Patent Publication Number: US-6661873-B2

Title: Motion artifacts reduction algorithm for two-exposure dual-energy radiography

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates generally to imaging systems, such as radiographic systems, and more particularly, to processing techniques for dual-energy radiography. Even more particularly, the present invention relates to a system and method for reducing motion artifacts in soft tissue and bone images decomposed from low and high-energy images acquired from an imaging system, such as a dual-energy digital radiography system using flat-panel technology. 
     Medical diagnostic and imaging systems are ubiquitous in modem health care facilities. Currently, a number of modalities exist for medical diagnostic and imaging systems. These include computed tomography (CT) systems, x-ray systems (including both conventional and digital or digitized imaging systems), magnetic resonance (MR) systems, positron emission tomography (PET) systems, ultrasound systems, nuclear medicine systems, and so forth. Such systems provide invaluable tools for identifying, diagnosing and treating physical conditions and greatly reduce the need for surgical diagnostic intervention. In many instances, these modalities complement one another and offer the physician a range of techniques for imaging particular types of tissue, organs, physiological systems, and so forth. 
     Digital imaging systems are becoming increasingly widespread for producing digital data that can be reconstructed into useful radiographic images. In one application of a digital imaging system, radiation from a source is directed toward a subject, typically a patient in a medical diagnostic application, and a portion of the radiation passes through the subject and impacts a detector. The surface of the detector converts the radiation to light photons, which are sensed. The detector is divided into an array of discrete picture elements or pixels, and encodes output signals based upon the quantity or intensity of the radiation impacting each pixel region. Because the radiation intensity is altered as the radiation passes through the subject, the images reconstructed based upon the output signals may provide a projection of tissues and other features similar to those available through conventional photographic film techniques. In use, the signals generated at the pixel locations of the detector are sampled and digitized. The digital values are transmitted to processing circuitry where they are filtered, scaled, and further processed to produce the image data set. The data set may then be used to reconstruct the resulting image, to display the image, such as on a computer monitor, to transfer the image to conventional photographic film, and so forth. 
     In dual-energy imaging systems, such as dual-energy digital radiography systems, the system acquires two images of a desired anatomical region of a patient at different energy levels, such as low and high energy levels. The two images are then used to decompose the anatomy and to create soft tissue and bone images of the desired anatomical region. The two images are generally decomposed according to the dual-energy decomposition equations: 
     
       
         
           IS=IH/IL 
           WS 
         
       
     
     
       
         
           IB=IH/IL 
           WB 
         
       
     
     where IS represents the soft tissue image, IB represents the bone image, IH represents the high-energy image, IL represents the low-energy image, WS is the soft tissue decomposition parameter, WB is the bone decomposition parameter, and 0&lt;WS&lt;WB&lt;1. The soft tissue and bone decomposition parameters must be selected carefully to provide acceptable dual-energy image quality. Unfortunately, the soft tissue and bone decomposition parameters may be functions of several image and techniques variables, thereby complicating the selection of these parameters. Moreover, the decomposed images typically have significant noise, contrast artifacts, and motion artifacts, which degrade the images and reduce the value of the images for medical diagnosis. These artifacts are generally mitigated by post-decomposition processing techniques, yet the decomposed images still exhibit significant artifacts. 
     At relatively attenuated regions of the image, the foregoing dual-energy decomposition equations produce relatively noisy decomposed images. For example, during a low-dose clinical data acquisition, the computationally efficient decomposition equations amplify noise and produce very noisy decomposed images at highly attenuated regions of the image. Existing noise reduction techniques mitigate noise in the images after decomposition by the foregoing decomposition equations. However, the foregoing decomposition equations tend to amplify noise in the images, and the existing noise reduction techniques fail to mitigate the noise adequately. 
     Artifacts also may arise in the decomposed images due to anatomical movement between the two image acquisitions. Although the two images may be acquired over a relatively short time interval, such as 100-200 ms, these motion artifacts may significantly degrade the quality of the decomposed images. For chest radiography, the motion artifacts manifest as residual rib contrast, which causes rib structure to be visible in the soft tissue image. The residual rib structure, which is present in about 30 percent of acquisitions, decreases the conspicuity of lung pathology and essentially defeats the purpose of generating soft tissue lung images by dual-energy imaging. Traditional methods to correct for motion artifacts are relatively ineffective for dual-energy imaging, because the dual-energy images have significantly different local contrasts. 
     Accordingly, a technique is needed for reducing noise, contrast, and motion artifacts in the images decomposed from a dual-energy imaging system, such as a dual-energy digital radiography imaging system. A technique is also needed for selecting parameters for the dual-energy decomposition process. It also would be advantageous to automate various aspects of the image processing and decomposition process, including the selection of decomposition parameters. 
     SUMMARY OF THE INVENTION 
     The present technique provides a variety of processing schemes for decomposing soft tissue and bone images more accurately from low and high-energy images acquired from an imaging system, such as a dual-energy digital radiography system using flat-panel technology. In particular, a pre-decomposition process is provided for spatially matching, or registering, low and high-energy images using warping registration prior to dual energy image decomposition, which creates the soft tissue and bone images. Accordingly, the pre-decomposition process reduces motion artifacts between the low and high-energy images, thereby improving image clarity of the decomposed soft tissue and bone images. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing and other advantages and features of the invention will become apparent upon reading the following detailed description and upon reference to the drawings in which: 
     FIG. 1 is a diagrammatical overview of a digital X-ray imaging system in which the present technique may be utilized; 
     FIG. 2 is a diagrammatical representation of the functional circuitry in a detector of the system of FIG. 1 that is adapted to produce image data for reconstruction; 
     FIG. 3 is a partial sectional view illustrating an exemplary detector structure for producing the image data; 
     FIG. 4 is a circuit schematic illustrating rows and columns of pixels in an exemplary detector; 
     FIG. 5 is a flowchart representing a method of operating an exemplary imaging system for providing image data; 
     FIG. 6 is a flow chart illustrating an exemplary dual-energy image acquisition and processing scheme of the present technique; 
     FIG. 7 is a flow chart illustrating an exemplary pre-decomposition processing scheme for the scheme of FIG. 6; 
     FIG. 8 is a flow chart illustrating an exemplary image registration process for the pre-decomposition processing scheme of FIG. 7; 
     FIG. 9 is a flow chart illustrating an exemplary parameter selection process for dual-energy image decomposition processes, such as illustrated by FIGS. 6-8 and  10 - 12 ; 
     FIG. 10 is a flow chart illustrating an exemplary dual-energy image decomposition process for the scheme of FIG. 6; 
     FIG. 11 is a flow chart illustrating an exemplary soft tissue image decomposition process for the scheme of FIG. 6; 
     FIG. 12 is a flow chart illustrating an exemplary bone image decomposition process for the scheme of FIG. 6; and 
     FIG. 13 is a flow chart illustrating an exemplary post-decomposition processing scheme for enhancing the decomposed soft tissue and bone images and for modifying decomposition parameter data based on a modification of the soft tissue and bone images. 
    
