WO2000025268A2 - Tomographie assistee par ordinateur pour inspection non destructive - Google Patents

Tomographie assistee par ordinateur pour inspection non destructive Download PDF

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Publication number
WO2000025268A2
WO2000025268A2 PCT/IL1999/000320 IL9900320W WO0025268A2 WO 2000025268 A2 WO2000025268 A2 WO 2000025268A2 IL 9900320 W IL9900320 W IL 9900320W WO 0025268 A2 WO0025268 A2 WO 0025268A2
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WO
WIPO (PCT)
Prior art keywords
image
reconstruction
reconstmction
responsive
data
Prior art date
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PCT/IL1999/000320
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English (en)
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WO2000025268A3 (fr
Inventor
Avraham Robinson
Alex Roytvarf
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Romidot, Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Romidot, Ltd. filed Critical Romidot, Ltd.
Priority to KR1020017005251A priority Critical patent/KR20010081097A/ko
Priority to JP2000578781A priority patent/JP2002528828A/ja
Priority to AU42875/99A priority patent/AU4287599A/en
Priority to EP99971130A priority patent/EP1145194A2/fr
Publication of WO2000025268A2 publication Critical patent/WO2000025268A2/fr
Publication of WO2000025268A3 publication Critical patent/WO2000025268A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2211/00—Image generation
    • G06T2211/40—Computed tomography
    • G06T2211/412—Dynamic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2211/00—Image generation
    • G06T2211/40—Computed tomography
    • G06T2211/424—Iterative

Definitions

  • the present invention relates to CT imaging techniques, especially for high resolution non-destructive testing.
  • CT computerized (axial) tomography
  • a plurality of projections of the imaged slice are acquired and the slice is reconstructed from data acquired for the projections.
  • the projection data may be obtained by trans-illumination of the slice with X-rays.
  • the projection data is obtained by emission of radiation from radio-nucleotides introduced into the body.
  • CT imaging has also been used to some extent for industrial uses, such as non- destructive testing.
  • these systems are usually characterized by a relatively low resolution.
  • Medical systems are also usually characterized by relatively low resolution, however, they are generally optimized to minimize radiation exposure and to provide suitable contrasts for imaging low-contrast tissue.
  • High resolution CT imaging is achieved for small objects using micro-focus X-Ray systems.
  • the number of pixels in the image is approximately the same as for other CT imaging techniques.
  • the complexity of reconstruction increases as the square of the number of pixels, making the reconstruction of large high-resolution images impractical.
  • Two-dimensional X-Ray photographic imaging for non-destructive testing is also known.
  • Manufactured objects which include cavities are currently inspected by cutting open the object and performing measurements on the cut open object. This inspection is important since the amount of raw material required to manufacture the object is often dictated by manufacturing tolerances of the thickness of a portion surrounding the cavity. Clearly, such inspection cannot be performed on every manufactured object. If CT imaging were to be applied to inspect objects with cavities, two main issues are to be considered. First, a greater than conventional dynamic range is required; and, second, the reconstruction time for a large, high resolution image would be prohibitive.
  • the method allows a reduced memory requirement for such reconstruction.
  • the reconstruction method is used for industrial inspection of manufactured objects.
  • An aspect of some preferred embodiments of the invention is providing a method of
  • the a-priori knowledge is provided from design specifications of the object. Alternatively or additionally, the knowledge is provided from a photograph of the object and/or a slice thereof. Alternatively or additionally, the knowledge is provided from expected manufacturing tolerances and/or expected problem areas in an object. In a preferred embodiment of the invention, the knowledge is used to modify any part of the reconstruction of an image of the object, including, and not limited to, reconstruction technique, reconstruction constraints, reconstruction parameters, reconstruction resolution, data acquisition parameters and/or post processing of an image. Alternatively or additionally, to utilizing a-priori knowledge, an estimated (rough) image may be generated by a fast reconstruction of at least some projection data, for example, by back-projection.
  • An aspect of some preferred embodiments of the invention is providing a reliable method of reconstructing an image of an object even in the presence of inconsistencies in acquired data.
  • Such inconsistency may be caused by noise.
  • the inconsistency is caused by near total absorption of radiation by the object in portions of at least one projection.
  • the inconsistency is caused by partial or negligible absorption of radiation at corners of an object and/or other thin parts.
  • such inconsistencies are resolved by enhanced reconstruction of the problematic area and/or by rejecting bad data.
  • An aspect of some preferred embodiments of the invention relates to providing individualized data acquisition and/or reconstruction for different portions of the image slice.
  • individual treatment of each portion of the image includes locally setting one or more of pixel size, reconstruction type, reconstruction parameters, initial value, expected deviation and/or certainty level.
  • the individual attention is provided by imaging only a difference between an estimation of an image slice and an actual imaged slice of an object. Where little or no difference is expected between the imaged slice and the estimated image, the reconstruction is preferably performed using fewer resources (e.g., memory and CPU cycles).
  • reconstruction parameters refer to parameters of the reconstruction method itself, which when varied, modify the mathematical and/or procedural behavior of reconstruction. For example, in ART, the number of iterations, relaxation coefficients and stopping conditions are examples of reconstruction parameters.
  • portions of the estimated image slice are set to certain a-priori image values, and only other portions of the slice are reconstructed.
  • different portions of the image slice may be associated with levels of certainty with respect to a-priori image density values of those areas.
  • different data acquisition methods may be applied for projections or parts of projections containing data from these different image portions.
  • different reconstruction methods may be used for different portions of the image slice.
  • An aspect of some preferred embodiments of the invention relates to reconstructing significantly fewer than all the pixels in an image slice.
  • pixels whose image value can be estimated with a high certainty level are not reconstructed.
  • a lower quality reconstruction is used for pixels which have a lesser effect on the final usage of the reconstructed image.
  • An example of such a usage is measurement of a corner, where the sharpness of the corner has no effect on a desired measurement of a wall thickness.
  • a lower quality reconstruction is used responsive to a lower expect error level in the object manufacture and/or in the image reconstruction.
  • An aspect of some preferred embodiments of the invention relates to a method of selecting those pixels on which reconstruction is to be performed.
  • an estimated image (or other representation) of the object is used to determine portions at the object at which a deviation from the estimation may be expected or for which it is important.
  • the amount of expected deviation may be different for each portion of the object, depending for example on feature thickness, location in the object, material and/or manufacturing method. Alternatively or additionally, the expected deviation may be determined responsive to a design-allowed deviation at the location.
  • the boundaries of the object are determined. Pixels which are far enough from a boundary, either by virtue of being inside the object (full pixels) or outside the object (empty pixels) are preferably attributed a fixed value.
  • the pixels which are not attributed a fixed value generally define a band around some or all of the object boundaries.
  • the pixels to be reconstructed may define geometrical shapes other than bands, for example, islands. Preferably, only pixels inside the band areas are reconstructed.
  • An aspect of some preferred embodiment of the invention is a reduction in reconstruction artifacts.
  • artifacts are restricted only to parts of the image which are actually reconstructed and do not affect parts of the image which are assigned a fixed a-priori value.
  • the reconstructed image is relatively immune to noise, since the noise is also limited to the reconstructed areas only.
  • the fact that a significant part of the image is known before reconstruction may be used to determine the presence of artifacts and/or noise in the image, for example by comparing a reconstructed image with an estimated image.
  • spatial noise, distortions and/or artifact problems are detected on portions of the image whose structure is known.
  • Compton scatter statistics may be obtained from analyzing portions of an image in which no deviation is expected. Preferably, different statistics are gathered for different parts of the image, so that each reconstructed pixel may be corrected using Compton statistics of a nearby unreconstructed pixel.
  • An aspect of some embodiments of the invention relates to a significant reduction in a computational complexity of reconstruction. As can be appreciated, the number of pixels in a band image is significantly smaller than the number of total pixels in the same image. Possibly, the number of pixels to be reconstructed is reduced from n ⁇ to kn, with a bounded k, which k is preferably determined by a complexity of the object.
  • the reconstructed image is a binary image.
  • the reconstructed image is a discrete image, having only discrete image values.
  • the number of discrete density values is the same as the number of materials in the imaged object, plus one for air.
  • a greater number of discrete values may be used, for example to correspond to boundary areas, but possibly even a continuous range.
  • An aspect of some preferred embodiments of the invention relates to a multi-step image reconstruction method.
  • first an estimated image is provided and then the estimated image ("first image") is used to reconstruct a second image which is more exact, at least in certain portions thereof.
  • the estimated image is provided based on design specifications of the imaged object.
  • the estimated image is provided based on a fast reconstruction of the image, possibly at the expense of quality, preferably using less and/or lower quality data and/or a lower quality reconstruction method, for example, using filtered backprojection, a small number of projections, a lower spatial resolution of the projection data, a larger pixel size (for reconstruction) and/or a shorter data acquisition times.
  • data is acquired at a large number of projection angles, for generating the estimated image.
  • angles used for the estimated image reconstruction are determined by an analysis of a-priori knowledge, such as a CAD design of the object.
  • a concrete first image is not reconstructed or otherwise provided. Rather, the second image is reconstructed utilizing a-priori information, optionally utilizing a sinogram of the first image and/or a partially reconstructed first image.
  • the more exact reconstruction is preferably based on the estimated image (or the a- priori information). Preferably, pixels having values that can be guessed to a high level of certainty in the estimated image, are not reconstructed in the second image.
  • the projection data used for the reconstruction is cleaned up and a third, even, more exact image is reconstructed.
  • further additional clean-up and/or reconstruction steps may be performed, possibly using different clean-up techniques and/or parameters and/or different reconstruction techniques and/or parameters for at least one of the repetitions.
  • edges and/or other features of the image are extracted in order to perform measurements. In a preferred embodiment of the invention, the edges are determined to a sub- pixel resolution using moments. Alternatively or additionally to edge determination, the reconstructed image may be used for other measurements and/or uses.
  • the estimated reconstruction uses a filtered backprojection method.
  • the exact reconstruction and/or the more exact reconstruction use an ART (Algebraic reconstruction technique) reconstruction method and/or its variants.
