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:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation-in-part application of U.S. Regular application, Ser. No. 11/017,629 filed 20 Dec. 2004 now abandoned, and claims priority to U.S. Provisional Application, Ser. No. 60/585,742, filed 6 Jul. 2004, and PCT Application No. PCT/IB2005/001896, filed on Jul. 1, 2005, the contents of which are incorporated herein by reference thereto. 
    
    
     FIELD OF THE INVENTION 
     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 frame rate, 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 
     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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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—Tc 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—Tc or Cd—Zn—Tc detector material issues, CMOS-ASIC production issues, and overall device manufacturing issues. 
     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. 
     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—Tc 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. 
     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 10 fps 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 
     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 such as the one encountered with CdTe and CdZnTe pixilated radiation detectors bonded to CMOS readout. 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 Kcv 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. 
     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 (“Application Specific Integrated Circuit”) 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 and secondly enables image reconstruction (real time or static) from a plurality of digitized individual frames. 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. 
     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. 
     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 or 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 and in certain embodiments up to 300 frames per second or more. 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 he 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. 
     In accordance with the invention, each pixel value in the image frame includes an individual calibration correction specific to that pixel value of the specific frame, 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. The final image can be a real time image or a static digitally accumulated image. 
     A characteristic of the present invention is that the final image to be displayed has pixel values with a bit depth that is higher than the bit depth of pixels from individual frames. For example, each frame may have a 12 bit resolution but when accumulating several such frames to compose a real time or static image the final pixel depth in the displayed image can indeed be 14, 16 or even 18 bits in real terms. This is a significant advancement over the prior art because the extra bit resolution does not come at the expense of performance in other respects. For example, in the prior art, in order to get 16 bits or more, one has to integrate on the device (analog integration) for several hundred milliseconds or even seconds. However, in doing so, one integrates dark (or leakage) current and other types of noise as well. To achieve the desired performance, it is of paramount importance that the individual frames are calibrated and that pixel values of individual frames be corrected with high precision. Therefore, it is a further object of the present invention to provide such 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 may be 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 
         FIG. 1   a  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. 
         FIG. 1   b  is a schematic diagram of the X-ray imaging system of the invention. 
         FIG. 2   a  is a schematic representation of an imaging device useful in the camera module of the present invention. 
         FIG. 2   b  is a cross-sectional side view of the camera of the invention. 
         FIG. 2   c  is a schematic view of the camera and/or frame of the invention, made up of an array of image pixels. 
         FIG. 3   a  is a schematic representation of the static frame accumulation method of the invention. 
         FIG. 3   b  is a schematic representation of the shift-and-add method of the invention. 
         FIG. 3   c  is a graph of the measured pixel response compared with the ideal pixel response. 
         FIG. 4   a  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). 
         FIG. 4   b  is a schematic of the detector substrate bias switching circuit used in the invention. 
         FIG. 5  is a graph illustrating the temporal variation in the raw intensity value of the same single image pixel of  FIG. 4   a  overlaid with a series of image frame capture points generated over time after a bias voltage switching event. 
         FIG. 6  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. 
         FIG. 7  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. 
         FIG. 8  is a simple block diagram of the calibration procedure of the invention. 
         FIG. 9  is a block flow chart illustrating a general overview of the present calibration procedure. 
         FIG. 10  is a block flow diagram illustrating a data collection strategy from a single pixel circuit at a specific reference X-ray field intensity. 
         FIG. 11  is a block flow diagram illustrating a strategy for calculating correction coefficients for each image pixel in a pixel frame. 
         FIG. 12  is a block flow diagram illustrating a strategy for detecting and compensating for bad or uncorrectable pixels. 
         FIG. 13  is a block flow diagram illustrating the application of the present calibration process to provide a normalize image frame. 
         FIG. 14   a  is a graph illustrating the typical prior uniform sampling method wherein a piece-wise constant function is used to determine correction coefficient for normalizing pixel intensity values at specific times or intensities to fit a curve. 
         FIG. 14   b  is a graph illustrating a non-uniform sampling method wherein a piece-wise constant function is used to determine correction coefficients for normalizing pixel intensity values at specific times. 
         FIG. 14   c  is a graph illustrating an alternative (non-uniform) sampling method wherein a piece-wise linear function is used to determine correction coefficients for normalizing pixel intensity values at specific times. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     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. As illustrated in  FIGS. 1   a  and  2   b , 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 . 
     Referring now to  FIG. 1   b , the system  10  includes a PC  76 , in which a frame grabber  78  and imaging software  80  operate, connected via a camera link  82  to the X-ray unit  84 , including a camera  37 , power supply  86 , and ac/dc adapter  88 . The X-ray unit  84  generally further includes network connections  90  for connecting to the PC or terminals in a network, for example. 