    
     DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS 
     FIG. 1 illustrates diagrammatically an imaging system  10  for acquiring and processing discrete pixel image data. In the illustrated embodiment, system  10  is a digital X-ray system designed both to acquire original image data and to process the image data for display in accordance with the present technique. For example, the system  10  may acquire multiple images of a desired anatomy over a short time interval for comparison and processing, such as high and low-energy image exposures used for a dual-energy decomposition system. Accordingly, the system  10  may embody a dual-energy digital X-ray system, which is operable to decompose high and low-energy image exposures into soft tissue and bone images for further analysis of the desired anatomy. In the embodiment illustrated in FIG. 1, imaging system  10  includes a source of X-ray radiation  12  positioned adjacent to a collimator  14 . Collimator  14  permits a stream of radiation  16  to pass into a region in which a subject, such as a human patient  18  is positioned. A portion of the radiation  20  passes through or around the subject and impacts a digital X-ray detector, represented generally at reference numeral  22 . As described more fully below, detector  22  converts the X-ray photons received on its surface to lower energy photons, and subsequently to electric signals, which are acquired and processed to reconstruct an image of the features within the subject. 
     Source  12  is controlled by a power supply control circuit  24 , which furnishes both power, and control signals for examination sequences. Moreover, detector  22  is coupled to a detector controller  26 , which commands acquisition of the signals generated in the detector  22 . Detector controller  26  may also execute various signal processing and filtration functions, such as for initial adjustment of dynamic ranges, interleaving of digital image data, and so forth. Both power supply/control circuit  24  and detector controller  26  are responsive to signals from a system controller  28 . In general, system controller  28  commands operation of the imaging system to execute examination protocols and to process acquired image data. In the present context, system controller  28  also includes signal processing circuitry, typically based upon a general purpose or application-specific digital computer, associated memory circuitry for storing programs and routines executed by the computer, as well as configuration parameters and image data, interface circuits, and so forth. 
     In the embodiment illustrated in FIG. 1, system controller  28  is linked to at least one output device, such as a display or printer as indicated at reference numeral  30 . The output device may include standard or special purpose computer monitors and associated processing circuitry. One or more operator workstations  32  may be further linked in the system for outputting system parameters, requesting examinations, viewing images, and so forth. In general, displays, printers, workstations, and similar devices supplied within the system may be local to the data acquisition components, or may be remote from these components, such as elsewhere within an institution or hospital, or in an entirely different location, linked to the image acquisition system via one or more configurable networks, such as the Internet, virtual private networks, and so forth. 
     FIG. 2 is a diagrammatical representation of functional components of digital detector  22 . FIG. 2 also represents an imaging detector controller or IDC  34 , which will typically be configured within detector controller  26 . IDC  34  includes a CPU or digital signal processor, as well as memory circuits for commanding acquisition of sensed signals from the detector. IDC  34  is coupled via two-way fiber optic conductors to detector control circuitry  36  within detector  22 . IDC  34  thereby exchanges command signals for image data within the detector during operation. 
     Detector control circuitry  36  receives DC power from a power source, represented generally at reference numeral  38 . Detector control circuitry  36  is configured to originate timing and control commands for row and column drivers used to transmit signals during data acquisition phases of operation of the system. Circuitry  36  therefore transmits power and control signals to reference/regulator circuitry  40 , and receives digital image pixel data from circuitry  40 . 
     In a present embodiment, detector  22  consists of a scintillator that converts X-ray photons received on the detector surface during examinations to lower energy (light) photons. An array of photo detectors then converts the light photons to electrical signals, which are representative of the number of photons or the intensity of radiation impacting individual pixel regions of the detector surface. Readout electronics convert the resulting analog signals to digital values that can be processed, stored, and displayed, such as in a display  30  or a workstation  32  following reconstruction of the image. In a present form, the array of photo detectors is formed on a single base of amorphous silicon. The array elements are organized in rows and columns, with each element consisting of a photodiode and a thin film transistor. The cathode of each diode is connected to the source of the transistor, and the anodes of all diodes are connected to a negative bias voltage. The gates of the transistors in each row are connected together and the row electrodes are connected to the scanning electronics as described below. The drains of the transistors in a column are connected together and an electrode of each column is connected to readout electronics. 
     In the particular embodiment illustrated in FIG. 2, by way of example, a row bus  42  includes a plurality of conductors for enabling readout from various columns of the detector, as well as for disabling rows and applying a charge compensation voltage to selected rows, where desired. A column bus  44  includes additional conductors for commanding readout from the columns while the rows are sequentially enabled. Row bus  42  is coupled to a series of row drivers  46 , each of which commands enabling of a series of rows in the detector. Similarly, readout electronics  48  are coupled to column bus  44  for commanding readout of all columns of the detector. In the present technique, image acquisition rate is increased by employing a partial readout of the detector  22 . 
     In the illustrated embodiment, row drivers  46  and readout electronics  48  are coupled to a detector panel  50  which may be subdivided into a plurality of sections  52 . Each section  52  is coupled to one of the row drivers  46 , and includes a number of rows. Similarly, each column driver  48  is coupled to a series of columns. The photodiode and thin film transistor arrangement mentioned above thereby define a series of pixels or discrete picture elements  54  which are arranged in rows  56  and columns  58 . The rows and columns define an image matrix  60 , having a height  62  and a width  64 . Again, as described below, the present technique allows an enhanced number of pixels to be read out via the row and column drivers and readout electronics. 
     As also illustrated in FIG. 2, each pixel  54  is generally defined at a row and column crossing, at which a column electrode  68  crosses a row electrode  70 . As mentioned above, a thin film transistor  72  is provided at each crossing location for each pixel, as is a photodiode  74 . As each row is enabled by row drivers  46 , signals from each photodiode  74  may be accessed via readout electronics  48 , and converted to digital signals for subsequent processing and image reconstruction. Thus, an entire row of pixels in the array is controlled simultaneously when the scan line attached to the gates of all the transistors of pixels on that row is activated. Consequently, each of the pixels in that particular row is connected to a data line, through a switch, which is used by the readout electronics to restore the charge to the photodiode  74 . 
     It should be noted that as the charge is restored to all the pixels in one row simultaneously by each of the associated dedicated readout channels, the readout electronics is converting the measurements from the previous row from an analog voltage to a digital value. Furthermore, the readout electronics are transferring the digital values from two previous rows to the acquisition subsystem, which will perform some processing prior to displaying a diagnostic image on a monitor or writing it to film. Thus, the read out electronics are performing three functions simultaneously: measuring or restoring the charge for the pixels in a particular row, converting the data for pixels in the previous row, and transferring the converted data for the pixels in a twice previous row. 
     FIG. 3 generally represents an exemplary physical arrangement of the components illustrated diagrammatically in FIG.  2 . As shown in FIG. 3, the detector may include a glass substrate  76  on which the components described below are disposed. Column electrodes  68  and row electrodes  70  are provided on the substrate, and an amorphous silicon flat panel array  78  is defined, including the thin film transistors and photodiodes described above. A scintillator  80  is provided over the amorphous silicon array for receiving radiation during examination sequences as described above. Contact fingers  82  are formed for communicating signals to and from the column and row electrodes, and contact leads  84  are provided for communicating the signals between the contact fingers and external circuitry. 
     It should be noted that the particular configuration of the detector panel  22 , and the subdivision of the panel into rows and columns driven by row and column drivers is subject to various alternate configurations. In particular, more or fewer row and column drivers may be used, and detector panels having various matrix dimensions may thereby be defined. The detector panel  22  may be further subdivided into regions of multiple sections, such as along a vertical or horizontal centerline. 
     It should be further noted that the readout electronics in the detector generally employ a pipeline-type architecture. For example, as the charge is restored to all the pixels in a particular row simultaneously by each of the associated dedicated readout channels, the readout electronics convert the measurements from the previous row from an analog signal to a digital signal. Concurrently, the readout electronics transfer the measured digital values from two previous rows to the data acquisition subsystem. The data acquisition subsystem typically performs some processing prior to displaying a diagnostic image on a display. Thus, the readout electronics in the present technique perform three functions simultaneously. 
