       2   ⁢           ⁢     σ   2             )                     (   4   )             
 
         [0050]     A problem with Maximum Likelihood estimation is that it is very difficult to apply any prior knowledge accurately. To overcome this, a Maximum A Posteriori (MAP) estimation is used. In a MAP estimation, the posteriori distribution of all the samples is maximized by:  
               p   (       E   ¨     ,     f   |   x       )     =         p   (       x   |     E   ¨       ,   f     )     ⁢           ⁢     p   ⁡     (   f   )           p   ⁡     (   x   )                 (   5   )             
 
 where L is the estimated covariance matrix of samples assuming independence, A=diag[σ 1  . . . σ Ndata ], x=[x 1  . . . x Ndata ] is the vector of data samples and f=[f(x 1 ) f(x Ndata )] is the vector of calibrated values for this pixel. p(x) is the uninteresting scaling factor, evidence. If we assume normal distribution for noise and for function parameter prior  
               p   (       x   |     E   ¨       ,   f     )     =         (     2   ⁢           ⁢   π     )       -       N   data     2         ⁢           ⁢            E   ¨            -     1   2         ⁢           ⁢     exp   (       -     1   2       ⁢           ⁢     x   T     ⁢           ⁢       E   ¨       -   1       ⁢   x     )               (   6   )                 p   ⁡     (   f   )       =         (     2   ⁢           ⁢   π     )       -       M   +   1     2         ⁢           ⁢     σ   prior   2     ⁢           ⁢     exp   (       -     1     2   ⁢           ⁢     σ   prior   2           ⁢           ⁢       ∑     i   =   0     M     ⁢     a   i   2         )               (   7   )             
 
 then the final posteriori will have form of:  
               p   (       E   ¨     ,     f   |   x       )     =                 (     2   ⁢           ⁢   π     )       -       N   data     2         ⁢           ⁢            E   ¨            -     1   2         ⁢           ⁢       exp   (       -     1   2       ⁢           ⁢     x   T     ⁢           ⁢       E   ¨       -   1       ⁢   x     )     ·                     (     2   ⁢           ⁢   π     )       -       M   +   1     2         ⁢           ⁢     σ   prior   2     ⁢           ⁢     exp   (       -     1     2   ⁢           ⁢     σ   prior   2           ⁢           ⁢       ∑     i   =   0     M     ⁢     a   i   2         )               p   ⁡     (   x   )                 (   8   )             
 
         [0051]     If we take the natural logarithm of the formula above and group all the constant coefficients to new ones, we will get a cost function of:  
             Cost   =         ∑     i   =   1       N   data       ⁢       1     σ   i   2       ⁢       (       x   i     -     f   ⁡     (     x   i     )         )     2         +       σ   prior   2     ⁢           ⁢       ∑     i   =   0     M     ⁢     a   i   2                   (   9   )             
 
 σ prior   2  which can be interpreted as a weighted and constrained linear least squares cost function with penalty parameter. The final parameter values can be solved by differentiating the equation above with respect to all the function parameters a i  and then setting the derivative equal to zero. The motivation for using weighted least squares is that when using different X-ray intensities, the quantum noise for the highest intensity is much higher than, for example, the dark current. This allows more weight to be given to smaller values, which are probably more accurate. 
 