  • the ART technique is iteratively applied.
  • a different iterative technique for example Baysian or maximum likelihood, may be applied.
  • a non-iterative technique such as backprojection may be applied.
  • the second, third and/or subsequent reconstructions do not all use a same reconstruction technique.
  • the results of a previous reconstruction are utilized as a starting point for the next reconstruction.
  • the radiation levels used for each projection to acquire data for the second (and possibly subsequent images, if additional data is acquired for them) are determined based on previous reconstructions.
  • the estimated image may be used to set up desired radiation levels, so that a desired signal to noise level, dynamic range and/or sensitivity of the CT imager are achieved.
  • the radiation level is selected to optimize portions of projections which include rays which pass through pixels which are to be reconstructed, possibly at the expense of portions of projections which do not include pixels which are being reconstructed.
  • a particular portion of the object may be imaged at several radiation levels, possibly from different angles, in order to better image fine details.
  • a sub-section of the imaged object may be singled out for special reconstruction, for example at different radiation levels and/or at a different resolution of data acquisition and/or reconstruction.
  • such singling out is a result of analyzing a reconstructed image.
  • An aspect of some preferred embodiments of the invention relates to setting initial guessed density values for pixels to be reconstructed.
  • the pixels to be reconstructed are characterized by being in a band around object boundaries.
  • each pixel is associated with a density value responsive to a distance from the nearest boundary(s) and/or other features of an object.
  • the density value is a function of the width of the band and/or other parameters of the object or portions thereof, for example its moment.
  • These initial density values are preferably used as initial values in an ART type reconstruction method.
  • the initial values are chosen in accordance with a moment of the projection data.
  • an aspect of some preferred embodiments of the invention relates to defining an outer reconstruction boundary "hull".
  • the hull is used to clamp density values estimated in a reconstruction step.
  • the hull is defined to cover a subset of the image that is bounded by the pixels from which non-zero projection values were obtained.
  • the hull may be approximated by a polygon, defined by the outermost pairs of radiation rays that yield a non-zero projection value. All the pixels outside the hull only yield zero projection values when a radiation ray passes through them. An initial and/or final density value for these pixels may be set to zero.
  • an estimated density value may be taken to be a minimum of the value of a previous iteration and a hull value.
  • the hull value is initially taken to be "one", if the pixel is inside the hull or "zero", if the pixel is outside the hull.
  • the hull may be defined to include more than two discrete values for calculating the estimated density values, for example based on estimated errors and/or based on an identification of a plurality of materials.
  • the hull may be used to perform other mathematical operations on pixels values, for example multiplication.
  • the pixels outside the hull are clamped to a non-zero value, for example, if the imaged object is immersed in an x-ray attenuating fluid.
  • the density of the fluid is greater than the (x-ray) density of the object.
  • the density of the fluid is smaller than the density of the object.
  • the reconstruction technique is an ART-like reconstruction method, for example, ART, MART, SART, ART-3 and/or other versions of algebraic reconstruction techniques.
  • the reconstruction method is applied iteratively and different relaxation coefficients are determined for each iteration, preferably based on results of a previous iteration.
  • relaxation coefficients in an ART-like reconstruction method are established as a function of deviation of the inconsistent values. Preferably, the deviation is calculated relative to an allowed deviation gate, for example 3 ⁇ .
  • the constraints are established as a square of the deviation. Alternatively or additionally, the constraints are limited to a maximum value. Alternatively or additionally, the constraints are scaled to the maximum value.
  • An aspect of some preferred embodiments of the invention relates to discarding a portion of acquired raw data, to increase reconstruction quality.
  • projections and/or portions of projections which represent rays which pass through a large amount of material are ignored.
  • constraints are suspected as being unsuitable if they cause a deviation in a reconstructed image relative to projection data, of a greater magnitude than a predetermined value.
  • constraints may be "suspect" based on their magnitude relative to a mean and/or other statistical considerations.
  • the deviation of the constraint over several iterations may be taken into account.
  • suspected constraints are rejected if they form only a small percentage of the constraints for a particular direction. Alternatively or additionally, the rejection may be based on the relative deviations from constraints generated from projection data from a same or near angles.
  • An aspect of some preferred embodiments of the invention relates to generating constraints for an ART-like reconstruction method based on moments of the projection data.
  • lower order moments such as zero and first order are used.
  • higher order moments may be used.
  • An aspect of some preferred embodiments of the invention relates to utilizing a non- uniform pixel size in reconstruction.
  • a higher pixel resolution is used for pixels of greater importance (e.g., for reconstruction or measurement) and or pixels where an expected error and/or variations in the local pixel values is greater.
  • pixel size may be determined responsive to an effect of lower pixel resolution on other reconstructed pixels, for example those sharing a same radiation trace.
  • the projection data may be acquired and/or stored at varying resolution levels. In one example, portions of a projection which only pass through pixels that are of lesser importance in the usage of the image, may be acquired and/or stored at a lower spatial and/or density value resolution.
  • the reconstruction data and/or the projection data are stored using a hierarchical data representation.
  • the hierarchical representation maintains a spatial organization of the data.
  • the data structure is a Quad tree.
  • significant data storage is required only for "important pixels".
  • higher resolution is provided at band pixels.
  • a higher resolution is provided for pixels nearer an estimated boundary of the imaged object, possibly related to an expected error level and/or density level.
  • the local grid resolution may be changed during reconstruction. Alternatively or additionally, a fixed (but possibly spatially varying) resolution is used.
  • the higher pixel resolution is approximately the same as a detector resolution. Alternatively, a higher or lower resolution may be used.
  • An aspect of some preferred embodiments of the invention relates to minimizing a number of projections required for reconstruction.
  • fewer than 360 projections are used to reconstruct the data.
  • fewer than 64 or 32 projections are used.
  • the projections are selected to be useful projections.
  • projections angles are rejected if a significant portion of the acquired values are at or near noise levels, as a result of passing through a large amount of attenuating material.
  • the restriction of projection angles may be applied to minimize the number of acquired projections. Alternatively or additionally, the restriction is applied to minimize the number of projections taking part in the reconstruction.
  • the object to be imaged and/or its expected errors are analyzed (preferably offline) to determine a small number of projection angles, suitable for reconstruction.
  • An aspect of some preferred embodiments of the invention relates to automatic registration of a to o graphically imaged object to a coordinate system.
  • an object is placed on an imaging platform in an arbitrary orientation and/or position.
  • a rough scan and reconstruction are used to estimate the orientation.
  • the position may be estimated by comparing a size of the imaged object with an expected size and a known fan beam geometry.
  • an identification of the object may be performed based on the results of the rough scan.
  • the raw data of the scan e.g., a sinogram
  • An aspect of some preferred embodiments of the invention relates to determining a threshold for binarizing an image (or thresholds for setting discrete image values to more than two levels).
  • the threshold selected is one in which a zero order moment of the thresholded image matches a zero order moment of some or all of the original projections.
  • the moment is found by binary searching a range of thresholds between 0 and 1.
  • a higher order moment may be used for binarizing or otherwise thresholding an image.
  • a method of image reconstruction for tomographic imaging comprising: providing an indication of an internal structure of an object to be imaged, which object is of non-animal origin; selecting projection angles for reconstruction responsive to the indication; and reconstructing an image from data acquired at the selected projection angles.
  • the method comprises: acquiring data at a plurality of projection angles; and reconstructing a low quality image from the acquired data, where the indication comprises the low quality image.
  • the data acquired at selected projection angles comprises a sub-set of data acquired at the plurality of projection angles.
  • the selected projection angles and the plurality of projection angles each include at least one projection angles not found in the other.
  • the data acquired at the plurality of projection angles comprises a sub-set of all data acquired at the selected plurality of projection angles.
  • selecting projection angles comprises selecting an estimated minimum number of projection angles which would yield a suitable reconstruction.
  • selecting projection angles comprises selecting projection angles responsive to an object complexity.
  • selecting projection angles comprises selecting projection angles responsive to indications of heavy absorption in the indication of internal structure.
  • selecting projection angles comprises selecting projection angles responsive to indications of low absorption in the indication of internal structure.
  • selecting projection angles comprises selecting projection angles responsive to a particular feature in the reconstructed image.
  • the particular feature comprises at least one sharp comer.
  • the particular feature comprises a boundary area of the object.
  • the boundary area comprises a boundary between two materials which comprise the object.
  • selecting projection angles comprises selecting projection angles responsive to an incidence angle of traces of the projection angles with features of the indicated internal structure.
  • a method of image reconstruction for tomographic imaging comprising: providing an indication of an internal stmcture of an object to be imaged, which object is of non-animal origin; selecting at least one resolution for data acquisition responsive to the indication; and reconstructing an image from data acquired using the at least one resolution.
  • the at least one resolution comprises at least two different resolutions for two different projection angles.
  • the at least one resolution comprises at least two different for data acquired at a single projection angles.
  • the resolution comprises a spatial resolution.
  • the resolution comprises a gray-scale resolution.
  • selecting at least one resolution comprises selecting an estimated minimum resolution which would yield a suitable reconstruction.
  • selecting at least one resolution comprises at least one resolution responsive to an object complexity.
  • the object complexity comprises a local object complexity.
  • selecting at least one resolution comprises selecting at least one resolution responsive to indications of heavy absorption in the indication of internal stmcture.
  • selecting at least one resolution comprises selecting at least one resolution responsive to indications of low absorption in the indication of internal stmcture.
  • selecting at least one resolution comprises selecting at least one resolution responsive to a particular feature of the reconstructed image.
  • the particular feature comprises a reference feature used for measurement on the image.
  • the particular feature comprises a boundary area.
  • the particular feature comprises a feature which is itself measured on the image.
  • the indication comprises an estimation of internal stmcture of the object.
  • the estimation comprises a design specification of the object.
  • the estimation comprises an at least two-dimensional representation of the object.
  • the estimation comprises a previously reconstructed image of the object.
  • the estimation comprises an image of a previously imaged object of similar manufacture.