     FIG. 4 illustrates an array of pixels  86  located on an exemplary detector having a plurality of column lines and row lines. As illustrated by the array of pixels  86 , each pixel comprises the transistor  72  and the photodiode  74 . It should be noted that the array is made up of a plurality of scan lines  88 ,  90 ,  92  and a plurality of data lines  94 ,  96  and  98 . The scan lines  88 ,  90 ,  92  represent rows of pixels scanned during the imaging process. Similarly, the data lines  94 ,  96  and  98  represent the columns of pixels through which data is transmitted to a data acquisition system. As can be appreciated by those skilled in the art, the scan lines typically recharge the photodiode and measure the amount of charge displaced. The column or data lines typically transmit the data from each row of pixels to the data acquisition system. 
     As illustrated, scan line  88  (denoted N in FIG. 4) is coupled to each one of the pixels in that specific row. Additionally, scan line  88  is coupled to each of one of the data lines. For example, scan line  88  is coupled to data line  94  (denoted K in FIG. 4) and data line  98  (K+1). Similarly, each one of the data lines is coupled to each one of the scan lines. Thus, as illustrated for the array of pixels  86 , scan line  88  (N), scan line  90  (N−1), and scan line  92  (N+1) are coupled to data line  94  (K), data line  96  (K−1), and data line  98  (K+1) and so on. It should be understood that each data line is typically coupled to one specific column of pixels and each scan line is coupled to one specific row of pixels. Additionally, although in the present embodiment of FIG. 4, 25 pixels are illustrated, it should be noted that additional pixels may, of course, be incorporated in the pixel array. 
     Turning to FIG. 5, a flowchart is represented illustrating a method  100  for operating an imaging system of the type described above. Initially, an X-ray exposure is initiated by an operator, as represented by step  102 . Once an X-ray exposure is taken the readout electronics within the detector  22  are activated, as indicated by step  104 . As mentioned above, an exposure is taken of a patient, whereby X-rays are transmitted through the patient and received by the detector. The array of pixels  86  typically measures the attenuation of the X-rays received by the detector  22 , via the readout electronics provided within each individual pixel. The readout electronics typically collect data utilizing circuitry associated with each of the pixels, as indicated by step  106 . Once the data are collected for a particular row of pixels, the data are transmitted to a data acquisition subsystem as indicated by step  108 . Once data from one specific row of pixels is transmitted to the data acquisition subsystem, the next row of pixels is scanned and read. Thus, the readout of the next row of pixels is activated, as indicated by step  110 . It should be understood that this process continues until the detector  22 , and more particularly all the pixels, are read out. Subsequently, the collected data are processed and ultimately used to reconstruct an image of the exposure area. 
     As mentioned above, the digital x-ray system  10  may be used to acquire high and low-energy image exposures, which may be decomposed into soft tissue and bone images for detailed analysis of the desired anatomy. Accordingly, a process  200  for dual-energy image acquisition and processing is illustrated with reference to FIG. 6, which illustrates the general processing chain that is further illustrated with reference to FIGS. 7-12. As illustrated, the process  200  proceeds by initiating the dual-energy imaging system, such as the digital x-ray system  10  illustrated in FIG. 1 (block  202 ). The process  200  then proceeds to acquire low and high-energy images of a desired anatomy, such as chest images (block  204 ). The process  200  may then process the low and high-energy images prior to dual-energy decomposition, as further illustrated by FIGS. 7-8 (block  206 ). For example, the process  200  may perform a variety of motion correction, noise reduction, and display processing to provide higher quality images. The process  200  then proceeds to decompose the low and high-energy images into soft tissue and bone images, as further illustrated by FIG. 10 (block  208 ). The process  200  may then perform post-decomposition processing on the soft tissue and bone images (block  210 ). For example, the process  200  may perform a variety of motion correction, noise reduction, and display processing to provide higher quality images. The process  200  then proceeds to display the soft tissue and bone images for analysis by a physician (block  212 ). 
     FIG. 7 is a flow chart illustrating an exemplary pre-decomposition processing scheme  300  for performing the act of processing low and high-energy images, as illustrated by step  206  of FIG.  6 . As illustrated, the dual-energy images acquisition system  10  provides a low-energy image  302  and a high-energy image  304  to a dual-energy image processing system  306 , which processes the images  302  and  304  and passes the processed images to a dual-energy image decomposition system  308 . Accordingly, the dual-energy image processing system  306  may perform a variety of processing routines on the images  302  and  304  prior to decomposition into soft tissue and bone images. As illustrated, the system  306  performs detector corrections on the low and high-energy images (block  310 ). For example, the system  306  may correct the low and high-energy images  302  and  304  for variations in the x-ray imaging detectors to provide a corrected low-energy image  312  and a corrected high-energy image  314 . The system  306  may then proceed to perform image registration on the corrected low and high-energy images  312  and  314  to reduce motion artifacts between the images, as further illustrated by FIG. 8 (block  316 ). Accordingly, the system  306  may register the corrected low-energy image  312  to the corrected high-energy image  314  by performing image transformations on either of the images  312  and  314 . In this exemplary embodiment, the system  306  transforms the corrected low-energy image  312  to provide a registered low-energy image  318 , which is registered (e.g., spatially matched) to the corrected high-energy image  314 . 
     FIG. 8 is a flow chart illustrating an exemplary image registration process  400  for the pre-decomposition processing scheme  300  of FIG.  7 . An image registration system  402  performs the process  400  by executing a variety of image registration routines on the low and high-energy images  302  and  304 , which are acquired by the dual-energy image acquisition system  10 . Upon completion, the system  402  passes a registered low-energy image  404  and the high-energy image  304  to the dual-energy image decomposition system  308 . As described in detail below, the process  400  registers the low and high-energy images  302  and  304  by obtaining shift vectors of one image with respect to the other. A warping transformation is then performed on the low-energy image  302  to align the anatomy with respect to the high-energy image  304  prior to dual-energy decomposition into soft tissue and bone images. The process  400  is computationally efficient because the motion artifacts are constrained to only a few pixels due to the relatively short time interval between the low and high-energy image exposures. The process  400  is also advantageously insensitive to the contrast differences between the low and high-energy images  302  and  304 . Accordingly, the soft tissue and bone images subsequently produced by dual-energy decomposition exhibit significantly reduced motion artifacts. 
     In operation, the system  402  proceeds by computing or retrieving a variety of image registration parameters, as further illustrated by FIG. 9 (block  406 ). The system  402  then proceeds to clip or redefine the minimum image intensities of the low and high-energy images  302  and  304  to nonzero values, such as a value of 1 (block  408 ). Step  408  prevents errors associated with division by zero. The system  402  then proceeds to select, or prompt the user to input, spatial limitations for image registration (block  410 ). For example, a search space (S) may be selected to control the degree of image warping/transformation by the image registration process  400 . The search space S is an integer defining the maximum number of pixels that any point in the image being registered (i.e., the low-energy image  302 ) is allowed to shift in either the X or Y direction. For example, if S=3, then the search space is a seven pixel by seven pixel matrix centered on the point of interest. The system  402  then proceeds to define, or prompt the user to input, dimensions for a region of interest (ROI) for the image registration process  400  (block  412 ). For example, the region of interest ROI may be less than, equal to, or larger than the search space S. The system  402  then proceeds to divide the image (i.e., the low-energy image  302 ) into an ROI matrix comprising the maximum number of non-overlapping contiguous ROIs centered within the image and leaving a border to allow for image shifting (block  414 ). The pixels outside the ROI matrix are border pixels, which may be equal to or greater than the search range (e.g., S=3). The system  402  then performs various computations on the low and high-energy images  302  and  304 . 
     For each ROI, the system  402  computes an edge strength for each possible shift within the spatial limitations defined by the search space S (block  416 ). Accordingly, for each ROI of the low-energy image  302  (IL), the system  402  shifts the ROI center to each possible location in the search space S. Each shifted ROI defines a low-energy sub-image (IL′ X,Y ), where X and Y are the shift vector components in horizontal and vertical pixels, respectively. The unshifted ROI also defines a corresponding high-energy sub image (IH′ 0,0 ). Accordingly, for each possible shift of the ROI in the search space S, the system  402  derives a pseudo-soft-tissue sub-image (I PST )′ X,Y  by performing the log-subtraction operation: 
     
