Method to evaluate the presence of a source of x-ray beam inhomogeneity during x-ray exposure

Abstract

A statistical analysis is performed on pixel values of at least one region of interest in an image obtained by substantially uniform irradiation of an x-ray detector and deciding upon the presence of a source of x-ray beam in-homogeneity by comparing the results of the statistical analysis with at least one predetermined acceptance criterion.

Claims

1. A method to generate gain correction data for correcting a direct radiography or computed radiography image, the method comprising the steps of: exposing an x-ray detector to a substantially uniform x-ray beam to generate an x-ray image; converting the x-ray image into a digital image representation; using the digital image representation for calculation of the gain correction data; performing a statistical analysis on pixel values of at least one region of interest in the digital image representation; determining a presence of a source of x-ray beam inhomogeneity by comparing results of the statistical analysis with least one predetermined acceptance criterion; and using the digital image representation for the calculation of the gain correction data when it is determined in the determining step that no source of x-ray beam inhomogeneity is present; wherein the statistical analysis includes an evaluation of low and high histogram shoulders of a histogram of the pixel values in the region of interest determined relative to a center value in the histogram; the center value is a median, an average, or a modus value of the pixel values of the region of interest; and the method further comprises the step of determining a cause of the beam inhomogeneity by evaluating at least one significance level being determined by thresholding low and high shoulder fractions, the low and high shoulder fractions being determined as an amount of ROI-pixels with values below a low shoulder value divided by a total amount of ROI-pixels and an amount of ROI-pixels with values above a high shoulder value divided by the total amount of ROI-pixels.

2. The method according to claim 1, wherein at least two partially overlapping regions-of-interest are subjected to the statistical analysis.

3. The method according to claim 1, wherein a single region of interest is subjected to the statistical analysis.

4. The method according to claim 1, wherein the criterion is an image-wide criterion.

5. The method according to claim 1, wherein the statistical analysis is only performed on non-defective pixels in the at least one region of interest.

6. The method according to claim 1, wherein the low and high shoulder values are determined as the center value, the average, or the modus value minus and plus a predetermined fraction of: the center value; or a median or an average of standard deviations calculated for a subset of spatially distributed regions-of-interest in the digital image representation.

7. The method according to claim 1, further comprising determining a cause of the beam inhomogeneity by performing a logical operation on at least one significance level and an unbalance level.

8. A method of subjecting a non-corrected detector-image, an offset-map corrected image, a defective-pixel-map corrected image, or a combination of both the offset-map corrected image and the defective-pixel-map corrected image to the method of claim 1.

9. A method to generate gain correction data for correcting a direct radiography or computed radiography image, the method comprising the steps of: exposing an x-ray detector to a substantially uniform x-ray beam to generate an x-ray image; converting the x-ray image into a digital image representation; using the digital image representation for calculation of the gain correction data; performing a statistical analysis on pixel values of at least one region of interest in the digital image representation; determining a presence of a source of x-ray beam inhomogeneity by comparing results of the statistical analysis with least one predetermined acceptance criterion; and using the digital image representation for the calculation of the gain correction data when it is determined in the determining step that no source of x-ray beam inhomogeneity is present; wherein the statistical analysis includes an evaluation of low and high histogram shoulders of a histogram of the pixel values in the region of interest determined relative to a center value in the histogram; the center value is a median, an average, or a modus value of the pixel values of the region of interest; and the method further comprises the step of determining a cause of the beam inhomogeneity by evaluating an unbalance level of a shoulder ratio relative to a predetermined ratio-threshold level, the shoulder ratio being determined as a largest shoulder fraction divided by a smallest shoulder fraction, and the low and high shoulder fractions are an amount of ROI-pixels with values below a low shoulder value and an amount of ROI-pixels with values above a high shoulder value.

10. The method according to claim 9, wherein at least two partially overlapping regions-of-interest are subjected to the statistical analysis.

11. The method according to claim 9, wherein a single region of interest is subjected to the statistical analysis.

12. The method according to claim 9, wherein the criterion is an image-wide criterion.

13. The method according to claim 9, wherein the statistical analysis is only performed on non-defective pixels in the at least one region of interest.

14. The method according to claim 9, wherein the low and high shoulder values are determined as the center value, the average, or the modus value minus and plus a predetermined fraction of: the center value; or a median or an average of standard deviations calculated for a subset of spatially distributed regions-of-interest in the digital image representation.

15. The method according to claim 9, further comprising determining a cause of the beam inhomogeneity by performing a logical operation on at least one significance level and an unbalance level.