  • the indication comprises a possible deviation from a desired internal structure.
  • reconstructing an image comprises reconstructing only a portion of the object, responsive to the indication.
  • a method of image reconstruction comprising: providing projection data of an object, which object is of non-animal origin; and reconstructing an image from the projection data, where a different reconstruction treatment is applied to at least one portion of the image, where the different reconstmction treatment comprises a different reconstruction method for the at least one portion.
  • a method of image reconstmction comprising: providing projection data of an object, which object is of non-animal origin; and reconstructing an image from the projection data, where a different reconstmction treatment is applied to at least one portion of the image, where the different reconstruction treatment comprises using a different value for at least one reconstruction parameter of a reconstruction method used for the at least one portion.
  • a method of image reconstruction comprising: providing projection data of an object, which object is of non-animal origin; and reconstmcting an image from the projection data, where a different reconstmction treatment is applied to at least one portion of the image, where the different reconstruction treatment comprises reconstructing the at least one portion at a different spatial resolution.
  • the at least one portion comprises at least two portions, each receiving different treatment from each other and from at least a third portion of the image.
  • a method of image reconstmction comprising: providing projection data of an object, which object is of non-animal origin; and reconstmcting an image from the projection data, where a different reconstruction treatment is applied to at least one portion and at least a second portion of the image, such that at least three different treatments are applied to the image, one for each of the portions and for at least another portion of the image.
  • applying a different reconstruction treatment comprises reconstructing at a different resolution.
  • the method comprises: providing an indication of an internal structure of the object, where the different reconstruction treatment is applied responsive to the indication.
  • a method of image reconstruction comprising: providing projection data of an object, which object is of non-animal origin; providing an indication of an intemal stmcture of the object; and reconstmcting an image from the projection data, where a different reconstmction treatment is applied to at least one portion of the image, where the different reconstruction treatment is applied responsive to the indication.
  • providing the indication comprises reconstructing a low quality image of the object.
  • the low-quality image is reconstmcted using a backprojection method.
  • the low-quality image is reconstructed using a Baysian method.
  • the low-quality image is reconstmcted using a Fourier transform method.
  • the low-quality image is reconstructed using a Maximum Likelihood method.
  • the low-quality image is reconstmcted using a Maximum Entropy method.
  • the low-quality image is reconstructed using a different resolution grid than used for the reconstmcting an image.
  • reconstructing an image comprises reconstmcting using an algebraic reconstmction method.
  • reconstructing an image comprises reconstructing using a Baysian method.
  • reconstmcting an image comprises reconstructing using a Fourier transform method.
  • reconstmcting an image comprises reconstmcting using a Maximum Likelihood method.
  • reconstructing an image comprises reconstmcting using a Maximum Entropy method.
  • reconstructing an image comprises reconstructing using a backprojection method.
  • reconstmcting an image comprises reconstmcting using a finer resolution grid than used for the indication.
  • the special treatment comprises setting up an initial estimate of the image, responsive to the indication.
  • providing the indication comprises retrieving an indication generated responsive to an imaging of a similar object.
  • providing the indication comprises providing a design specification of the object.
  • providing the indication comprises providing a manufacturing specification of the object.
  • the special treatment is responsive to an estimated distance, of the at least one portion, from at least one edge of the object, where the estimation is based on the indication.
  • the special treatment is responsive to an expected reconstmction error, of the at least one portion, where the expected error is based on the indication.
  • the special treatment is responsive to an expected manufacturing error, of the at least one portion, where the expected manufacturing error is based on the indication.
  • the special treatment is responsive to a confidence in an internal stmcture of the object, where the confidence is based on the indication.
  • the at least one portion is at least one band surrounding the object.
  • the band comprises a region that overlaps at least an outside edge of the object.
  • the band comprises a region that surrounds an aperture of the obj ect.
  • the at least one portion encompasses substantially only a feature of the object.
  • the feature comprises an area having a particular density.
  • the special treatment comprises utilizing an estimate for selected pixels inside the at least one portion, instead of reconstructing the selected pixels.
  • the special treatment comprises utilizing an estimate for selected pixels outside the at least one portion, instead of reconstructing the selected pixels.
  • the special treatment comprises providing a lower quality reconstmction outside the at least one portion.
  • the lower quality reconstmction comprises a reconstmction with a greater error.
  • the lower quality reconstmction comprises a reconstmction with a lower spatial resolution.
  • a same pre-processing is applied to pixels inside and outside of the at least one portion.
  • a different pre-processing is applied to pixels inside and outside of the at least one portion.
  • the pre-processing comprises filtering.
  • the special treatment is responsive to a level of detail required in the at least one portion. Alternatively or additionally, the special treatment is responsive to measurements to be performed on the at least one portion. Alternatively or additionally, the special treatment is responsive to a material composition of the object.
  • the object comprises a composite material and the special treatment is responsive to at least one characteristic of the composite material.
  • the at least one characteristic comprises a fiber direction of the material. Alternatively, the at least one characteristic comprises a cell size of the material.
  • a method of image reconstruction comprising: providing projection data of an object, which object is of non-animal origin; providing an indication of an internal structure of the object; and reconstructing an image from the projection data, where the reconstructing comprises only reconstructing pixels from the data in at least one certain region of the image, which region has a shape determined responsive to the indication.
  • the at least one certain region comprises at least two non-contiguous regions.
  • the shape comprises a band shape enclosing at least one area of non-reconstructed pixels.
  • the shape is determined from boundaries of the object which boundaries are indicated by the indication.
  • a method of image reconstruction comprising: providing projection data of an object, which object is of non- animal origin; providing an indication of an intemal structure of the object; and reconstructing an image from the projection data, responsive to at least one potential- problem area in the indication.
  • the at least one potential-problem area comprises an edge.
  • the at least one potential-problem area comprises a suspected crack area.
  • the at least one potential-problem area comprises a suspected void area.
  • reconstmcting comprises varying a spatial resolution of the reconstruction, responsive to a location of the at least one potential problem area.
  • reconstructing comprises varying a gray- level resolution of the reconstmction, responsive to a location of the at least one potential problem area.
  • the method comprises defining areas to reconstruct differently, responsive to the at least one potential problem area.
  • the method comprises detemiining a local confidence level responsive to the edges.
  • a method of iterative image reconstmction comprising: providing projection data of an object to be imaged, which object is of non-animal origin; first reconstmcting the object from the projection data; rejecting at least some of the data responsive to the first reconstruction; and repeating the first reconstmction, at least once, after the rejecting.
  • a method of iterative image reconstmction comprising: providing projection data of an object to be imaged, which object is of non-animal origin; generating reconstruction constraints; first reconstructing the object from the projection data, responsive to the reconstruction constraints; rejecting at least some of the constraints responsive to the first reconstmction; and repeating the first reconstruction, at least once, after the rejecting.
  • a method of iterative image reconstruction comprising: providing projection data of an object to be imaged, which object is of non-animal origin; generating reconstmction constraints; first reconstructing the object from the projection data, responsive to the reconstmction constraints; generating relaxation coefficients responsive to the first reconstruction; and repeating the first reconstmction, using the relaxation coefficients.
  • a method of iterative image reconstruction comprising: providing projection data of an object to be imaged, which object is of non-animal origin; generating reconstruction constraints; first reconstmcting the object from the projection data, responsive to the reconstruction constraints; varying values in the first reconstruction responsive to a majorant distribution function; and repeating the first reconstruction, at least once, after the varying.
  • the method comprises: determining the majorant distribution to be zero outside a convex object which is defined by all traces in the projection data which have zero projection values.
  • the majorant distribution function comprises at least three values.
  • the method comprises processing the data prior to repeating the first reconstmction.
  • the first reconstmction comprises a plurality of iterations.
  • the repeated reconstruction comprises a plurality of iterations.
  • the repeated reconstmction comprises applying a different pre-processing to the first reconstruction of the object, responsive to the first reconstmction.
  • the repeated reconstruction comprises applying a different pre-processing to the projection data, responsive to the first reconstruction.
  • the repeated reconstmction comprises applying a different pre-processing to the first reconstruction of the object, responsive to an indication of an internal structure of the object.
  • the repeated reconstmction comprises applying a different pre-processing to the projection data, responsive to responsive to an indication of an internal stmcture of the object.
  • a method of image acquisition of an object comprising: acquiring a set of projection data; reconstmcting a first image from the projection data using a first reconstmction method; analyzing the image to determine special treatment for portions of the image; reconstmcting a second image of the object, using the analysis, where the second reconstmction is a different reconstruction method from the first reconstruction.
  • the method comprises acquiring data for the second reconstmction, responsive to the analysis.
  • the data is acquired responsive to a desired image quality in the second image.
  • the data is acquired responsive to a desired analysis of the second image.
  • the method comprises varying an intensity of ionizing radiation used for the data acquisition, responsive to the analysis.
  • the method comprises varying a wavelength of ionizing radiation used for the data acquisition, responsive to the analysis.
  • the ionizing radiation comprises x-ray radiation.
  • the ionizing radiation comprises gamma radiation.
  • the data is acquired using non-ionizing electro-magnetic radiation.
  • the method comprises varying at least one parameter of a detection circuit, responsive to the analysis.
  • the at least one parameter comprises a gain.
  • the method comprises selecting at least one element of a detection system, from a plurality of available elements, responsive to the analysis.
  • the element comprises a detector.
  • the element comprises a filter.
  • the element comprises a collimator.
  • the second reconstruction method comprises an algebraic reconstruction method.
  • the algebraic reconstmction method comprises an ART-like reconstruction method.
  • the second reconstmction method comprises a Baysian reconstmction method.
  • the second reconstruction method comprises a Fourier transform reconstruction method.
  • the second reconstruction method comprises a Maximum Likelihood reconstmction method.
  • the second reconstmction method comprises a Maximum Entropy reconstruction method.
  • the second reconstmction method comprises a backprojection reconstruction method.
  • the second reconstruction method uses a different resolution grid than the first reconstruction method.
  • the first reconstmction method comprises a backprojection method.