       
         ( I   PST )′ X,Y =( IH′   0,0 )/( IL′   X,Y ) WS   
       
     
     where WS is a soft tissue decomposition parameter that may be selected as illustrated by FIG.  9 . The system  402  then proceeds to obtain an edge sub-image (I E )′ X,Y  by convolving (I PST )′ X,Y  with a Prewit edge operator (e.g., two 3×3 kernels). This operation may leave a border, such as a 1 pixel border, on the resulting image. The system  402  then obtains a total edge strength E X,Y  by summing all values in the edge sub-image (I E )′ X,Y , excluding the foregoing 1 pixel border. The foregoing computations are repeated for each possible shift of the ROI in the search space S. 
     For each ROI, the system  402  then proceeds to determine the shift vector for registering the low-energy image  302  to the high-energy image  304 . Accordingly, for each ROI, the system  402  defines the ROI-centered shift vector based on the (X,Y) spatial coordinates that minimize the total edge strength E X,Y  computed above (block  418 ). The system  402  then uses the ROI-centered shift vectors to interpolate shift vectors for each pixel in the low-energy image  302  (block  420 ). For example, bilinear interpolation may be used to compute the shift vectors for each pixel in the low-energy image  302 . The individual pixel shift vectors may then be rounded to integer values. In the border area surrounding the ROI matrix, the individual pixel shift vectors may be computed by replicating the shift vectors from the closest point, or from several adjacent points, within the ROI matrix. 
     Accordingly, the edge-based technique of the process  400  obtains shift vectors that minimize motion artifacts. The system  402  may then proceed to transform the low-energy image  302  using the individual pixel shift vectors computed above (block  422 ). Accordingly, the system  402  transforms or warps the low-energy image (IL) to form the registered low-energy image  404  (IL R ), which is registered to the high-energy image  304 . These images  404  and  304  are then passed to the dual-energy image decomposition system  308  for decomposition into soft tissue and bone images. 
     As mentioned above, FIG. 9 is a flow chart illustrating an exemplary parameter selection process  500  for dual-energy image decomposition processes, such as illustrated by FIGS. 6-8 and  10 - 12 . The parameter selection process  500  uses a variety of system parameters of the dual-energy image acquisition system  10  and the patient to select soft tissue and bone decomposition parameters WS and WB, as indicated by reference numerals  502  and  504 , respectively. In this exemplary embodiment, the process  500  selects the parameters  502  and  504  automatically without any direct user intervention. However, the process  500  may operate with some degree of user interaction and input depending on the particular application. An automatic parameter selection system  506  performs the process  500  by accessing low and high-energy images  302  and  304  acquired from system  10 , system settings, patient information, and other information to facilitate an optimal selection of the parameters  502  and  504 , which are required by the dual-energy image decomposition system  308 . 
     The system  506  may be used to select parameters for any dual-energy decomposition process. For dual imaging of chest anatomy, the parameters  502  and  504  are determined primarily by the energy levels (kVp) of the low and high-energy images  302  and  304 , the collimator filtration selection, and the patient size. Accordingly, the process  500  is tailored to these parameters for automatic selection of the parameters  502  and  504  for dual-energy image decomposition of chest anatomy. As illustrated, the process  500  proceeds by accessing a variety of parameters for dual-energy image decomposition, such as by reading default parameters from the system configuration file (block  508 ). For example, the process  500  may access default cancellation parameters (W), filtration offsets (F 1 , F 2 , F 3 ), and size offsets (P), as indicated by reference numerals  510 ,  512 , and  514 , respectively. The process  500  also may prompt the user to input desired parameters to facilitate the selection/computation of decomposition parameters. The foregoing filtration offsets  512  correspond to the collimator filtration selection, while the size offsets  514  corresponds to the patient size selection. The process  500  also may restrict the parameter selection to low and high-energy images  302  and  304  having determined energy ranges, such as 60-80 kVp for the low-energy image  302  and 110-150 kVp for the high-energy image  304 . If the energy levels of the low and high energy images  302  and  304  exceed these predetermined ranges, then the process  500  may generate an error message and terminate the automatic parameter selection process. Accordingly, the default cancellation parameter W may be retrieved from a W-table, such as illustrated below, which provides the cancellation parameter W for energy levels of the low and high-energy images  302  and  304  within the foregoing energy ranges. 
     