         [0052]     Implementation and Performance Considerations. To optimize image quality, 32-bit floating-point arithmetic was used in all the calculations. Current x86 processors offer good SIMD (single instruction, multiple data) command that allowed very efficient parallel processing.  
         [0053]     Selecting Appropriate Time-Stamped Calibration Image Frames for Use in the Correction Protocol. For practical reasons, every time-stamp in the data collection cycle Cl cannot be used because the amount of data generated would be huge, and processing time and memory allocations prohibitive in certain circumstances. This is because current large cameras offers images up to 508×512 pixels. There are 4 parameters per pixel (3 rd  order polynomial) and each parameter is 4 bytes. This means there are 3.97 MB of data collected per frame. In the current embodiment, the camera provided 50 frames per second, which meant a data collection rate of 198 MB/second. In addition to this, the images were read over the PCI bus in 16-bit format (24.8 MB/second) and stored in the memory (another 24.8 MB/second). The total data rate for 50 fps operation was 248 MB/second. In frame averaging mode, the previous image values were also read from the memory, which gave another 24.8 MB/second, and a total of 273 MB/second memory bandwidth. If the images are displayed on a screen, the 16-bit pixel values is read from the memory, a 32-bit color value is read from the lookup-table per pixel and the final 32-bit values is stored in the display memory giving additional 124 MB/second for a grand total of 397 MB/second. And the field is moving to even larger cameras.  
         [0054]     One pixel requires at least two 32-bit floating point numbers/frame. For a data collection cycle time of 30 second, at a frame rate of 300 fps and a 96000 pixel image frame would mean 6.4 GB of data generated over a single data collection cycle.  FIGS. 12A  to  12 C are a further illustration of this.  FIG. 12A  shows the prior art method of error sampling. However, at 300 fps with a 30 sec data collection cycle and a 100,000 pixel camera, and 4 parameters at 4 bytes/parameter, 13 GB of data must be collected and processed. This is impractical.  FIG. 12B  shows a present non-uniform method of error sampling, which under camera operating perimeters similar to  FIG. 12A  only generated about 480 MB of data to be collected and processed. This is a reduction in storage and processing requirements by a factor of 30 over the prior art.  FIG. 12C  illustrates a preferred non-uniform error sampling method using linear interpolation. Under camera operating perimeters similar to  FIG. 12A , this method only generated about 16 MB of data to be collected and processed. This is a reduction in storage and processing requirements by a factor of 30 over the prior art method of  FIG. 12A .  
         [0055]     As shown in  FIGS. 6 and 12 C, a selection can be made to utilize an optimized subset image frames, which the present calibration does. At the beginning of the data collection cycle Ct, the changes in a pixel cell&#39;s circuit output signal over time  40  are more drastic. Because of this greater variability, the calibration data sets should include more relatively reference frames from this portion of the collection cycle Ct than towards the end of the collection cycle Ct where the output signal over time  40  can be relatively flatter. In a preferred embodiment, an automatic method was used to allow the user to change exposure time (i.e. frame rate) and/or the off-time of the detector bias voltage  50 , but the settings can be accomplished manually as well.  
         [0056]     How to Select Which Pixels to Mask. Some of the pixels cells  29  in an imaging device  28  are practically useless because of material and manufacturing defects. Therefore, these pixels cells  29  have to be identified and masked out, i.e., each of their outputs replaced with some reasonable value calculated from the neighboring pixel cells  29 . The present calibration method calculates a local average value of a set of neighboring pixel cell output signals and then compares this value to individual pixel output signal values. This allows the calibration method to adapt to a non-stationary radiation field. A preferred embodiment, calculated an average frame at least 5 complete data collection cycles at a single reference radiation field intensity setting. This provided a very robust and dependable determination in minimal time of the bad pixels cells  29  in an imaging device  28 .  
         [0057]     Calculating Replacement Values. After all the bad pixel cells  29  have been located, their values are replaced with their local arithmetic averages. There for the output signal of a solitary bad pixel cell  29  is replaced with the average of four good adjecent pixel output signals. The pixel output signal from the bad pixel cell  29  is excluded in this calculation. The four good adjacent pixel cells  29  were selected so that all the possible directions were equally weighted. For example, if the pixel cell  29  above a first bad pixel cell  29  is also a bad, then either the pixel cell  29  to up-left or up-right is used instead in calculating the replacement value for the pixel output signal of the first bad pixel cell  29 .  
         [0058]     Geometry Correction and Filling-in Inactive Zones. The relative positions on the ASIC hybrids are ideally close and uniform, which means that there are some inactive areas (dead space) between adjacent hybrids and that the relative distances can vary between different adjacent hybrid. The solution to this problem is two-step. First, measurements were made of the distances between hybrids and possible rotation angles of hybrids based on a calibration image of a reference object. Then, the errors were corrected based on these measurements. The measurements were made by using the camera itself as a measuring device, and taking images with a calibrated reference object that has very accurate dimensions. Then after measuring the distances, the known and measured values were compared and the mismatches detected.  
         [0059]     Correction for Mismatches and Filling. After the exact positioning of the hybrids was known, a correction algorithm was implemented. Based on the distances a grid was constructed which showed exactly where a given pixel should lie in the image. Based on this, a bilinear interpolation method was used to get the sub-pixel translated and rotated new pixel values.  
         [0060]     While the above description contains many specifics, these should not be construed as limitations on the scope of the invention, but rather as exemplifications of one or another preferred embodiment thereof. Many other variations are possible, which would be obvious to one skilled in the art. Accordingly, the scope of the invention should be determined by the scope of the appended claims and their equivalents, and not just by the embodiments.