  • the first reconstruction method comprises a Baysian method.
  • the first reconstruction method comprises a Maximum Likelihood method.
  • the first reconstruction method comprises a Maximum Entropy method.
  • the first reconstruction method comprises a Fourier transform method.
  • the first reconstmction failed to achieve a satisfactory convergence for at least a portion of the image.
  • the failure is determined from an error level.
  • the failure is determined from unexpected values for reconstmcted pixel values.
  • a method of image acquisition of an object comprising: acquiring a set of projection data; reconstmcting a first image from the projection data using a first reconstruction method; analyzing the image to determine special treatment for portions of the image; acquiring data for a second reconstmction, utilizing a different data acquisition configuration from a first configuration used for the acquiring a set of projection data, responsive to the analysis, which data acquisition configuration comprises selected elements from an available set of functionally equivalent elements; and reconstmcting a second image of the object.
  • the different configuration uses a different optical detector from the first configuration.
  • the different configuration uses a different filter from the first configuration.
  • the different configuration uses a different detector circuit from the first configuration.
  • the method comprises post-processing the reconstmcted image using an image-processing method.
  • the image processing method is adapted to enhance measurement of features in the reconstructed image.
  • the object is a manufactured object.
  • the object is a cast object.
  • the object is an extruded object.
  • CT imaging apparatus comprising: a source of x-ray radiation; a data acquisition system for acquiring attenuation data corresponding to an x-ray density of an object placed in the system, where the data acquisition system comprises at least one set of functionally equivalent elements; and a controller which selectively selects a particular one of the elements to be used in the data acquisition system, responsive to an analysis performed by the controller, of data acquired through the system, with a different one of the elements.
  • a method of algebraic CT image reconstruction comprising: providing projection data; generating a constraint on a moment of the data; and reconstmcting an image from the data using the moment constraint.
  • the moment comprises a first-order moment.
  • the moment comprises a second-order moment.
  • the moment comprises a higher than second-order moment.
  • a method of generating relaxation coefficients for an iterative algebraic reconstmction method comprising: providing a preliminary image reconstructed with a set of constraints during a given iteration; and setting relaxation coefficients for a subsequent iteration responsive to deviations between the constraints and values in the image.
  • setting relaxation coefficients comprises: comparing the deviations to a threshold; and setting relaxation coefficients responsive to the comparison.
  • the threshold is a function of statistical properties of the deviations.
  • a method of CT image reconstmction comprising: providing projection data of an object from a plurality of projection angles, which object is of non-animal origin; back-projecting the data into a data stmcture representing a varying resolution grid.
  • the data-structure comprises a hierarchical data structure.
  • an industrial inspection system comprising: a feeder of one or more objects to be imaged, which objects are of non-animal origin; and a CT imager, mechanically coupled to the feeder, which images the one or more objects.
  • the source comprises a conveyer belt conveying a plurality of objects.
  • the object is assembled from sub-components.
  • the object is cast.
  • the object is rolled.
  • the object is injection molded.
  • the feeder comprises a take-up device.
  • the object is machined.
  • the feeder comprises an extruder which extrudes an object in a form of a continuous profile.
  • the extmder comprises an extrusion nozzle.
  • the extruder comprises a shaper.
  • the object consists essentially of one material.
  • the object consists essentially of two materials.
  • the object is composed of a composite material.
  • the object consists of more than two materials.
  • the CT imager is synchronized to image an object responsive to a provision of the object by the feeder.
  • the CT imager images the objects using a spiral imaging method.
  • the CT imager utilizes a method as described herein.
  • CT imager for industrial imaging comprising: a detector; an x-ray source; an imaging area; and a manipulator which lifts and conveys objects to be imaged to and from the imaging area.
  • the manipulator comprises a robotic arm.
  • the manipulator comprises a winch.
  • a method of manufacturing quality assurance comprising: manufacturing an object; imaging the object with a CT imager; measuring features on the image to detect deviations in an internal stmcture of the object from design specifications; and rejecting the object if it does not meet the design specifications.
  • the object is cast.
  • the object is extmded.
  • the object is mo Id- formed.
  • the method comprises: analyzing the image to detect at least one material defect; and rejecting the object responsive to the detected defects.
  • the at least one defect comprises a bubble.
  • the at least one defect comprises a void.
  • the at least one defect comprises a variation in density.
  • the at least one defect comprises a crack.
  • the at least one defect comprises a cmst.
  • a method of tomographic reconstruction comprising: acquiring image data from a plurality of projection angles of an object, which object is of non-animal origin; first reconstmcting an image from the image data, using a tomographic reconstruction method; and applying a discretization to the image to convert image values of the reconstructed image into a limited set of allowed image values, where the discretization maintains at least one image property, which at least one image property comprises a moment of the image.
  • the moment is a first-order moment.
  • the moment is a second- or higher- order moment.
  • the limited set of values comprises only two values.
  • the method comprises second reconstmcting the image, in an iterative manner after the applying a discretization.
  • the limited set of values substantially corresponds in number of value and in relative values to expected density values in the image.
  • a method of tomographic reconstruction comprising: providing an object having at least one sub-component with a known geometry, which object is of non-animal origin; acquiring image data of the object from a plurality of projection angles; reconstructing an image of the object using the known geometry of the at least one subcomponent.
  • reconstmcting an image comprises: generating a low quality reconstruction of the image; identifying the sub-component in the image; and further reconstructing the image using the identification, as a basis.
  • identifying the sub-component comprises identifying a position of the sub-component.
  • identifying the sub-component comprises identifying an orientation of the sub-component.
  • the further reconstmcting comprises fixing values for pixels in the image, which pixels correspond to portions of the at least one-sub component, which fixing is responsive to the known geometry.
  • the object comprises essentially of the at least one sub-component.
  • the at least one sub-component comprises two sub-components having known geometries.
  • Fig. 1 is a schematic illustration of a slice of a spherical object, showing deviations from a design specification
  • Fig. 2 is a flowchart of a process of CT image reconstmction, in accordance with a preferred embodiment of the invention
  • Fig. 3 is a flowchart of a method of reducing reconstruction complexity, in accordance with a preferred embodiment of the invention
  • Fig. 4 is a flowchart of a method of selecting projection angles, in accordance with a preferred embodiment of the invention
  • Fig. 5A is a flowchart of a method of defining a multi-resolution grid, in accordance with a preferred embodiment of the invention
  • Fig. 5B is a schematic illustration of an image of an object slice, illustrating a multi- resolution grid defined in accordance with the method of Fig. 5 A;
  • Fig. 6A is a flowchart of a method of generating band limitations, in accordance with a preferred embodiment of the invention;
  • Fig. 6B is a schematic illustration of a banded image of an object slice, determined in accordance with the method of Fig. 6 A;
  • Fig. 7 is a flowchart of a method of iterative reconstruction, in accordance with a preferred embodiment of the invention.
  • Fig. 8A is a flowchart of a method of setting relaxation coefficients, for the iterative reconstmction of Fig. 7;
  • Fig. 8B is a flowchart of a method of creating moment constraints, in accordance with a preferred embodiment of the invention
  • Fig. 9 is a schematic illustration of an imaging device in accordance with a preferred embodiment of the invention.
  • Fig. 10 is a schematic illustration of an embodiment of the invention for quality control of extrusion.
  • Fig. 11 is a schematic illustration of a conveyer belt embodiment of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
  • Fig. 1 is a schematic illustration of a slice of a spherical object 20, showing deviations from a design specification.
  • Object 20 includes a wall 22 surrounding a cavity 24.
  • a dotted line 28 indicates a design specification of object 20, while a line 26 indicates a deviation during manufacture.
  • a significant fraction of the cost of cast objects is a result of uncertainty in the thickness of inner walls. Since such walls require a minimum thickness, cast objects are often manufactured using a greater than required wall-thickness, to compensate for possible errors. It should be noted that the thicknesses and deviations involved are often small - relevant deviations being less than a tenth of a millimeter, for an object whose cross-section may include several centimeters of material.
  • Fig. 2 is a flowchart of a process of CT image reconstmction, in accordance with a preferred embodiment of the invention. The method is first described in general terms, with a more detailed description given with reference to Figs. 3-8.
  • an estimated image of the imaged slice of object 20 is obtained (30).
  • the estimated image is produced by a low quality imaging of object 20.
  • a small number of projections for example 16, is used to reconstruct the image slice.
  • a filtered backprojection reconstruction method is used.
  • an edge detection method is performed on the image, since backprojection often generates blurred images.
  • object 20 is essentially a known object, for example manufactured according to design specifications.
  • the design specifications and/or CAD files are analyzed to provide the estimated image.
  • a low quality data acquisition and/or reconstruction are used to judge a similarity between the design specification and the imaged object, to determine whether the design specifications may be used as a rough estimation of the object slice.
  • the sinogram of the acquired data is analyzed to determine a similarity to the design specifications and/or CAD files, and no reconstruction is necessary.
  • a low quality data acquisition and/or reconstmction is used identify which object is being imaged.
  • such an identification may be performed by matching an acquired sinogram to a virtual sinogram of the design specifications, without reconstmcting an estimated image.
  • the estimated image is then preferably analyzed (32) to determine the possibility and/or extent of applying complexity reduction methods, in accordance with a prefe ⁇ ed embodiment of the invention.
  • the analysis includes edge detection of the object boundaries.
  • Additional projection data may be acquired (34).
  • the projection angles and/or other parameters of the acquired projections are determined responsive to analysis (32) and/or the currently acquired data.
  • projection angles at which a radiation trace passes through a large amount of material and is attenuated to a system noise level are not used and/or data not acquired.
  • a sensitivity of a detector array may be preset to an expected radiation transmission level.
  • a source radiation level may be set responsive to an expected attenuation.
  • the data may be analyzed again, so that parameters of reduction in reconstmction complexity (36) may be determined.
  • the reduction is achieved by reducing the number of pixels to be reconstmcted and/or their resolution and/or accuracy of reconstruction.
  • the acquired projection data is then reconstmcted as a reconstmction "A" (38).
