Patent Publication Number: US-10761038-B2

Title: Multi-energy x-ray absorption imaging for detecting foreign objects on a conveyor

Description:
BACKGROUND 
     The invention relates generally to x-ray systems and particularly to apparatus and methods for absorption imaging of product advancing continuously on a conveyor belt. 
     Damage to modular plastic conveyor belts used in the meat, poultry, and other food industries often causes shards of plastic to contaminate the conveyed product. Besides the costs of belt repair or replacement and interruption in production, the food processor must also deal with possible contamination of the conveyed food product by the shards. 
     SUMMARY 
     One version of apparatus for detecting materials on a conveyor belt comprises a conveyor belt conveying product in a conveying direction, an x-ray source, and a spectroscopic x-ray detector. The x-ray source directs a beam of x-rays having a source intensity distributed across a source spectrum through the thickness of the conveyor belt along an x-ray path. The spectroscopic x-ray detector comprises one or more pixels on the opposite side of the conveyor belt from the x-ray source that receive the x-rays that are attenuated as they pass through the conveyor belt, the conveyed product, and any foreign object advancing with the conveyed product on the conveyor belt at discrete pixel positions across the width of the conveyor belt. The one or more pixels define a corresponding field of view at each pixel position and determine a received intensity distributed across a received spectrum of the attenuated x-rays in its corresponding field of view at each pixel position. A processing system relates the received spectrum at each pixel position to the source spectrum to determine a measured attenuation of the x-rays and relates the measured attenuation to an x-ray attenuation model that includes attenuation coefficients of a set of preselected constituent materials including materials constituting the product, materials constituting the conveyor belt, and materials constituting foreign objects suspected as possible contaminants to determine the thickness of those materials in the fields of view at each pixel position. 
     One version of a method for detecting materials on a conveyor belt comprises: 
     (a) conveying product on a conveying surface in a conveying direction; (b) directing source x-rays having a source intensity distributed across a source x-ray spectrum along an x-ray path through the conveying surface and the product along a line across the width of the conveying surface; (c) detecting the x-rays attenuated on passing through the conveying surface at a plurality of pixel positions along the line with a spectroscopic x-ray detector comprising one or more pixels; (d) measuring the intensity of the attenuated x-rays in contiguous energy bins at each of the pixel positions to produce a received x-ray spectrum at each of the pixel positions; (e) relating the received x-ray spectrum to the source x-ray spectrum to determine a measured x-ray attenuation; and (f) relating the measured x-ray attenuation to an x-ray attenuation model that includes attenuation coefficients of a set of preselected constituent materials including materials constituting the product, materials constituting the conveyor belt, and materials constituting foreign objects suspected as possible contaminants to determine the thickness of those materials in the fields of view of each pixel in the line. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic drawing of an x-ray imaging apparatus embodying features of the invention; 
         FIG. 2  is an enlarged view of a portion of the imaging apparatus of  FIG. 1  illustrating the fields of view of a spectroscopic x-ray detector; 
         FIG. 3  is a log-log graph of the x-ray attenuation coefficients of an exemplary material as a function of x-ray energy level; 
         FIG. 4  is a graphical representation of exemplary energy spectra of x-rays emitted by an x-ray source and of x-rays received by the spectroscopic x-ray detector of  FIG. 3  after attenuation; 
         FIG. 5  is a schematic of a processing system for an x-ray imaging system as in  FIG. 1  showing various notification options; 
         FIG. 6  is a semilog graph of the x-ray attenuation coefficients of various materials as a function of x-ray energy level; 
         FIG. 7  is a flowchart of the setting up of the model of the constituent materials to be imaged by the x-ray imaging apparatus of  FIG. 1 ; and 
         FIG. 8  is a flowchart of an exemplary process for imaging with the x-ray imaging apparatus of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     One version of an x-ray imaging apparatus embodying features of the invention is shown in  FIG. 1 . An x-ray source  10  generates a bremsstrahlung beam  12  of x-ray photons having a source intensity distributed over a broad source spectrum. The x-ray source directs the beam of x-rays in a fan beam along an x-ray path  13  through a conveyor belt  14  in a line across the width of the belt. One example of an x-ray source is an x-ray tube with a tungsten target. The conveyor belt advances with product P in a conveying direction  16  out of the page in  FIG. 1 . The x-rays  12  are attenuated by absorption as they pass through the conveyed product P and the conveyor belt  14 . The attenuation depends on the thickness of the intervening material through which the x-rays pass from source to detector and on the attenuation coefficients of the various materials constituting the conveyor belt  14 , the product P, and any other material that intersects the x-ray path  13 . Spherical spreading of the x-rays along the x-ray path  13  also attenuates the x-ray intensity according to the inverse-square law, but that attenuation is set by the fixed distance between the x-ray source  10  and the detector  18  and so is known a priori and can be accounted for. The attenuated x-rays are received by a spectroscopic x-ray detector  18  on the opposite side of the conveyor belt  14  from the x-ray source  10 . 