     Referring now to  FIG. 2   a , the camera  37  has an interface printed circuit board (PCB)  92  connected via a databus  94  to a Detector PCB  96  and having a cooling element  98 . 
     The x-ray imaging device  28  is capable of producing multiple image frames  44  Each frame  44  is made up of an array  45  of un-corrected image pixel values. 
     Referring now to  FIG. 2   b , a schematic representation of an imaging device  28  useful in the camera module  12  of the present imaging system  10  is shown. In these imaging devices  28  as generally exemplified in  FIG. 2 , the detector semiconductor substrate  30  has electrically connections  35  to an readout ASIC substrate  32  (e.g., hump-bonds in the preferred embodiment illustrated). The detector material  34 , a Cadmium-Telluride or Cadmium Zinc 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. In accordance with the invention, each pixel value  36  in an image frame  44  is digitized and additionally includes an individual calibration correction specific to that pixel value of the specific frame, and therefore in fact is a corrected digital pixel value. The specific calibration correction for each image pixel  47  is derived from a plurality Of individual single frame pixel values  36  corrected according to a correction calibration process. The individual corrected digital pixel values  36  of the same specific image pixel  47  from different image frames  44  are processed according to an algorithm of the normalization module  24  over at least some of the collected image frames  44  to provide the pixel value to be displayed in the final image. The final image can be a real time image or a static digitally accumulated image. 
     Referring now to  FIG. 2   c , the imaging device  28  of the invention is capable of producing multiple image frames  44 , each frame including an array  45  of frame pixel values  36 , with a certain bit depth (i.e. the color or gray scale of an individual pixel—a pixel with 8 bits per color gives a 24 bit image, because 8 Bits×3 colors is 24 bits—e.g., 24 bit color resolution is 16.7 million colors). The system  10  includes processing means  24  for calculating image pixel values  47  from pixel values  36  of different frames  44 , wherein the bit depth of the image pixel values  47  is greater than the bit depth of the pixel values  36  from the individual frames  44 . By way of example, single frame digitization may be for example only 12 bits, or 0 to 4096, maximum. Such analog to digital converters (ADC&#39;s) are quite common and inexpensive today. Additionally, they can be quite fast and operate with clock rates of 5 MHz or even 10 MHz-20 MHz. A typical CdTe-CMOS camera as implemented by the assignee of the current invention may comprise 10 k pixels to 1 M pixels. This means that frame rates of 20 fps-300 fps or even up to 2,000 fps can be achieved with a single ADC. After the frames  44  are read out, un-corrected pixel values are digitized and, consequently, in accordance with the present invention, a correction, calculated using a pixel correction algorithm  20 , is applied to the digital pixel values to obtain corrected frame digital pixel values  36  from single frames. Then, as depicted in  FIG. 3   a , digital corrected pixel values  36  from different frames  44  can be accumulated to yield digital corrected pixel values  47  of an image to be displayed with a bit resolution far greater than the 12 bits. For example, with 17 frames of 12 bit resolution, each one can become more than 16 bit after digital accumulation (17×4096=69632&gt;16 bits). This indeed is a breakthrough in digital x-ray imaging because such resolutions were previously achievable at the expense of long integration times and the use of 16 bit ADC&#39;s that are very expensive and very slow, thus inhibiting real time image display. Additionally, as was explained and will be explained further, long integration times of the analog signal cause other problems such as an increase of the dark current and other types of noise. 
     Referring now to  FIG. 3   b , corrected digital pixel values  36  combined from different frames  44  can be corresponding pixel values or can he from different positions in the frame  44  (such as in the case of scanning). In essence, the system  10  includes a method  20 ,  49 , for correcting the image pixel values from different image frames  44 , and a processing method  24  for calculating corrected pixel values  47  of an image, the method utilizing corrected digital pixel values which correspond in a broad sense, from several frames. 
     As mentioned, the current invention  10  comprises preferably a CdTe or CdZnTe based x-ray/gamma-ray imaging device  28  whereby the CdTe/CdZnTe pixilated detector substrate or substrates  30  is/are bump-bonded to at least one readout ASIC  32 , the CdTe/CdZnTe detector substrate provided for directly converting impinging x-rays or gamma rays to an electronic signal and the readout ASIC provided for storing and/or processing the electronic signal from each pixel  36  and consequently reading out the signal. The CdTe/CdZnTe imaging device  28  is read out at a high frame rate of preferably 10 individual frames per second, or even more preferably 25 fps-100 fps and in some cases up to 300 fps or more. The individual frames  44  are being digitized so that each frame is a string of pixel values  36 , each pixel value corresponding to a digitized signal level for a specific pixel  36  in the frame that was produced by the device  28 . The digitized pixel values for each frame  44  are being corrected in accordance with a pixel correction algorithm  49  already described hereafter. The individual corrected digital pixel values  36  of different frames  44  corresponding to the same pixel  36  are digitally added or averaged or processed according to an algorithm over at least some of the collected frames to provide a pixel value  47  to be displayed in the final image. 
     Critical to the invention is the actual implementation of the correction of the digital pixel values from each individual frame  44  which has to take into account all the deficiencies of the CdTe or CdZnTe crystals, CMOS non-linearity and offsets, dark current, polarization and other effects which will be subsequently explained. In the next section, real time, efficient pixel correction is described. Different correction and/or calibration techniques may be used in the present invention, without changing the scope or diverting from the invention. 
     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  36  (or pixel cell  29 ), to the frame processor module  18 . The high speed frame processor module  18  includes a circuit for the frame grabber  78  ( optionally frame grabber  78  may also he part of the camera module  12 ). typical in 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 then communicates the raw time-stamped image frame data via a frame data link  66  to the calibration module  20  if the system  10  is in the calibration mode, or otherwise to the normalization module  24 . 