  • an iterative reconstmction method is used, preferably ART or an ART-like reconstmction method.
  • the estimated image is used as a starting point for the iterations.
  • a starting point is generated responsive to the estimated image and/or a-priori knowledge.
  • a discrete reconstmction method is used.
  • a continuous reconstmction method is used.
  • a description of ART may be found in GT Herman, "Image Reconstmction from Projections", in the "The Fundamentals of Computerized Tomography", 1980 or in Y. Censor, “Finite Series-expansion Reconstruction Methods", Proceedings of the IEEE, vol. 71, No. 3 Mar. 1983, pp. 409-419, the disclosures of which are incorporated herein by reference.
  • the second reconstruction step includes rejecting data and/or reconstmction constraints which appear problematic (40) and then performing a reconstruction "B" (42).
  • the second reconstruction is also an ART reconstruction. It is noted that in some prefe ⁇ ed embodiments of the invention one or both of the reconstructions may use non- ART or even non-iterative reconstmction techniques. However, one benefit of using ART-like techniques is their suitability for parallel processing. Additionally, in some ART-like technique, there is freedom in selecting the reconstruction starting point.
  • pre-processing operations especially clean-up operations may be performed on the partially reconstmcted image and/or on the projection data.
  • a smoothing filter may be applied.
  • the pre-processing is responsive to the partially reconstmcted image, for example a determination of problematic areas in the image. For example smoothing may not be applied to these areas.
  • the pre-processing is responsive to the earlier provided indication of the imaged object. For example, smoothing may not be applied to complex portions of the object or to data from which these portions are reconstructed.
  • edges are preferably determined in the image slice to reconstruct an outline of a slice of object 20 (44).
  • Fig. 3 is a flowchart of a method of reducing reconstruction complexity (36 in Fig. 2), in accordance with a preferred embodiment of the invention.
  • steps in Fig. 3 are shown in a particular order, it will be clear from the following discussion that these steps may be performed in any order or even that two steps combined into a single step. Also, some or all of these steps may be performed in conjunction with analyzing the estimated image (32 in Fig. 2) or even before an estimated image is obtained (30), for example if an object specification is entered into the system. It should be noted that each of the steps in Fig. 3 may be individually applied. In particular, in some embodiments of the invention, only one or even none of the steps of Fig. 3 are applied.
  • the steps in Fig. 3 include: (a) selecting only a limited number of projection angles (50);
  • Fig. 4 is a flowchart of a method of selecting projection angles, in accordance with a prefe ⁇ ed embodiment of the invention (50 of Fig. 3).
  • Selecting projection angles may serve two ends, (i) to reduce the number of projections used in a reconstmction, possibly reducing memory and/or CPU requirements; and/or (ii) to use more suitable projections, reducing e ⁇ or and possibly assuring a faster convergence.
  • some or all of the projection data is acquired only after projection angles are selected. Additionally or alternatively, the projection data may first be acquired and only that data which matches the desired projection angles is used for reconstmction.
  • a step 60 the sinogram and/or estimated image and/or a-priori knowledge, such as design data, are analyzed to determine suitable and/or unsuitable angles.
  • angles at which significant traces are completely attenuated by object 20 are considered to be unsuitable.
  • significant traces are those which pass through important pixels (e.g., based on user indication and/or automatic analysis) and/or traces which pass through pixels which do not have many associated traces.
  • the angles are selected so that the dynamic range of expected values in a single projection is minimized and/or matches system limitations.
  • the angles are selected so that the traces do not pass through areas which are difficult to reconstmct.
  • the angles are selected so that portions of the image which are of interest are "illuminated" with substantially independent traces, to whatever extent possible that allows a reasonable image to be reconstructed.
  • a preset number of projections is used, for example 64 or 32.
  • the possible projections are preferably ranked (62), for example according to the criteria of step 60.
  • the 32 or 64 best angles are preferably selected (64).
  • An exemplary prefe ⁇ ed selection criterion is that the selected angles match a certain angular distribution function over the range of possible projection angles. Additionally or alternatively, a criterion is that the projection angles are not clumped together.
  • the number of projections may be determined according to which object is being imaged.
  • One method of selecting a number of projection angles is by simulating reconstruction with varying numbers of projections and/or particular projection angles and selecting a set of angles which yields satisfactory results and/or minimizes computation time.
  • the projections may be selected by analyzing a geometrical shape of the object and/or the imaged slice.
  • an object to be imaged may be placed in the system at a random orientation and a low resolution acquired image and/or sinogram are used to estimate its orientation, for example, facilitating selection of projection angles and/or registration with an a-priori shape, for example a CAD drawing.
  • a plurality of projections may be acquired at near angles, and the best and/or most angularly exact projection used for reconstruction. Thus, a lower angular accuracy is needed.
  • the slice to be imaged is preferably selected to match a slice in a design specification.
  • a smallest amount of deviation should be expected.
  • any deviation determined may be more meaningful if the deviation is indicated on a design drawing of the slice.
  • a report of the results of imaging an object include a visual, statistical and/or location specific analysis of any deviations found.
  • the other steps (52, 54 and 56) of Fig. 3 are all associated with identifying and/or selectively dealing with particular portions of the image slice.
  • the selection of projection angles is made responsive to these steps. For example, the above described data acquisition dynamic range may be determined only for traces which pass through important areas. Additionally or alternatively, projection angles may be selected based on how data for these particular portions is acquired, and not necessarily with respect to the entire image.
  • Fig. 5 A is a flowchart of a method of defining a multi-resolution grid (52 of Fig. 3), in accordance with a prefe ⁇ ed embodiment of the invention.
  • Fig. 5B is a schematic illustration of an image of an object slice 70, illustrating a multi-resolution grid 72 defined in accordance with the method of Fig. 5 A.
  • smaller pixels are assigned to portions of the image slice in which a higher imaging resolution is required.
  • the pixel size is determined responsive to an expected local e ⁇ or and/or range of density values.
  • object 20 is being imaged to detect deviations from a known design.
  • a higher imaging resolution is typically required at or near the outline of object slice 70.
  • certain heuristics may be applied to the image, based on task parameters. For example, in some object slices it is not expected there to be an island smaller than a certain size. Thus, based on a rough reconstmction at a resolution of that size, it is possible to determine all of those image areas in which there might be a non-empty pixel. It is noted that in typical object inspection applications, there are only two or three materials in object 20, e.g., metal, air and possibly spacers. Preferably, pixels whose values are relatively certain remain large, while pixels whose values are expected to change are made smaller.
  • Fig. 5 A illustrates a method of providing small pixels at object slice boundaries.
  • these small pixels are preferably preset with an estimated density value, based on the estimated image.
  • the pixel values may take expected deviations in the object manufacture into account.
  • Minimum and maximum pixel (cell) sizes are set (80).
  • the estimated image slice is divided into pixels of the maximum size and each large pixel is assigned an initial density value (82). For each pixel, if the pixel is not at the minimum allowed size and its (normalized) density is neither 0 or 1, the pixel is split into smaller pixels, preferably four or nine (84).
  • each pixel is assigned a calculated "real" density value (86).
  • the density value is the amount of "black” (attenuation) in the area covered by the pixel divided by the size of the pixel.
  • the virtual density is the density of the pixel from which the pixel was split off.
  • other density functions may be used, for example, the pixel value being a function of a distance from the boundary.
  • Tl is the minimum pixel size.
  • Tl may be spatially varying, for example, being a function of a distance from a boundary (or band - see Fig. 6A)
  • the process is preferably repeated until all the pixels have values of 0 or 1 or are at a minimum size.
  • all pixels that have the density values of 0 or 1 and are not minimal in size are not changed during reconstmction.
  • the projection data is preferably sensitivity-corrected to accommodate ignoring the pixels, for example, by reducing the attenuation values of the projection data.
  • Tl and the min and max pixels sizes are the same over the entire image. Alternatively or additionally, different values are used for different parts of the image.
  • a grid 72 is shown (for only a portion of the image slice), which is a result of applying the method of Fig. 5 A.
  • Pixels 74 are the original maximum size
  • pixels 76 are an intermediate size
  • pixels 78 are a minimum size.
  • the pixels on the boundary are automatically determined to be of a minimal size.
  • other pixels may be set to be the minimal size.
  • a pixel on the boundary may be assigned a size larger than the minimum size, for example if only a low-quality reconstmction is required at that point.
  • the pixel size may be defined to be a monotonically increasing function of the pixel's distance, starting for example at a distance of 1 standard deviation (of the expected or allowed errors) from a boundary.
  • the method of Fig. 5A may be modified so that a pixel is split into smaller pixels responsive to a required reconstruction quality and/or interest in the pixel and/or in a pixel whose reconstmction is significantly affected by that pixel.
  • the pixels are square pixels and at each step of the method of Fig. 5 A they are split into four equal parts.
  • non-square pixels may be used, for example hexagons or other polygons.
  • a five- or nine- way split and/or a non-symmetric split may be used.
  • the pixels are not aligned with a regular grid.
  • the pixels have a non-isotropic resolution, for example being higher along one axis of the object than along a different axis.
  • different pixel grids may be used for different parts of the image, the grids preferably being selected to match image reconstmction requirements.
  • the pixels may be overlapping.
  • the pixels are stored as pixel center values, in which pixel values are determined by interpolation between values at pixels centers and/or derivatives.
  • Fig. 6A is a flowchart of a method of generating band limitations (steps 54 of Fig. 3), in accordance with a prefe ⁇ ed embodiment of the invention.
  • Fig. 6B is a schematic illustration of a banded portion 100 of an image slice, in accordance with the method of Fig. 6 A.
  • many deviations in an object manufacture process occur at boundaries of the object. Assuming the deviations to be bounded by some amount, some pixels of an image slice are not expected to include any material (empty pixels). Alternatively or additionally, other portions of the image may be expected to include a full pixel.
  • two bands 104 and 102 are defined around portion 100.
  • the density of a pixel (noting that a boundary pixel may have an intermediate density) is only in doubt in this band.