     The spectroscopic x-ray detector  18  comprises a linear array of individual static x-ray-detecting pixels  20  that extend across the width of the belt  14 . As one example, the pixels  20  can be solid-state cadmium telluride (CdTe) detectors. Each pixel  20  produces a received energy spectrum binned in contiguous fixed-width bins, such as 1 keV-wide bins, at each pixel position across the width of the belt  14 . Thus, the array of pixels  20  represents a line scan that measures the received x-ray intensity distributed across a received x-ray spectrum. The received x-ray spectra are sent to a processing system  22  that includes a programmable computer running software programs such as a two-dimensional (2D) imager, an x-ray source controller, and a user-interface controller. The x-ray source controller pulses the x-ray source  10  in synchrony with the sampling of the spectroscopic x-ray detector  18 . An alternative x-ray detector  18 ′, shown in dashed lines in  FIG. 1 , comprises a single x-ray-detecting pixel  20 ′ that is advanced rapidly across the width of the belt  14  as indicated by arrow  19  by a driver, such as a stator  21  forming a linear synchronous motor with a magnetic forcer in the pixel  20 ′. The pixel  20 ′ advances rapidly across the width of the belt  14  as it samples the received x-ray intensity at discrete pixel positions and measures the energy spectrum at those positions across the belt width, such as the positions defined by the pixels  20  in the fixed-array x-ray detector  18 . As another alternative, an array of multiple pixels spaced apart at regular intervals across the belt width could be advanced by a driver over just the regular interval rather than the entire width of the belt  14  to measure the spectra at the pixel positions. The processing system  22  receives the pixel spectra and controls the operation of the pixel driver  21  in the moving-pixel detectors. 
     As shown in  FIG. 2 , each pixel  20  has a collimator  24  defining a field of view  26  at each pixel position that intersects the conveyor belt  14  at its conveying surface  27  and eliminates scatter. The output signals of the pixels  20  are sent over signal lines  28  to signal-conditioning circuits  30  including buffers and analog-to-digital converters (ADCs). Digital signals representing the received spectra are sent from the ADCs to the processing system  22  over digital lines  32 . The distance  34  between laterally consecutive fields of view  26  is not greater than a predetermined detection threshold, i.e., the dimension of the smallest foreign object to be detected. 
     The received x-ray spectrum at every pixel position is fitted to an x-ray attenuation model based on the Beer-Lambert Law, which models the received x-ray intensity I r  in each energy bin at the pixel position as the product of the source x-ray intensity I s  and an exponentially decaying term: I r (E)=I s (E)·e −Σi[μ(E)·d]     i   , where I r  and I s  are functions of the energy-bin centers E and [μ(E)·d] i  is the attenuation along the x-ray path due to absorption by an ith preselected constituent material included in the attenuation model.  FIG. 4  shows an example of the source x-ray spectrum for a tungsten x-ray tube and the attenuated received x-ray spectrum. 
     As shown in  FIG. 3 , radiation interacts with matter in three ways: (1) the photoelectric effect  36 , (2) Compton scattering  38 , and (3) coherent scattering  40 . The attenuation resulting from each of these effects for an exemplary material is a function of the x-ray energy E. The total attenuation coefficient μ(E) is the sum of the attenuations due to the three effects as a function of the energy E. As shown, μ decreases monotonically with increasing energy E for this exemplary material.  FIG. 6  shows the relative attenuation coefficients of meat (μ M ), bone (μ B ), acetal (μ A ), steel (μ F ), and glass (μ G ). As  FIG. 6  clearly shows, glass has an attenuation coefficient that does not decrease monotonically with increasing energy. The attenuation coefficient μ(E)—more strictly an attenuation function—is stored in tabular form in the processing system&#39;s memory or is computed algorithmically for each preselected constituent material of interest, i.e., those materials expected to be present: conveyor belt material; meat or other food product being conveyed; and the material constituting any other foreign object to be detected, such as a contaminant. 