       
         
           
               
            
               
                   
               
               
                 W-TABLE 
               
            
           
           
               
               
               
            
               
                   
                   
                 Energy Levels of Low- 
               
               
                   
                 Energy Levels of High- 
                 Energy Image in kVp 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 Energy Image in kVp 
                 60 
                 65 
                 70 
                 75 
                 80 
               
               
                   
                   
               
               
                   
                 150 
                 .37 
                 .41 
                 .46 
                 .50 
                 .55 
               
               
                   
                 140 
                 .39 
                 .44 
                 .49 
                 .54 
                 .59 
               
               
                   
                 130 
                 .41 
                 .46 
                 .51 
                 .56 
                 .62 
               
               
                   
                 120 
                 .44 
                 .49 
                 .54 
                 .59 
                 .65 
               
               
                   
                 110 
                 .47 
                 .52 
                 .58 
                 .64 
                 .71 
               
               
                   
                   
               
            
           
         
       
     
     If the low or high-energy levels of images  302  and  304  are between the energy values within the W-table, then the process  500  interpolates (e.g., bilinear interpolation) the cancellation parameter W from the W-table based on the actual energy levels of images  302  and  304  (block  516 ). The process  500  may then truncate the computed cancellation parameter W to a desired number of decimal places, such as two decimal places. 
     The default cancellation parameter W is then corrected by a variety of correction factors, such as filtration correction parameter K 1  and a patient size correction parameter K 2 . As illustrated, process  500  selects the filtration correction parameter K 1  based on collimator filtration settings and filtration offsets  512  (block  518 ). For example, the process  500  may select the filtration correction parameter K 1  as follows: 
     
       
         
           
               
               
             
               
                   
               
               
                 Collimator Filtration Setting 
                 Set filtration correction parameter K1 to: 
               
               
                   
               
             
            
               
                 0.0 mm Cu 
                 K1 = 0   
               
               
                 0.1 mm Cu 
                 K1 = F1 
               
               
                 0.2 mm Cu 
                 K1 = F2 
               
               
                 0.3 mm Cu 
                 K1 = F3 
               
               
                   
               
            
           
         
       
     
     The process  500  may use any suitable filtration settings, any number of filtration offsets, or fractions of the filtration offsets to facilitate the selection of an optimal filtration correction parameter K 1 . The process  500  also may provide different parameters depending on the specific imaging system  10  or the process  500  may set K 1 =0 for a particular imaging system  10 . 
     The process  500  also defines the patient size correction parameter K 2  based on the size of the patient diagnosed by the imaging system  10  (block  520 ). For example, the process  500  may define the patient size correction parameter K 2  as follows: 
     
       
         
           
               
               
               
             
               
                   
                   
               
               
                   
                 Patient Size 
                 Set patient size correction parameter K2 to: 
               
               
                   
                   
               
             
            
               
                   
                 Small patient 
                  K2 = −P 
               
               
                   
                 Medium patient 
                 K2 = 0 
               
               
                   
                 Large patient 
                 K2 = P 
               
               
                   
                   
               
            
           
         
       
     
     The process  500  may use any suitable size ranges (e.g., weight or dimensions) to define the patient size correction parameter K 2 . Moreover, the process  500  may use multiple size offsets P or fractions of the size offsets P to provide further patient size ranges, which may facilitate the selection of an optimal patient size correction factor. 
     Using the foregoing correction parameters K 1  and K 2 , the process  500  proceeds to calculate corrected soft tissue and bone decomposition parameters WS and WB, respectively. At block  522 , the process  500  calculates a soft tissue decomposition parameter WS as follows: 
     
       
         
           WS=W−K 
           1 
           −K 
           2 
         
       
     
     At block  524 , the process  500  calculates a bone decomposition parameter WB as follows: 
     
       
           WB =( WS +1)/2 
       
     
     As mentioned above, the process  500  may compute these decomposition parameters WS and WB automatically without any direct user intervention. Moreover, the process  500  avoids robustness problems associated with image-based algorithms, opting instead to compute the decomposition parameters WS and WB based on system and patient variables. Although the process  500  specifically correlates WS and WB to filtration and patient size parameters, the present technique may use any suitable system settings and patient data to compute optimal decomposition parameters for the decomposition of a desired anatomy. 
     FIG. 10 is a flow chart illustrating an exemplary dual-energy image decomposition process  600  for performing the act of decomposing low and high-energy images  302  and  304  into soft tissue and bone images  602  and  604 , as illustrated by step  208  of FIG.  6 . As illustrated, the dual-energy images acquisition system  10  provides the low-energy image  302  and the high-energy image  304  to the dual-energy image decomposition system  308 , which executes a system input block  606 , a system initialization block  608 , and image decomposition blocks  610  and  612  to generate the soft tissue and bone images  602  and  604 . As described in detail below, the dual-energy image decomposition system  308  performs a variety of operations to reduce/stabilize noise and to stabilize contrast of the images  602  and  604  that are decomposed from the low and high-energy images  302  and  304 . 
     The soft tissue and bone images  302  and  304  are generally decomposed from the images  302  and  304  according to the dual-energy decomposition equations: 
     
       
         
           IS=IH/IL 
           WS 
         
       
     
     
       