     The calibration module  20  controls the calibration process  49 . The calibration process  49  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  49  must be run prior to normal imaging operation of the present system  10 . 
     When not in calibration mode, the frame processor  18  communicated the time-stamped data of the image frame  44  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 it corresponding correction coefficient from the look-up table. 
     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—Tc 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 seconds etc., depending on the device. 
     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. 
     Referring now to  FIGS. 4   a  and  4   b , 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 the detector bias voltage switching technique, in which 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 these 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 the result of switching the detector bias voltage back on, and is the initial period that the data collection cycle that the pixel&#39;s response to a static radiation field has not yet stabilized. Both of these limitations are illustrated in  FIG. 4   a . The detector substrate bias switching circuit  121  is shown in  FIG. 4   b.    
     For the purpose of the embodiment illustrated in  FIG. 4   a , 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. 
     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. 4   a ). 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. 
     Referring now to  FIG. 9 , the present calibration method  49  collects calibration data for the complete data collection cycle at a number of different homogeneous reference radiation field intensities, including a dark current intensity. Thus, the method  49  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/ASIC 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   b . Further, for each reference intensity, the cycle is repeated to reduce random noise. 
     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 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. 
     The Calibration Procedure 
     Referring now to  FIG. 8 , a very high level flow chart of the calibration procedure  49  is shown, including the input of a pixel value  36  into a correction function  110 , in which a correction coefficient  120  is applied, to yield a corrected pixel value  47 .  FIG. 9  is a more detailed overview of the steps of the calibration process  49  of the present imaging system  10 . Calibration data is collected for the complete data collection cycle at a number of different homogeneous reference radiation field intensities, including a dark current intensity I D . In a first step  49   a , data is collected for dark current I D . In a second step  49   b , data is collected for X-ray intensity I 1 . In a third step  49   c , data is collected for X-ray intensity I N . In a fourth step  49   d , correction coefficients are calculated and the look-up table  22  written to. 
     Referring now to  FIGS. 10 to 12 , the calibration procedure  49  is described in still further detail. In  FIG. 10 , the data collection cycle submethod  120   a  is described. In a first step  122 , the Data Bins are initialized, such bins having a structure as follows: 
     Bin  1 : Time  0  . . . T 1    
     Bin  2 : Time T 1  . . . T 2    
     Bin N: Time T N-1  . . . T N . 
     In a second step  124 , the radiation field intensity is set. In a third step  126 , high voltage is pulsed. In a fourth step  130 , the timer is reset. In a fifth step  132 , the collect time is set to equal the time of the image frame, T IF , and a loop, which continues as long as the cycle is still active, the data bin B is found and the frame  44  is added to the bin. Then, in a seventh step  134 , if there are more repetitions to be performed, in order to reduce noise, for example, the loop is run again. 
     In  FIG. 11 , the submethod  120   b  for calculation of correction coefficients includes the following steps. In a loop  140  over each bin and, within that loop, a loop  142  over each pixel  36 , in a first step  144 , a polynomial is fit to all intensity values 1.sub.D and 1.sub.O . . . 1.sub.N and, if the pixel fails a threshold test, the pixel  36  is flagged. In a second step  146 , the data structure (look up table), is written to. In a third step  148 , the submethod  120   b  continues to a masking routine  150 . 
     In  FIG. 12 , the masking submethod  150  includes the following steps. In a first step  152 , the submethod  150  checks for flagged pixels  36 . For each flagged pixel  36 , in a second step  154 , the submethod  150  finds good neighboring pixels  36 . In a third step  156 , the good neighboring pixel locations are written to the data structure  20 . When there are no flagged pixels  36  remaining, the submethod  150  ends. 
     In  FIG. 13 , the normalization procedure  160  is described, including the following steps, performed on the raw image pixel data from the frame processor module. In a first step  162 , during operation of the system  10 , the image frame  44  is received and time stamp is set to “T”. In a second step  164 , the bin is found for time “T”. In a third step  166 , looping over each pixel  36 , the pixel is checked to see if it&#39;s flagged or bad. If yes, then, in a fourth step  168 , the pixel value  47  is replaced with a weighted mean value of good neighbors. In a fourth step  170 , any correction polynomials are applied. 
     The raw image pixel data from the frame processor module 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. 
     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  44  was generated. In the preferred embodiment illustrated in  FIG. 5 , the image frames  44  that are time-stamped were captured (grabbed) from the camera module  12  at uniform frame intervals  46  in the data collection cycle Ct. Therefore, the image frames  44  that are time-stamped 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. 
     Referring now to  FIG. 6  is a graph  186  illustrates normalization of the intensity value of an image pixel  47  by the application of a specific time dependent correction coefficient to the raw intensity value of the particular image pixel s output in each image frame  44 . 
     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 for one or more times (generally 20 or more), 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 that calibration had to be redone whenever the radiation source or the geometry between the imaging device  28  and the radiation source was changed. Also, calibration should was repeated for each radiation spectrum used. 
     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.
 