     The attenuation model is represented by a system of equations given in matrix form as: 
                   [           μ     1   ⁢     E   0             …         μ     NE   0               ⋮       ⋱       ⋮             μ     1   ⁢     E   n             …         μ     NE   n             ]     ⁡     [           d   1             ⋮             d   N           ]       =     [             -     ln   ⁡     (       I   r       I   s       )         ⁢     E   0               ⋮               -     ln   ⁡     (       I   r       I   s       )         ⁢     E   n             ]       ,         
where n is the number of energy bins used in the attenuation model; N is the number of preselected constituent materials in the attenuation model; μ i (E j ) is the attenuation coefficient of preselected constituent material i in energy bin j; and d i  is the thickness of preselected constituent material i. All the μ i (E j ) attenuation coefficients are known and stored in the processing system&#39;s memory or calculated algorithmically by the processing system. Likewise, the source intensity ME)) is known for each energy bin E j . The received intensity I r (E j ) for each energy bin is measured by the spectroscopic x-ray detector each sample time. The ratio of the received intensity to the source intensity is the measured attenuation. The processing system solves the system of equations by regression to determine the thicknesses di of each of the preselected constituent materials. The processing system uses a nonlinear regression, such as a least-squares regression. The Levenberg-Marquardt algorithm is one example of such a regression. The regression finds the best fit of the data to the attenuation model by minimizing the residuals of the di material thickness terms. The resulting di terms define the thickness of each of the preselected constituent materials for each pixel and, together with the calculated thicknesses for other samples, represent an image of the conveyor belt and the product and any other of the preselected constituent materials on the conveyor belt.
 
     For example, to detect shards  42  of a conveyor belt  14  contaminating a piece of meat M as in  FIG. 1 , the preselected constituent materials might be: (a) acetal (the conveyor belt material); (b) muscle (a product constituent); and (c) bone B (a product constituent). The system of equations for each pixel position would include the known attenuation coefficients for each of the three materials (N=3). Because three independent equations suffice to solve for three unknowns (the thickness of each of the three materials), solving the equations for only three selected energy bins is all that is algebraically necessary to find the three thicknesses. But performing the regression on the attenuation equations for more than three bins provides, in most cases, a more robust solution. Once the thicknesses of the three preselected constituent materials are calculated, the calculated thickness of the acetal material is compared to the known thickness of the conveyor belt. If the calculated thickness is greater than the thickness of the belt  14 , then a contaminating shard  42  of the acetal belt material is known to be present in the pixel&#39;s field of view. If additional potentially contaminating materials, such as glass, wood, and steel, are expected to possibly be present, their x-ray attenuation factors μ can be added to the attenuation model to detect their thicknesses. 
     As shown in  FIG. 5 , once the processing system  22  determines that a contaminant is present, its user-interface controller can sound an alarm  44 , display an alarm condition on a monitor  46 , send an alarm message to other locations over a hardwired or wireless network  48 , or issue a divert signal  50  to a diverter  52  to divert a portion of product from the conveyor belt  14  to a reject conveyor  54 , stop the conveyor, or take any other action required to ensure that the contaminated product is removed from the conveyor line. 
     As previously described in reference to  FIG. 2 , the distance  34  between consecutive fields of view  26  sets the detection threshold in the width direction of the conveyor belt  14 . The detection threshold in the conveying direction is set by the product of the belt speed and the detector sampling interval. So the sampling rate must be high enough that the length of conveyor belt that passes the detector line from sample to sample does not exceed the detection threshold, i.e., the dimension in the conveyor direction of the smallest contaminant to be detected. The processing system can determine sizes of contaminants and product by analyzing the results of product thicknesses at contiguous pixel positions and on consecutive samples. With a table of densities of the preselected constituent materials, the processing system can estimate weights. And because the attenuation of the x-rays is set by the net thickness of each of the materials in the x-ray path, the order of the materials along the path does not usually matter unless x-ray scattering by one or more of the materials is too great. 
       FIGS. 7 and 8  are flowcharts illustrating one method of detecting materials by the x-ray imaging system of  FIG. 1 .  FIG. 8  is a flowchart of an exemplary process stored in program memory and executed by the processing system to generate the system of equations that constitute the attenuation model. The equations are set up by first, at step  60 , selecting the constituent materials to be screened. The selection can be a fixed preselection of one or more materials or can be made by an operator over a user-interface device  56  in the user interface  58  ( FIG. 5 ). The attenuation coefficients μ i (E j ) for the selected materials are imported from a library  62  of attenuation coefficients for a variety of materials stored in the processing system&#39;s memory. Or they can be manually entered from the user input device  56 . The processing system then generates the system of equations with the attenuation coefficients μ i (E j ) of the selected materials at step  64  to construct the model. 