         
           IB=IH/IL 
           WB 
         
       
     
     where IS represents the soft tissue image, IB represents the bone image, IH represents the high-energy image, IL represents the low-energy image, WS is the soft tissue decomposition parameter, WB is the bone decomposition parameter, and 0&lt;WS&lt;WB&lt;1. However, this computationally efficient decomposition algorithm produces relatively noisy decomposed images at highly attenuated regions of the image during a low-dose clinical data acquisition. Accordingly, the system  308  uses a modified dual-energy decomposition scheme (e.g., process  600 ) to mitigate the noise amplification during the decomposition at highly attenuated regions and to provide a robust decomposition prior to further noise mitigation. 
     As illustrated, process  600  acquires or computes a variety of image data and parameters for the decomposition at the system input block  606 . The system input block  606  begins by acquiring low and high-energy images  302  and  304  from the dual-energy image acquisition system  10  (block  614 ). For example, the system  308  may acquire rows and columns of image data for the low and high-energy images  302  and  304  from the dual-energy image acquisition system  10 , which may embody digital flat-panel technology. The system input block  606  also selects/computes the image decomposition parameters WS and WB, such as illustrated by the automatic parameter selection process of FIG. 9 (block  616 ). 
     In this modified decomposition scheme, the system input block  606  also inputs a variety of image stabilizing parameters for use in modifying the dual-energy decomposition equations provided above. For example, the system input block  606  inputs/selects noise stabilizing parameters Ψ S  and Ψ B , which facilitate noise reduction/stabilization for the decomposition of the soft tissue and bone images  602  and  604 , respectively (block  618 ). The foregoing stabilizing parameters may be obtained by experimentation with decomposition for the desired anatomy. For example, the stabilizing parameter Ψ S  for reducing noise in the soft tissue image  602  may range from 1 to 5, but may have a preferred value of 1.4. The stabilizing parameter Ψ B  for reducing noise in the bone image  604  also may range from 1 to 5. However, the value of Ψ B  is a trade-off between noise and blooming artifacts in highly attenuated regions of the image. At Ψ B =1.0, the modified decomposition scheme generates the bone image  604  with relatively no blooming artifacts, but with relatively significant noise. At Ψ B &gt;1.0, the modified decomposition scheme generates the bone image  604  with increasingly more blooming artifacts, but with increasingly less noise. Accordingly, the stabilizing parameter Ψ B  may have a preferred value of 3 to 4 to stabilize the image. 
     The system input block  606  also inputs/selects contrast stabilizing parameters LS, LB, Φ, Φ 1 , and Φ 2 , which facilitate contrast stabilization for the decomposition of the soft tissue and bone images  602  and  604 , respectively (block  620 ). For example, the stabilizing parameter Φ for contrast matching may be computed from the decomposition parameters WS and WB, as follows: 
     
       
         Φ= WB /( WB−WS ) 
       
     
     The remaining stabilizing parameters may be obtained by experimentation with decomposition for the desired anatomy. For example, the stabilizing parameter Φ 1  for removing contrast abnormalities (e.g., irregular intensities) in the soft tissue image  602  may range from 1 to 100, but may have a preferred value of 10. Similarly, the stabilizing parameter Φ 2  for removing contrast abnormalities (e.g., irregular intensities) in the bone image  604  may have a preferred value of 1. 
     The process  600  also performs a variety of initialization operations, such as illustrated by the system initialization block  608 . As illustrated, the system initialization block  608  clips/redefines the minimum image intensities of the low and high-energy images  302  and  304  to be nonzero and positive values (block  622 ). For example, if a pixel intensity value is zero, then block  622  may redefine the pixel intensity value to a positive integer value of 1. The foregoing operation prevents division by 0 in the dual-energy decomposition equations. The system initialization block  608  also computes the mean intensity (M) of the high-energy image (IH)  304  for all nonzero positive values (block  624 ). The mean intensity (M) is used by the modified decomposition scheme to renormalize the decomposed bone image  604  following noise and contrast stabilization. The system initialization block  608  also computes lookup tables (LUTS) for IL WS  and IL WB , which are used in the modified dual-energy decomposition scheme (block  626 ). The lookup tables (LUTs) are computed only for the known intensity range of the dual-energy image acquisition system  10  or the image  302  (e.g., intensities of a 2k×2k pixel image). For example, the system  10  may have an intensity range comprising integer values of 0 to 16383. The lookup tables (LUTs) are subsequently used by the modified dual-energy decomposition scheme to perform the various decomposition and stabilization operations more efficiently. 
     Accordingly, the process  600  uses the foregoing data and parameters input by block  606  and initialized by block  608  to decompose the soft tissue image  602  from the low and high-energy images  302  and  304 , as illustrated by FIG.  11 . The process  600  also uses the foregoing data and parameters to decompose the bone image  604  from the low and high-energy images  302  and  304 , as illustrated by FIG.  12 . 
     FIG. 11 is a flow chart illustrating an exemplary soft tissue image decomposition process  700  for performing the act of decomposing the soft tissue image  602  from the low and high-energy images  302  and  304 , as illustrated by step  610  of FIG.  6 . As illustrated, the soft tissue image decomposition process  700  is executed by a soft tissue image decomposition system  702 , which comprises a noise stabilizing module  704  and a contrast stabilizing module  706  adapted to reduce/stabilize noise and to stabilize contrast during the decomposition of the soft tissue image  602  from the images  302  and  304 . The soft tissue image decomposition process  700  is particularly advantageous for improving image quality at highly attenuated regions of the image caused by a low-dose clinical data acquisition. 
     As illustrated, the noise-stabilizing module  704  utilizes a modified decomposition equation: 
     
       
           IS   1 = IH*IL   WS /(IL WS   *IL   WS +Ψ S ) 
       
     
     where IS 1  is a noise reduced/stabilized soft tissue image, IL is the low-energy image  302 , IH is the high-energy image  304 , WS is the soft tissue decomposition parameter, and Ψ S  is the soft tissue noise stabilizing parameter. The modified decomposition equation is equal to the general dual-energy decomposition equation only if Ψ S =0. However, at nonzero values of Ψ S  (preferably Ψ S &gt;1), the modified decomposition equation provides a robust decomposition that advantageously stabilizes noise at low values of IL WS . 
     At each pixel, the stabilizing module  704  uses the lookup tables (LUTs) to compute IL WS  for the foregoing modified decomposition equation (block  708 ). The stabilizing module  704  then proceeds pixel-by-pixel to compute the noise stabilized soft tissue image IS 1  at the desired value for the noise stabilizing parameter Ψ S  (block  710 ). As mentioned above, values of Ψ S &gt;1 (e.g., Ψ S =1 to 5) mitigate the noise of the conventional dual-energy decomposition equation. However, a relatively high value of Ψ S  defeats the purpose of decomposition, because high values of Ψ S  produce an image that resembles the high-energy image  304 . Accordingly, a value of Ψ S  (e.g., Ψ S =1.4) may be selected to optimize the noise stabilization. 
     The process  700  then proceeds to stabilize the contrast via the contrast-stabilizing module  706 . At block  712 , the contrast-stabilizing module  706  proceeds pixel-by-pixel to contrast match the image with the high-energy image  304  by computing: 
     