  • a central area 103 (bounded by dashed lines), it is expected that all pixels will be full.
  • an external, distant area 105 it is expected that all the pixels will be empty.
  • These pixels (in regions 103 and 105) are preferably fixed in value and are not modified during reconstmction.
  • non-zero density values of pixels are allowed outside the band. However, in many cases, only zero values will exist outside the bands. Alternatively or additionally, the allowed values may be a continuous, not necessarily limited to 0 or 1, both inside and outside the band. It is noted that if the full range of deviation is found in a part of the image, the quality of reconstmction at that point may not be high. However, such an object is probably defective, so that exact level of defect may not be important. Thus, larger pixels may be tolerated at the outer reaches of bands, in some embodiments of the invention.
  • a first step is to determine "e ⁇ or locations", in which deviations are expected or may occur (106).
  • different amount of deviations may be expected or allowed on different parts of the imaged object.
  • certain parts of the object may be pre-defined to be especially problematic and requiring a higher quality reconstmction and/or a greater allowed deviation range.
  • certain pixels may be determined to have a known density and thus are not reconstmcted.
  • the imaged object may include a jig having exactly known dimensions, The pixels of the jig are preferably identified and removed from consideration. Alternatively or additionally, certain pixels may be reconstructed even through they are outside defined bands.
  • any wall having a thickness of less than twice that certain size may be reconstructed to make sure it does not include bubbles.
  • Such reconstruction possibly being less limited with respect to density values and attenuation.
  • an expert system which analyses the object slice (estimated image), the manufacturing method and/or previous detected deviations and generates a map of areas of the object which are prone to be problematic.
  • a map may be provided by a human expert.
  • the band sizes and/or certainty levels are generated by simulated imaging of the object.
  • the system includes a simulator in which different e ⁇ or and imaging conditions may be tested to determine, for example, the effect of imaging angle and pixel size of the detection of deviations.
  • the reconstruction and/or small pixel sizes may be limited to a portion of the object on which measurements are performed. Preferably, the projection data for radiation traces which do not pass through these areas is ignored.
  • the band is extended to be aligned with a pixel boundaries and/or pixels of at least a minimal size.
  • the spatial resolution of reconstmction is varied responsive to determination of problematic areas.
  • the gray-level resolution is enhanced for problematic areas, possibly by increasing the detection sensitivity and/or gain for traces which pass through the problematic areas.
  • each pixel is assigned an a-priori certainty level regarding its value, possibly based on an estimated image.
  • pixels which are closer to an object have a greater probability of being full than pixels which are further away.
  • the form of this dependency may vary over the image, preferably taking into account local e ⁇ or conditions.
  • it may be different for pixels inside the object and for pixels outside the object, for example reflected a greater probability of material being missing outside the nominal edge than there being extra material present.
  • a linear dependency on distance from the edge is used to relate a distance with a certainty value.
  • a square or an exponential dependency is used for the relationship between distance and certainty.
  • a plurality of (possibly nested) bands may be defined, each band having its own certainty level and/or zero- order approximation.
  • the certainty levels are used as a constraint in the reconstruction process, so that it is more difficult to "fill" a pixel which has a low certainty of being “full”.
  • the certainty levels are only used for the first reconstruction, for example to set initial density values.
  • the certainty levels are changed during the reconstmction.
  • the same certainty levels are used throughout the reconstruction.
  • the certainty values are used as a probability field, so that a calculated density of a pixel is a product of a reconstmcted density value and a certainty value. Such a product may be applied to an estimated image between reconstmction iterations.
  • the certainty level may be a set of values for each pixel, indicating the probability of the density value being within a certain range.
  • one pixel may have a high certainty of being “1”, another pixel a high certainty of being "0.5” (e.g., a different material) and another pixel a high certainty of being “0".
  • the certainty levels may comprise a multi-modal distribution function, whereby a pixel is more likely to have a value between 0.1 and 0.2 or 0.7 and 0.8 than any intermediate value.
  • different radiation levels are determined for different projections, for example to maximize a detector sensitivity.
  • the radiation level optimization and/or other imaging parameter optimizations only take into account the imaging of pixels which are inside the bands.
  • Fig. 7 is a flowchart of a method of iterative reconstmction, in accordance with a prefe ⁇ ed embodiment of the invention.
  • some of the projection data is first removed from consideration.
  • a hull is generated (110).
  • the hull is preferably defined as a convex geometrical object which su ⁇ ounds the object and outside of which no pixel of the object can exist.
  • projection data from traces which do not intersect the hull are removed from consideration.
  • the hull is defined for the object including a band.
  • the hull is defined only for the object itself, excluding the band, preferably by directly analyzing projection data.
  • the hull is defined to include a different width of band than used for reconstmction.
  • the hull is used to define a determinate majorant distribution function for the reconstmcted density.
  • each pixel may be assigned a minimum between the pixel's reconstmcted density value and the hull value, preferably before preceding with the iteration.
  • each pixel value may be clipped to an expected value range (e.g., between 0 and 1).
  • the hull may include a plurality of values, for different pixels, thus, one pixel may be limited (clamped) to a maximum value of "0.5" and another pixel to the value "0.9".
  • the number of clamping values may depend on the number of materials and/or on the certainty levels.
  • pixels which fall on (or very near) a boundary in the reconstmcted image are not clamped.
  • initial values are set for each pixel which is under consideration (112). Some of these values may be set using the method of Fig. 5A. In a prefe ⁇ ed embodiment of the invention, the initial values are selected so that a moment of at least some of the projections is maintained. Alternatively or additionally, the moment of all the projections are maintained. The maintained moment is preferably a zero-order moment. However, first, second or higher order moments may also be maintained.
  • an iterative reconstructive technique such as ART is used.
  • each radiation trace is co ⁇ ected for beam hardening, for example using a look-up table (and/or a previous estimate of the image).
  • the look-up table is calculated.
  • the look-up table is empirically derived during a calibration of the imaging device.
  • relaxation coefficients are preferably used to help the iterations converge to a final image.
  • every one or more iterative steps (114) may be followed by a step of re-setting relaxation coefficients.
  • an intermediate reconstruction may be binarized (or converted to an imager with more than two discrete values) between iterations.
  • binarization uses a threshold which preferably preserves a moment of the reconstmcted image and/or of the projection data.
  • the iterations of a reconstruction step are repeated until an exit condition is met.
  • the exit condition is that an absolute e ⁇ or between a reconstmcted image (or a binarized version thereof) and the projection data is smaller than a predetermined e ⁇ or level.
  • the exit condition comprises a time and/or computer operation limitation on the reconstruction.
  • the exit condition comprises determining that the amount of change in e ⁇ or over previous reconstmction iterations is smaller than a predetermined amount and/or bounded.
  • the above predetermined amounts may be functions, for example of the count of the reconstmction step being performed, the complexity of the image and/or reconstmction parameters.
  • a measure of local (or global) image complexity is the number of edges crossed by a trace. Another possible measure is the density of comers in an area. Another possibly measure is the average feature size, where a feature may be an edge or a line. Another possible measure is a standard deviation of local image properties.
  • an image complexity measure is a combination of one or more of the above measures and/or of image complexity measures which are known in the field of image processing.
  • the reconstruction for one part of the image may be paused or stopped while reconstmction continues for a second part of the image, for example based on the error in one part of the image being acceptable and the e ⁇ or in the second part being unacceptable.
  • the image is partitioned so that the traces intersect only one part and not the other.
  • different reconstruction techniques may be applied to different parts of the image.
  • different techniques may be applied to different image slices and/or different iterations.
  • the reconstruction may comprise two or more sets of iterations, in-between which problematic constraints may be removed (40 in Fig. 2).
  • a constraint is so identified if it deviates from the reconstructed image by more than a threshold value.
  • the threshold value is a function of the number of iterations and/or the mean and/or other statistical properties of the deviations.
  • statistical properties are calculated over the deviations for this (for which the constraint is determined) and/or other parts of the image or for the entire image.
  • the statistics are of the projection data for this and/or other parts of the image or for data for the entire image.
  • the statistics are of previously reconstructed image values in part or all the image.
  • the identification may be a function of statistical properties of deviations for that constraint over several iterations. For example, a constraint for which the deviations cycle between extreme values, may be a problematic constraint.
  • problematic constraints do not comprises a significant portion of the total path of a certain projection direction
  • the constraints are removed.
  • a significant part is defined to be more than 30, 20 or 10% of the data.
  • the constraints may be removed even if they form more than 30% of the data.
  • a problematic constraint may be provisionally removed and if this removal does not aid convergence, the removal may be reversed.
  • problematic constraints may be identified based on the existence of unexpected patterns in a partially reconstmcted image.
  • an island may be an unexpected feature.
  • a spike i.e., a pixel with value 1 surrounded by pixels with value zero may be assumed to be an e ⁇ or.
  • a sharp comer is often unrealistic.
  • the unexpected patterns may be determined based on the manufacturing technique and/or other a-priori knowledge, such as CAD drawings
  • the system of constraints is adjusted by eliminating the strongest constraint (e.g., the most limiting one).
  • more projection data may be acquired to generate more constraints.
  • the reconstruction for at least that area (of the constraints) may be repeated, for example if an area is identified to include a comer, special reconstruction parameters for a comer may be used, so that no constraints may need to be removed.
  • Some causes of problematic constraints include detector noise, detector blurring, indeterminate detector response, source noise, scattering and/or diffraction, thickness resolution limitations, very high or very low absorption (thick slabs or comers), non-uniform matter (for example caused by bubbles), poor calibration, poor object positioning and/or orientations, vibration and/or motion.
  • other clean-up operations may be performed between image reconstruction stages, for example image smoothing, averaging of pixel values, edge detection and enhancement and thresholding, for example, to assist in convergence.
  • the size of pixels does not change over the course of iterations.
  • the pixels size and/or the width of a band may be changed. In one example, if the value in a pixel reaches 1 (or 0) (to within a given precision) and/or if the pixel is surrounded by pixels of density value 1 (or 0), the pixel and/or its neighbors are removed from consideration in further iterations.