16. A method of subjecting a non-corrected detector-image, an offset-map corrected image, a defective-pixel-map corrected image, or a combination of both the offset-map corrected image and the defective-pixel-map corrected image to the method of claim 9.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 illustrates the decision making logic based on the set of local shoulder significance and unbalance results analyzed in the image as compared to a set of predetermined threshold levels.

(2) FIG. 2 represents a local region-of-interest as cropped from an object-disturbed image-tile and its statistical translation into spatially distributed shoulder-pixel patterns.

(3) FIG. 3 represents the valid pixel histogram of the object-disturbed local region-of-interest as analyzed and split statistically into distinctly gap-separated lower and higher shoulder bins.

(4) FIG. 4 represents both local shoulder fractions and their shoulder ratio relative to their significance and unbalance threshold levels.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

(5) Upon an initial calibration after installation or while performing a periodic or an additional re-calibration of a flat panel detector direct radiography system, the use of this equipment for diagnostic imaging purposes is temporarily suspended.

(6) Time and personnel are freed up to acquire new calibration-dedicated image sets, composed of non-exposed as well as homogeneously exposed raw flat panel detector images, and to be able to calculate the various updated defective pixel maps and updated gain maps, necessary to convert the many thousands of raw diagnostic images to follow into optimally corrected images.

(7) Below a specific preferred embodiment of the method of detecting a sub-optimal gain-correction-map condition, caused by the acquisition of beam-path disturbed quasi homogeneously exposed images during system-calibration, is described for a flat panel detector radiography system where individual x-ray images, acquired to compose the set of homogeneously exposed images are individually subjected to a fast, automatic, disturbing object inspection method prior to being accepted as a valid input image for the calculation of an updated gain map.

(8) FIG. 1 explains a specific preferred embodiment of the method of this invention by a process flow-chart acting on an individual image, acquired for the purpose of gain map determination, which might contain a disturbed, local region-of-interest.

(9) The avoidable, disturbed x-ray beam path condition is in this exemplary preferred embodiment caused by an elastic band which was left behind by accident on the detector-surface before starting with calibration-dedicated image acquisitions. This is depicted in FIG. 2.

(10) A disturbed image-tile, represented with high contrast-magnification for improved visibility of this low x-ray absorption object, shows how this ‘forgotten to remove’ object partially intersects with an arbitrarily chosen region-of-interest ROIij and how it locally influences the normally expected smooth background noise pattern which is typical for a homogeneously exposed detector image.

(11) The process of automatic detection of disturbing objects in homogeneously exposed flat field images starts by dividing the image, subjected to this inspection, in a plurality of much smaller, local regions-of-interest.

(12) Although exactly adjacent local inspection ROIs minimize the inspection work, a partial overlap of neighbouring ROIs will be more effective for the detection of small, disturbing beam-path objects.

(13) If these occur at the edge or in the corner of a local ROI the small size objects are sub-divided into even smaller image-disturbances scattered across the adjacent ROIs.

(14) The bigger the ROI-overlap, the smaller the effect of spatial scattering across the neighbour ROIs will be.

(15) The beam path inspection concept described in this preferred embodiment uses 64×64 pixels (8 mm square) inspection-ROIs with a ¼th ROI-size overlap in both image directions.

(16) By consequence the entire image-impact caused by disturbing objects with lengths below 4 mm will thus always have its full effect either locally or in one of the eight partially overlapping, neighbour ROIs which are all subjected to this inspection too.

(17) Once the image's inspection area is divided into a grid of partially overlapping inspection ROIs each of these local ROIs is analyzed as shown by the loop-structure in the FIG. 1 inspection method flowchart.

(18) For direct radiography flat-panel detector imagery a predetermined so-called defect map which flags the detector-array's unreliable pixels, rows and columns may be available for the purpose of image-reconstruction using neighbouring, reliable pixel data.

(19) Using that defect map (optionally), the inspection-ROI's valid pixels subset is derived from which either the local median (in a preferred embodiment) or the average or modus signal value is calculated.

(20) That value represents the central signal value “C” for the further operations performed on the histogram of valid pixel values as depicted in FIG. 3.

(21) Since all the image-pixels are valid in digitizer-/media-based computed radiography (CR) a defect map is neither available nor required for the detection of beam-path disturbing objects during the inspection of the homogeneously exposed images made for the calculation of the CR-cassette's gain-image.

(22) The histogram of undisturbed image-noise, as present in the smooth background signal of FIG. 2, is quantum noise dominated and is depicted as the dashed Gaussian-like distribution centred about the central value C in FIG. 3.