       
           IS   2 = IS   1   Φ   
       
     
     At block  714 , the contrast stabilizing module  706  proceeds pixel-by-pixel to stabilize any abnormality, such as a low intensity pixel, in the image by computing: 
     
       
           TS=IH /( IS   2 +Φ 1 ) 
       
     
     As discussed above, Φ 1  may range from 1 to 100, but preferably has a value of 10. The stabilizing parameter Φ 1  corrects image abnormalities (e.g., a low pixel intensity) by adding Φ 1 , thereby returning the particular pixel to a relatively normal intensity range. 
     The contrast stabilizing module  706  then proceeds to block  716 , where the image is smoothed by filtering the image data TS with a low pass filter to provide a filtered image TS LPF . For example, the image data TS may be filtered using a standard boxcar filter, which smoothes the image by the average of a given neighborhood using a separable and efficient computation. Each point in the image requires just four arithmetic operations, irrespective of the kernel size LS, which controls the amount of smoothing. The length of the separable kernel is variable, but a preferred value of LS=151 may be used for a 2048×2048 image. Accordingly, the foregoing blocks  712  through  716  of the contrast-stabilizing module  706  operate to stabilize the contrast of the decomposed and noise-stabilized soft tissue image IS 1 . 
     At block  718 , the process  700  proceeds pixel-by-pixel to generate the soft tissue image (IS) by computing: 
     
       
           IS=IS   2 * TS   LPF   
       
     
     The process  700  also performs a saturation check at each pixel of the soft tissue image (block  720 ). A particular pixel is saturated if it exhibits an intensity equal to the maximum possible intensity (i.e., RANGE) of the imaging system  10 . For example, if the low-energy image (IL)  302  and the high-energy image (IH)  304  both exhibit intensities equal to the maximum possible intensity (RANGE) of the imaging system  10 , then the process  700  may redefine the image IS to equal the maximum intensity at that particular pixel (i.e., IS=RANGE). 
     The soft tissue image generated by the foregoing process  700  exhibits relatively lower noise and relatively more stabilized contrast than a soft tissue image decomposed by the conventional dual-energy decomposition equation. As mentioned above, the stabilizing parameters may be selected to optimize the image quality for a particular imaging system and anatomy (e.g., chest radiography). Accordingly, the process  700  may produce noise and contrast-stabilized soft tissue images for any application by identifying the optimal stabilizing parameters experimentally or empirically. 
     FIG. 12 is a flow chart illustrating an exemplary bone image decomposition process  800  for performing the act of decomposing the bone image  604  from the low and high-energy images  302  and  304 , as illustrated by step  612  of FIG.  6 . As illustrated, the bone image decomposition process  800  is executed by a bone image decomposition system  802 , which comprises a noise stabilizing module  804  and a contrast stabilizing module  806  adapted to reduce/stabilize noise and to stabilize contrast during the decomposition of the bone image  604  from the images  302  and  304 . The bone image decomposition process  800  is particularly advantageous for improving image quality at highly attenuated regions of the image caused by a low-dose clinical data acquisition. 
     As illustrated, the noise-stabilizing module  804  utilizes a modified decomposition equation: 
     
       
           IB   1 = IH*IL   WB /( IL   WB   *IL   WB +Ψ B ) 
       
     
     where IB 1  is a noise reduced/stabilized bone image, IL is the low-energy image  302 , IH is the high-energy image  304 , WB is the bone decomposition parameter, and Ψ B  is the bone noise stabilizing parameter. The modified decomposition equation is equal to the general dual-energy decomposition equation only if Ψ B =0. However, at nonzero values of Ψ B  (preferably Ψ B &gt;1), the modified decomposition equation provides a robust decomposition that advantageously stabilizes noise at low values of IL WB . 
     At each pixel, the stabilizing module  804  uses the lookup tables (LUTs) to compute IL WB  for the foregoing modified decomposition equation (block  808 ). The stabilizing module  804  then proceeds pixel-by-pixel to compute the noise-stabilized bone image IB 1  at the desired value for the noise stabilizing parameter Ψ B  (block  810 ). As mentioned above, values of Ψ B &gt;1 (e.g., Ψ B =1 to 5) mitigate the noise of the conventional dual-energy decomposition equation. However, a relatively high value of Ψ B  defeats the purpose of decomposition, because high values of Ψ B  produce an image that resembles the high-energy image  304 . Accordingly, a value of Ψ B  (e.g., Ψ B =3 or 4) may be selected to optimize the noise stabilization. 
     The process  800  then proceeds to stabilize the contrast via the contrast-stabilizing module  806 . As discussed above, the value of Ψ B  is a trade-off between noise and blooming artifacts in highly attenuated regions of the image. At Ψ B &gt;1.0, the modified decomposition scheme generates the bone image  604  with increasingly more blooming artifacts, but with increasingly less noise. The image IB 1  computed at block  812  captures this low noise phenomenon. At Ψ B =1.0, the modified decomposition scheme generates the bone image  604  with relatively no blooming artifacts, but with relatively significant noise. The process  800  captures this low/no blooming artifacts phenomenon at block  812 , where the contrast stabilizing module  806  proceeds pixel-by-pixel to compute a contrast-stabilized bone image (IB 2 ), as follows: 
     
       
           IB   2 = IH*IL   WB /( IL   WB   *IL   WB +1.0) 
       
     
     At block  814 , the contrast-stabilizing module  806  proceeds pixel-by-pixel to stabilize any abnormality, such as a low intensity pixel, in the image by computing: 
     
       
           IB   3 = IB   2 /( IB   1 +Φ 2 ) 
       
     
     As discussed above, Φ 2  may range from 1 to 100, but preferably has a value of 1.0. The stabilizing parameter Φ 2  corrects image abnormalities (e.g., a low pixel intensity) by adding Φ 2 , thereby returning the particular pixel to a relatively normal intensity range. 
     The contrast-stabilizing module  806  then proceeds to block  816 , where the image is smoothed by filtering the image data IB 3  with a low pass filter to provide a filtered image IB 3   LPF . For example, the image data IB 3  may be filtered using a standard boxcar filter, which smoothes the image by the average of a given neighborhood using a separable and efficient computation. Each point in the image requires just four arithmetic operations, irrespective of the kernel size LB, which controls the amount of smoothing. The length of the separable kernel is variable, but a preferred value of LB=151 may be used for a 2048×2048 image. 
     At block  818 , the contrast-stabilizing module  806  combines the low noise properties of the decomposed image IB 1  with the low/no blooming artifacts properties of the decomposed image IB 2  by computing: 
     