     The flowchart of  FIG. 8  shows one version of a method executed by the processing system to detect suspected foreign objects on a conveyor belt. The first process (initialization  66 ) constructs a 2D image of an empty conveyor belt or conveyor-belt segment devoid of product. In the case of a belt with a regularly repeating belt pattern, as in most modular conveyor belts, a 2D image of a single belt row may suffice to define the entire belt. The processing system first pulses the x-ray source at step  68  and then records the received energy spectrum for each pixel position at step  70 . The responses of the x-ray detector to the empty belt are used to compute calibration terms used to match the spectra bin-to-bin across the width of the belt so that all spectra are identical at step  72 . The calibration terms are stored  74  in the processing system&#39;s memory for use in adjusting all the measured spectra to match. The initialization process then signals the belt motor to drive the conveyor belt at a constant speed at step  76 . The x-ray source is pulsed at x-ray source controller step  78 , and the received x-ray spectrum at each pixel position is measured and recorded at step  80  to produce a line scan. The process of pulsing the x-ray source  78  and recording the spectra  80  is repeated at a fixed pulse and sample rate to build up consecutive line scans until a repeating belt pattern is detected as indicated by step  82 . If the belt is, for example, a flat belt of uniform thickness and construction with no repeating pattern, a uniform pattern can be used. The frame of line-scan spectra representing the repeating empty belt pattern is used to construct a 2D image of the pattern at step  84 . The belt pattern is stored at step  86  for use  88  in the detection of foreign objects on the belt. The initialization process  66  can be run periodically to recalibrate the x-ray detector and to account for changes in the belt due to wear or module replacement. 
     With the x-ray detector calibrated and the belt pattern saved, the processing system runs the acquisition process  90  on the conveyor belt running loaded with product. The process first starts the conveyor belt at step  92  and pulses the x-ray source at step  94 . The energy spectrum at each pixel position is recorded at step  96 . The system of equations representing the attenuation model for the selected constituent materials is solved for a line scan after the calibration terms  74  are applied to the energy spectra at step  98  to construct a 2D image of a frame consisting of consecutive line images at step  100 . The 2D image is stored at step  102 . The stored 2D image  104  is available for display. If the belt speed has changed from the time when the empty belt pattern was stored by the initialization process  66 , the frame image of the loaded belt is adjusted to match the frame length of the stored belt pattern or vice versa. And the loaded belt frame is synchronized, or aligned, with the empty belt pattern. Once that is done at step  106 , the belt-pattern frame is subtracted from the loaded belt frame at step  108 . The resulting difference provides the thicknesses of each of the preselected constituent materials at each pixel position in the frame. If any of the thicknesses exceeds an alarm threshold, a foreign-object-detection (FOD) alarm is sounded or other action is taken at step  110 . If the belt is stopped at step  112 , the acquisition process is exited. If the belt continues running, the process resumes in a regular repetitive fashion by pulsing the x-ray source again at step  94 . 
     Although the processing system&#39;s initialization  66  and acquisition  90  processes can be implemented to run as individual sequential processes with program loops and delays to achieve the proper timing, the processes can alternatively be implemented more consistent with realtime multi-tasking programming using interrupt service routines and a task manager routine. As just one example, in the case of the acquisition process, the periodic pulsing of the x-ray source  94  and recording of the received energy spectra  96  could be implemented as an interrupt-service routine (ISR) scheduled to run at a preselected sampling rate. After all the spectra are recorded, the ISR could then bid the 2D imager task scheduled by the task manager to solve the system of equations 98, construct the 2D image  100 , and store it  102  so that it can be displayed by a user-interface controller task. The 2D imager task could also synchronize the image with belt speed  106 , subtract the stored empty belt pattern from the computed image  108 , and generate FOD alarms  110 . But those steps could alternatively be run in a separate task bid to run upon construction of the 2D image. 
     Although the attenuation model was described as using fixed-width energy bins, it is possible to combine energy bins to form effectively wider energy bins than those automatically produced by the pixels. Because the attenuation of x-rays is greater at lower energy levels, the pixels&#39; energy bins at lower energy levels could be combined to form wider energy bins to be used in the attenuation model. At the higher energies, the energy bins used in the system of equations need not be as wide as those at the lower energies. But because the attenuation curve is much steeper at low energies, wider bins increase the error in the estimates of the attenuation values to be assigned to wide low-energy bins. To maintain the stability of the regression solution of the attenuation model, the widths of the bins are optimized so that the error term due to statistics (i.e., the count rate in an energy bin) and the estimation error in attenuation coefficient due to the steepness of the curve are approximately equal in magnitude. 
     Although the x-ray imaging apparatus has been described mainly in reference to detecting contaminants, especially shards of conveyor belt, it can also be used to detect belt features, such as thickened portions of the belt, which the processing system could use as positional references to measure belt elongation or belt speed or to determine the location of contaminants on the belt. And if a longitudinal lane of the belt is maintained clear of product, pixels under that lane could be used to measure the source x-ray intensity and spectrum in real time.