  • the band may be widened if several non-zero valued pixels encroach on the edge of the band.
  • pixels may be reduced in size if they fail to converge on a value of 0 or 1.
  • Fig. 8A is a flowchart of a method of setting relaxation coefficients, for the iterative reconstmction of Fig. 7 (step 116).
  • different values of ⁇ are used for different constraints. Preferably, these different values are related to a size of deviation between the constraint and a projection of the reconstmcted value.
  • these relaxation coefficients may be set anew every one or more iteration steps.
  • the deviations of reconstmcted values from the constraints are determined (120). These deviations are preferably compared to each other and/or to their historical values (122).
  • New relaxation coefficients are preferably determined (124).
  • the new coefficients are constrained to the range 0 to 1, preferably, 0.05 to 1.
  • deviations are considered problematic only if they are outside a certain gate, for example a 3 ⁇ gate (around the expected value).
  • the relaxation coefficients are an increasing linear function of the deviations. Alternatively to a linear relationship, a quadric relationship is used. Alternatively, a power or an exponential relationship are used. Possibly, different relationships between deviations and relaxation coefficients may be defined for different portions of the image.
  • the relaxation coefficient may be maintained at its previous value or set to a predetermined value, for example 0.05.
  • a moment based constraint may be defined, for example, to constraint the total area (mass) of the image.
  • a zero order moment is used.
  • a higher order moment such as 1 st or 2nd order moment is used.
  • the moment is an average of moments for several projection directions, especially if non-parallel beams are used or in the presence of noise.
  • a moment of the projection(s) is calculated (126) and a constraint derived from its moment is generated (128), which limits the reconstmcted image as a whole rather than individual pixels.
  • moment constraints or other multi-pixel constraints may be defined for portions of an image.
  • the reconstmction is completed at this point.
  • additional post reconstruction steps may be performed, for example to clean-up the image, as described above with reference to clean-up operations between reconstmction steps.
  • the image is checked after reconstmction to determine that it matches the projection data.
  • the reconstructed image is projected at the projection angles and the amount of discrepancy between the projection data and the generated projections is noted.
  • the amount of discrepancy is used to indicate an expected e ⁇ or level in the image and/or measurements, to an end user.
  • the reconstructed image may be rejected and further data acquisition may be required.
  • only certain portions of the image are checked.
  • different amounts of discrepancy are allowed for different portions of the image.
  • the image is binarized or otherwise thresholded to include more than two discrete values) so that it includes only a discrete set of values, preferably, each value co ⁇ esponding to a material in the object.
  • the threshold is selected so that a zero (and/or higher) moment of the image is not changed by the thresholding operation (to within a preset precision level).
  • a proper threshold is found by performing a binary search over a range of threshold values between 0+ ⁇ and 1- ⁇ .
  • edges are detected on the resulting image.
  • these edges are used to perform measurements and/or for other inspection tasks.
  • a prefe ⁇ ed type of edge detection is described in J.C. Russ, The Image Processing Handbook, p. 674, 2nd Ed., CRC Press, 1994, the disclosure of which is incorporated herein by reference.
  • the image may be analyzed using other tools, for example pattern recognition tools.
  • the image may be used for non-inspection tasks, for example for promotional or instructional purposes.
  • various reconstmction parameters may be varied and/or relaxed, responsive to the required measurements, so that a faster and/or more efficient reconstmction is possible.
  • a lower pixel resolution may be used, at least at comers.
  • the imaging device may be calibrated so that the x-ray beam is collimated best at those portions of projection angles which generate traces which pass through pixels of interest, possibly at the expense of other pixels in the image slice. In some cases this may require translating the collimator perpendicular to the x-ray beam, so that a higher quality portion of the collimator interacts with a desired portion of the beam.
  • the reconstructed image may be analyzed to identify problematic portions thereof, for example inconsistent or unexpected absorbency. Such an portion may require more attention.
  • the extra attention is provided by repeating the reconstmction of the portion, possibly requiring new data to be acquired, possibly at higher resolution or with better statistics.
  • large bubbles can be viewed by using a high resolution. Smaller bubbles, which cannot be viewed, affect the average density, which can be measured.
  • Other problematic areas include areas in which two materials or two sources are blended, for example plastic and additives such as color or plastisizer or two separate aluminum billets.
  • intermediate density values may be allowed (for reconstmction) in some parts of the object and not allowed in others. Alternatively, such intermediate values indicate a potential problematic area.
  • an additional reconstmction step may be required for some portions of the image.
  • comers of complex portions of an object it may be desirable to acquire data at additional and/or different projection angles.
  • very dense portions or portions with a very low density might be outside the dynamic range of the imaging system.
  • these portions are imaged again with higher (or lower) intensity x-rays, so that data is acquired within the dynamic range.
  • the interface area between two materials is analyzed to detect the position of the boundary.
  • the interface area is analyzed to determine the presence of a c st which forms or separates between the two materials.
  • the boundary thickness is typically much smaller than the typical pixel sized used for image reconstruction.
  • a smaller pixel size is used for the boundary area.
  • at least one projection angle is selected so that the x-ray beam is substantially parallel to at least a portion of the boundary area. In a prefe ⁇ ed embodiment of the invention, an increased data acquisition resolution is only utilized along part of the boundary.
  • that part of the boundary comprises a plurality of boundary areas which are selected as a statistical sample of the entire boundary area of interest.
  • boundary areas which are selected as a statistical sample of the entire boundary area of interest.
  • by analyzing these areas it is possible to determine a probability of their being defects along the boundary and/or its average thickness.
  • such a statistical analysis approach is applied over the entire image to look for defects and/or determine other types of inconsistencies between the reconstmcted image and the object design
  • Fig. 9 is a schematic illustration of an imaging device 200 in accordance with a prefe ⁇ ed embodiment of the invention.
  • device 200 utilizes an x-ray tube (or other source) 202 and some type of detector 216.
  • device 200 uses very highly collimated beams, to achieve a high spatial resolution.
  • scintillator detectors do not provide the required resolutions.
  • device 200 may comprise three x-ray collimators and an optical collimator, for a CCD based detector 216.
  • device 200 includes a computer 218 which controls device 200 and/or reconstructs images from data acquired thereby.
  • a first x-ray collimator 204 is placed at an exit of tube 202.
  • a second x-ray collimator 206 is placed between tube 202 and an object 208 to be imaged.
  • a third x-ray collimator 210 is preferably placed after object 208, to remove scattering and diffraction in object 208.
  • the patterned x-ray beam is preferably detected using a screen-camera combination.
  • a screen 212 is preferably i ⁇ adiated by the beam.
  • Light exiting the screen is preferably collimated by an optical collimator 214 (which preferably absorbs x-ray radiation) and is detected by a linear CCD camera 216.
  • the screen is read out using laser beam.
  • the screen is a florescent screen, which emits light in the pattern of the x-ray beam for a short time after the irradiation.
  • the screen acts as a scintillator crystal, and the object is i ⁇ adiated again for each data line acquired.
  • the CCD camera has a high spatial resolution, such as 20 micrometers and a large number of elements, such as 10,000.
  • a TDI type CCD camera is used, to compensate for any motion in the image and/or synchronize with motion transverse to the camera and/or increase the cameras light sensitivity.
  • One cause of motion is vibration.
  • a two dimensional CCD camera may be used.
  • camera 216 may have a smaller field of view than screen 212, so that camera 216 is required to mechanically or optically scan across the surface of screen 212.
  • the scanning is along one axis only.
  • the scan is two-dimensional.
  • camera 216 includes a zoom lens, to allow control over object size and data acquisition resolution.
  • object 208 is brought into place using a conveyer belt, a crane, a robotic arm and/or other actuators known in the art.
  • the movement of the object is synchronized to the imaging, such that as soon as an object is brought it is imaged. This may be achieved using central control of both the feeder and the object.
  • a sensor on the imager may activate the imager when an object trips the sensor.
  • the projection angles are achieved by moving the detector and/or the x-ray tube. Alternatively or additionally, the object is moved and/or rotated.
  • device 200 is checked and/or calibrated using x-rays.
  • a phantom is used.
  • device 200 may be tested using an optical test pattern instead of screen 212.
  • a light source may be used instead of object 208.
  • device 200 uses a parallel beam of x-ray radiation.
  • device 200 uses a fan-beam configuration.
  • device 200 utilizes a scanning pencil beam.
  • the calibration and/or operation of device 200 may interact with the reconstruction method chosen.
  • the collimator may be closed, so that only a narrow x-ray beam is generated and less diffraction and scattering is generated.
  • the smallest pixel size may be made larger.
  • a smaller pixel size may be used to compensate.
  • device 200 includes multiple detection path elements, each optimized for particular data acquisition conditions. In a prefe ⁇ ed embodiment of the invention, the path element is automatically selected by the device, responsive to data acquisition requirements.
  • device 200 includes multiple collimators, for example higher and lower resolution collimators.
  • device 200 includes a plurality of optical detectors of different spatial, temporal and/or gray level resolution and/or of different sensitivity response, gain control, sensitivity range, spectral response and/or other detection parameters.
  • device 200 includes multiple optical or x-ray wave-length filters (not shown).
  • device 200 includes multiple signal processing circuits (for example incorporated in computer 218).
  • device 200 includes multiple scintillation or solid-state radiation detectors 216.
  • Fig. 10 is a schematic illustration of an embodiment of the invention for quality control of extmsion.
  • an extruded object 222 is extruded by a nozzle 224 connected to a material source 220.
  • a CT imager is positioned as shown by reference number 228, so that a cross- section of object 222 is imaged.
  • the CT imager is positioned at reference number 226, so that the both object 222 and nozzle 224 are imaged simultaneously.
  • problems in the extrusion process itself e.g., bubbles
  • wearing out of nozzle 224 may be detected.
  • object 222 is in continuous motion, and helical CT reconstruction techniques are used.
  • virtual projection data is generated by interpolation between nearby projection angles and/or positions of object 222.
  • the nozzle does not move, so helical scanning may not be applicable.