       
           IB   4 = IB   1 * IB   3   LPF   
       
     
     The contrast matching operation of block  818  normalizes the contrast of IB 1  to IB 2 , thereby allowing use of higher values of Ψ B  in the modified decomposition equation to provide improved noise mitigation without producing blooming artifacts. In comparison to the modified decomposition of soft tissue images, the bone image decomposition process  800  generally requires a higher value of the stabilizing parameter Ψ B  due to relatively higher noise in the bone images. Accordingly, the internal matching of the noise stabilized and contrast stabilized bone images provide an exceptional bone image for analysis by the physician. 
     The foregoing blocks of process  800  produce a fractional decomposed bone image, which necessitates a scaling operation to return decomposed bone image back to the original intensity levels. Accordingly, at block  820 , the process  800  proceeds to compute the mean intensity (MF) of the image data IB 4  and a ratio of means M/MF, where M is the mean intensity of the high-energy image  302  for all nonzero positive values. At block  822 , the process  800  proceeds pixel-by-pixel to scale the bone image data IB 4  with the ratio of means, as follows: 
     
       
           IB   5 = IB   4 * M/MF   
       
     
     The process  800  then computes the maximum (MIN) and minimum (MAX) intensities of the image data IB 5  by averaging 2×2 neighborhoods of the image data IB 5  (block  824 ). The process  800  also computes a scaling factor (S) at block  824 , by computing: 
     
       
           S =RANGE/(MAX−MIN) 
       
     
     where RANGE is the intensity range of the image acquisition system  10 . 
     At block  826 , the process  800  proceeds pixel-by-pixel to generate the bone image (IB) by scaling the image data IB 5 , as follows: 
     
       
           IB= ( IB   5 −MIN)* S   
       
     
     The process  800  also performs a saturation check at each pixel of the bone image (block  828 ). A particular pixel is saturated if it exhibits an intensity equal to the maximum possible intensity (i.e., RANGE) of the imaging system  10 . For example, if the low-energy image (IL)  302  and the high-energy image (IH)  304  both exhibit intensities equal to the maximum possible intensity (RANGE) of the imaging system  10 , then the process  800  may redefine the image IB to equal the maximum intensity at that particular pixel (i.e., IB=RANGE). 
     The bone image generated by the foregoing process  800  exhibits relatively lower noise and relatively more stabilized contrast than a bone image decomposed by the conventional dual-energy decomposition equation. As mentioned above, the stabilizing parameters may be selected to optimize the image quality for a particular imaging system and anatomy (e.g., chest radiography). Accordingly, the process  800  may produce noise and contrast-stabilized bone images for any application by identifying the optimal stabilizing parameters experimentally or empirically. 
     The present technique also may comprise a system and process for interactively selecting or modifying one or more parameters associated with decomposing the soft tissue image  602  and the bone image  604  from the low-energy image  302  and high-energy image  304 . This manual override or interactive modification technique is particularly advantageous for imaging systems that may drift out of calibration over time. For example, a user interface may allow the user to input or modify any of the foregoing decomposition parameters, including the soft tissue decomposition parameter WS, the bone decomposition parameter WB, the noise stabilizing parameters Ψ S  and Ψ B , the contrast stabilizing parameters LS, LB, Φ, Φ 1 , and Φ 2 , or any other such parameters. FIG. 13 illustrates an exemplary post-decomposition processing scheme  900  for enhancing the decomposed soft tissue and bone images  602  and  604  and for modifying decomposition parameter data based on a modification of the soft tissue and bone images. 
     As illustrated, the dual energy image acquisition system  10  produces the low-energy image  302  and the high-energy image  304 . The automatic parameter selection system  506  is then used to select optimal decomposition parameters  502  and  504 , which are passed to the dual energy image decomposition system  308 . Unfortunately, the decomposition parameters  502  and  504  are based on defaults, which may not provide optimally decomposed images  602  and  604  due to system calibration drifting or other factors. The decomposition system  308  then uses the soft tissue and bone decomposition parameters  502  and  504 , and any other default or user input parameters, to decompose the soft tissue and bone images  602  and  604 . The process  900  then proceeds to an evaluation of the soft tissue and bone images, which may be displayed via any suitable graphical display or monitor (block  902 ). If the images are acceptable to the user at block  904 , then the process  900  proceeds to end at block  912 . Otherwise, if the soft tissue and bone images are not acceptable at block  904 , then the process  900  proceeds to modify the soft tissue and bone images by changing one or more decomposition parameters interactively (block  906 ). 
     For example, the user may modify the decomposition parameters  502  and  504  by inputting new values, by moving an interactive slider, or by any other user input mechanism. If the user modifies one or more decomposition parameters using an interactive mechanism, such as an interactive slider, then the process  900  may automatically decompose new soft tissue and bone images  602  and  604  based on the modified parameters. Accordingly, the process  900  provides the user with an interactive image enhancement mechanism, which is associated directly with the parameters used for decomposing the soft tissue and bone images  602  and  604 . 
     After modifying the soft tissue and bone images via interactive modification of the decomposition parameters, the process  900  may provide the user with an option to accept the modified soft tissue and bone images (block  908 ). Alternatively, the process  900  may provide a parameter update option that is operable at any time by the user. If the user accepts the modified soft tissue and bone images, then the process  900  proceeds to modify the default data/parameters associated with the decomposition parameters for subsequent soft tissue and bone decomposition (block  910 ). For example, the process  900  may recalculate or redefine default parameters, such as default parameters  508  and the noise and contrast stabilizing parameters, based on the modified parameters in block  906 . In one exemplary embodiment, the user may interactively modify the soft tissue and bone decomposition parameters WS and WB to achieve the desired image quality. The process  900  may then perform a reverse operation of the parameter selection system  506 , as illustrated and described with reference to FIG.  9 . For example, the process  900  may calculate a new/modified default cancellation parameter table (i.e., W-table)  510 , one or more new/modified filtration offsets  512 , and one or more new/modified size offsets  514 . Similarly, the process  900  can modify any other default decomposition parameters to facilitate accurate soft tissue and bone decomposition for future imaging. 
     While the invention may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and have been described in detail herein. However, it should be understood that the invention is not intended to be limited to the particular forms disclosed. Rather, the invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the following appended claims.