     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 up to 3 rd  order polynomial was the basis of the current embodiment, but linear correction might be sufficient if a large enough number of time-dependent coefficient datasets is used.
 
     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 1  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:
 
     
       
         
           
             
               
                 
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     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   ⁡     (     Λ   ,     f   |   x       )       =         p   ⁡     (       x   |   Λ     ,   f     )       ⁢     p   ⁡     (   f   )           p   ⁡     (   x   )                 (   5   )               
where Λ is the estimated covariance matrix of samples assuming independence, Λ=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   |   Λ     ,   f     )       =         (     2   ⁢   π     )       -       N   data     2         ⁢          Λ          -     1   2         ⁢     exp   ⁡     (       -     1   2       ⁢     x   T     ⁢     Λ     -   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:
 
     
       
         
           
             
               
                 
                   
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     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   )               
which can be interpreted as a weighted and constrained linear least squares cost function with penalty parameter of σ prior    2 . The final parameter values can be solved by differentiating the equation above with respect to all the function parameters a 1  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.
 
     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. 
     Selecting Appropriate Time-Stamped Calibration Image Frames for Use in the Correction Protocol. For practical reasons, every time-stamp in the data collection cycle Ct 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-area cameras offer images up to 508×512 pixels. There are up to 4 parameters per pixel (if 3 rd  order polynomial is used) 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). So 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. 
     If a first order model is used, 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 12C  are a further illustration of this.  FIG. 14   a  shows the prior art method of error sampling, known as Uniform Sampling, at 300 fps, 30 sec cycle, 100,000 pixels, 4 parameters, 4 bytes/parameter, where we have a 3 rd  Order polynomial for signal and a 0 th  order for time. 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. 14   b  shows a present non-uniform method of error sampling, 0 th  order interpolation, 300 data sets, 4 parameters, which under camera operating perimeters similar to  FIG. 14   a  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. Note that there are artifacts in the beginning and at the end of the cycle.  FIG. 14   c . illustrates a preferred non-uniform error sampling method using linear interpolation, for 10 data sets, 4 parameters. Under camera operating parameters similar to  FIG. 14   a , 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. 14   a . Note that there are small artifacts in the beginning and at the end of the cycle. Bilinear correction was linear for signal and linear for time. Linear interpolation in time reduces apparent signal non-linearity and thus linear correction for signal is adequate. 
     As shown in  FIGS. 7 and 14   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. Note that in the graph shown, one bar represents one set of calibration values. 
     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 . 
     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 adjacent 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 . 
     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. 
     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 (or any other interpolation method) method was used to get the sub-pixel translated and rotated new pixel values. 
     Multiple variations and modifications are possible in the embodiments of the invention described here. Although certain illustrative embodiments of the invention have been shown and described here, a wide range of modifications, changes, and substitutions is contemplated in the foregoing disclosure. In some instances, some features of the present invention may be employed without a corresponding use of the other features. Accordingly, it is appropriate that the foregoing description be construed broadly and understood as being given by way of illustration and example only, the spirit and scope of the invention being limited only by the appended claims.