(23) The image impact of a local object disturbance in the inspection ROI introduces x-ray absorption which reduces the signal values of the affected pixels in a way that the histogram becomes asymmetrical with respect to the vertical dash-dot line, representing the central signal value without the presence of that disturbing object.

(24) The higher the object's absorption level, the lower the average signal value of the outward bulking lower signal shoulder.

(25) The higher the amount of ROI pixels affected by the object, the bigger that secondary maximum of the local histogram.

(26) Let's consider the lower and the higher signal values: C-delta and C+delta, symmetrically centered about the central signal value.

(27) Assuming +/−1% C signal-value gaps, relative to the central signal value, a lower and a higher signal shoulder can be defined inside the local histogram as the collections of the lower and the higher valid pixel shoulder fractions composed of the spatially distributed pixels having signal-values below C-delta or above C+delta.

(28) These shoulders can be expressed as fractions of the local ROI's valid pixels subset.

(29) From the Shoulder Pixels Spatial Distribution view in FIG. 2, it can be seen that (for this image and with this +/−1% C delta) nearly equal amounts of about spatially evenly distributed pixels in the smooth background noise still belong to the low and the high histogram shoulders.

(30) Seen from the pixel positions where the beam-path disturbing elastic band object is intersecting with the local inspection ROI however it is clear that these disturbed valid pixels all belong to the low histogram shoulder.

(31) The histogram-gap, determined by the signal delta, that separates the low and the high shoulders from the central value can be predetermined as either a fraction of the central value itself or as a predetermined factor times the standard deviation of the smooth background noise, calculated as the median noise deviation estimate from a limited set of spatially distributed image ROIs.

(32) This way the C-centered signal-gap separating both histogram-shoulders can automatically adapt itself to the amount of smooth background-noise present in the image under inspection.

(33) Many experiments conducted on homogeneously exposed images with various types of disturbing objects in the beam path have shown that the combination of a sufficient level of unbalance between the local histogram's low and high shoulder fractions with a sufficient level of significance of at least one of both shoulder fractions is a sensitive, reliable and fast indicator for the presence of a disturbing object condition in the image under inspection.

(34) The ROI's low and high shoulder fractions L and H are calculated as the amount of valid pixels with signals below the C-delta or above the C+delta value divided by the total amount of valid pixels locally present.

(35) The shoulder ratio is calculated by dividing the biggest of both shoulder fractions by the smallest. Undisturbed image noise will typically return a near equity shoulder ratio.

(36) The results of these calculations are shown in FIG. 4.

(37) As soon as an object will generate an unbalance of the shoulder fractions, the shoulder ratio will increase

(38) Once the L and H shoulder fractions and their ratio R are locally calculated, each of them is compared to its predetermined threshold level.

(39) The threshold for the shoulder fraction determines if the measured value is sufficiently significant to be flagged as one of the prerequisites for object detection.

(40) The threshold for the shoulder ratio determines if the measured value is sufficiently unbalanced to be flagged as one of the prerequisites for object detection.

(41) The decision logic for the detection of a ROI-disturbance is such that a local ROI is regarded as object-disturbed if a sufficient level of shoulder unbalance is present and if at the same time at least one shoulder fraction is sufficiently significant.

(42) The result of that local ROI decision can be stored in an image-wide disturbance memory for further decision making regarding the image-disturbance at a higher level.

(43) In case the detection of a single ROI-disturbance would be sufficient to regard the entire image as disturbed the further looping through the other inspection ROIs can be skipped.

(44) Once all the local ROIs have been investigated and their logical disturbance status is known the decision about the object disturbance of the entire image is made by comparing the predetermined criteria with the content of the image-wide disturbance memory.

(45) An image-wide criterion could be that a very limited amount of solitary disturbed ROIs can still be accepted if these isolated disturbances all occur in ROI's adjacent to the image borders.

(46) If these criteria aren't fulfilled the investigation of the image is halted and the inspected image is accepted and added to the set of input-images for the purpose of system (re-)calibration.

(47) If the image-wide disturbance criteria are met the beam path is regarded as object disturbed. In that case additional inspection, cleaning and or a correction (e.g. the removal of the disturbing object) of the x-ray beam path might be necessary before an image-retake can be performed.

(48) While preferred embodiments of the present invention have been described above, it is to be understood that variations and modifications will be apparent to those skilled in the art without departing from the scope and spirit of the present invention. The scope of the present invention, therefore, is to be determined solely by the following claims.