  • helical scanning may be utilized to image object 208 while it is being placed inside device 200. Such helical scanning is useful for more efficient volume imaging.
  • helical scanning and/or reconstmction may be used to abolish the need to stop the movement of object 208 (due to its being brought in) and/or its internal components before imaging.
  • a defect in object 222 may have a considerable axial dimension, thus, motion of object 222 may be ignored during reconstmction.
  • inconsistencies during reconstruction may indicate a defect, in and of themselves.
  • an image of object 222 is reconstructed in substantially real-time, for example, in less than 10, 5, 1, or 0.1 seconds.
  • the number of pixels in the reconstmcted image is at least 1,000,000, 20,000,000 or 100,000,000 pixels. This type of resolution and reconstmction time may also be used for imaging moving objects, such as a watch, in operation. It is noted that using the methods described herein and noting that major portions of the imaged object have nearly or exactly known geometries, the actual amount of processing requirement is preferably reduced as compared to prior art methods.
  • the CT imager is used to image objects constmcted of one or more of metal, glass, plastic, mbber, concrete, wood, composite materials and/or other industrial materials.
  • the imager is used to image objects comprising a plurality of materials, for example aluminum with plastic spacers.
  • the imaged objects are of non-animal origin, thus excluding living and dead human bodies.
  • composite materials are imaged in a way which reveals information about their compositing materials.
  • a higher spatial resolution may be provided (e.g., pixel size) in a direction of a grain of the material.
  • a very small pixel size is provided in at least parts of the object, to better view the grain of the individual component materials.
  • the radiation used for imaging may also be used for radiation treatment, for example to generate cross-linking in the polymer material and/or to cure it.
  • Objects imaged by the CT imager described herein may be manufactured using substantially any known manufacturing method, including, but not limited to: casting, extrusion, micro-extmsion, roller-milling, laminating, machining and/or assembling with and/or without connectors.
  • some prefe ⁇ ed embodiments of the invention are suitable for continuous processes, such as extrusion, as well as discrete processes, such as casting.
  • a cast object may be imaged while it is still in a cast.
  • imaging of a single slice may be sufficient to approve a specimen.
  • the imaged objects may have a cross-section between 10 and 100 cm, or even larger, for example being larger than 20, 60 or 100 centimeters.
  • the resolution is preferably between 0.001 and 0.1 mm, for example being better than 50, 20, 7 or even 1 micrometers.
  • an object may be too large to image at one time and overlapping portions of the object may be imaged.
  • the object is rotated (rather than the detector and/or the x-ray source. Possibly, knowledge of the internal stmcture of the object is used to estimate the orientation of the object. Alternatively or additionally, a rotation sensor on a rotational actuator used to rotate the object is used to estimate its angular position.
  • a plurality of axial slices of the object are acquired using multiple axially displaced arrays.
  • a plurality of trans-axially overlapping detector arrays may be used to receive radiation form the object or to read a phosphor display which receives radiation from the object.
  • small objects may be imaged, including for example minerals, mineral samples, gem stones (cut or uncut) and/or artificial gems stones. Such analysis is preferably used to find hidden flaws, bubbles and/or structures.
  • Fig. 11 is a schematic illustration of a conveyer belt embodiment of the invention.
  • a conveyer 240 conveys objects 242 to be imaged to a CT imager 230.
  • all the objects on the conveyer are imaged.
  • only a sample of the objects are imaged, for example responsive to a reconstmction time constraint of imager 230.
  • only a sample of the manufactured objects are passed along conveyer 240 to be imaged.
  • fast imaging of an object 242 is made possible by utilizing multiple radiation sources (not shown) and/or multiple detectors and/or multiple detector rows.
  • fast imaging utilizes an electron beam- CT imager, in which a ring shaped target is scanned using an electron-beam, to generate a fast moving source.
  • an electron beam- CT imager in which a ring shaped target is scanned using an electron-beam, to generate a fast moving source.
  • electron beams from a source at a location 232 are sent to a target 234 which is located along a half ring (shown as part of the dotted line).
  • a target 234 shown as part of the dotted line.
  • Such an image may also be used in the embodiment of Fig. 10.
  • the operation of the CT imager may be controlled by a user.
  • input which may be provided by a user, include: number of projections, required image quality, portions of the image which are of interest, estimated image, co ⁇ ections to an estimated image, identification of problem portions of an object, corrections to a reconstructed image and/or answers to questions posed by the CT imager.
  • Such questions may be posed by the CT imager, for example in case the imager stalls or cannot reconstruct an image. Answers to such questions may include, for example a selection between two alternate reconstmctions or resetting of certain reconstruction parameters.
  • each object to be imaged may utilize different reconstmction and/or acquisition parameters.
  • the parameters are selected based on a simulation n on the object design.
  • the simulation may be used, for example, to determine which parameters yield a fastest reconstruction, highest probability of detecting e ⁇ ors, assuring convergence and/or require a shortest data acquisition time.
  • such parameters may be determined by applying heuristics to the image.
  • a heuristic is to set a width of a band to be lower at straight areas of an image than at curved areas of an image.
  • the imaged object may be blurred as a result of motion artifacts.
  • such motion artifacts may be co ⁇ ected using the knowledge that the imaged object is a rigid object, making assumptions of the motion vector and shifting the projection data to take the motion vector into account.
  • the motion vector is determined by analyzing the image and/or co ⁇ espondence between sets of projection data from different angles.
  • the direction of the motion may be known, for example as being a result of motion of the CT imager and/or the object while it is being imaged.
  • a motion vector may be determined by comparing projection data from opposite sides of the object.
  • a motion vector may be determined utilizing the estimated image and/or other a-priori knowledge.
  • motion caused by vibration may be co ⁇ ected for by measuring the vibration and estimating the motion blur caused thereby. In some cases, such a co ⁇ ection may take into account the different resonance properties of different parts of the object. It is noted, that such an analysis can usually be performed on a single object and then applied to all the manufactured objects.
  • an object is analyzed to determine vibration modes of its sub-components.
  • the object is stmck and/or otherwise vibrated and one or more images are acquired. Differences in manufacturing between objects (deviations) will often cause a variation in the resonance characteristics of portions of the object. Thus, the acquired image may be different than for a "co ⁇ ect" object.
  • the correct image is preferably used as an estimated image for the tested image.
  • the present invention has been described mainly as using 2D X-ray CT imaging.
  • the above reconstmction techniques may be used for other imaging techniques.
  • the above reconstruction technique may be used to generate cine images of the operation of an object.
  • one type of a- priori knowledge in a manufacture object is the existence of certain objects (sub-components), usually rigid, therein. If the layout of each of these objects is known, even if their exact position is not, the objects may usually be identified from a low resolution reconstructed image. Once the objects are identified, it may be possible to more exactly specify their position and/or orientation in the image, for example using pattern matching, possibly generating strong constraints on the image.
  • the component objects In manufactured objects it is often the case that the component objects (usually sub-components of the manufactured object) are known, even if their exact position is not. Additionally, in an operating machine, (some of) the component elements are in motion, but their exact shape may be known (for example from a previous image). Alternatively or additionally, a cap on the amount of motion of the elements may be known (thereby possibly defining a band size). Alternatively or additionally, the imaging may be synchronized to the motion of the sub-components. In some cases, the periodicity or other regularity of motion of sub-components is determined by analyzing vibration vectors of the object or by Fourier analyzing a sequence of images to determine motion frequencies in certain parts of the object. Alternatively or additionally to cine imaging of an operating machine, a stressed object may be imaged to track its failure modes. In all of the above cases, it is possible to restrict the number of pixels which need to be actually reconstmcted.
  • 3D images of an object may be acquired.
  • One method of acquiring 3D images is helical scanning, described above.
  • a multi-slice image may be acquired, possibly using a multi-row detector and/or by moving the object and/or the imager relative to each other and/or using a cone beam.
  • bands and/or multi-resolution grids defined for a 3D image are 3D.
  • the band portion in one slice may be dependent on the object profile in nearby slices. Such a dependency may also be used when determining band widths and expected e ⁇ ors in a 2D imaging.
  • a 3D image of an object may be acquired by imaging the object from angles which do not all lie in a single plane.
  • an object may be imaged from three perpendicular directions, as well as from intermediate directions, possibly providing hemi-spherical coverage of the object.
  • the imaged object may be specially prepared for imaging, for example by impregnation or immersion in a contrast media, preferably a liquid or a gas. This may be useful when imaging a very transparent or a porous structure.
  • a contrast media preferably a liquid or a gas.
  • only one of a plurality of source materials which are mixed together to form a final material is tagged using a contrast media. Thus, an even dispersion of the source material in the mixture may be determined.
  • some of the above described embodiments may be used for imaging using gamma radiation.
  • emission imaging may also be applied, for example by using a radioactive source material instead of a contrast media.
  • SPECT imaging may be applied, using the above reconstmction techniques.
  • multi-wavelength and/or combined gamma radiation and x-ray radiation may be applied.
  • optical tomography may be performed. Generally however, when the imaged object is made radioactive it cannot be sold. Thus, this type of imaging is usually reserved for test objects and not for production objects.
  • Some embodiments of the present invention are especially suited to industrial imaging, due to the types of limitations, requirements, allowed radiation energies, type of motion, predictability of structure and/or pre-knowledge in that field.
  • some of the above features and/or prefe ⁇ ed embodiments of the invention may also be applied to medical imaging and/or other types of tomographic reconstruction, for example, electron microscopy.

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Abstract

L'invention se rapporte à un procédé de reconstruction d'images de tomographie assistée par ordinateur selon lequel on utilise différentes valeurs associées à au moins un paramètre de reconstruction pour au moins deux parties différentes de l'image. De préférence, ledit (ou lesdits) paramètre(s) de reconstruction possède(nt) la taille des pixels.
PCT/IL1999/000320 1998-10-26 1999-06-14 Tomographie assistee par ordinateur pour inspection non destructive WO2000025268A2 (fr)

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JP2000578781A JP2002528828A (ja) 1998-10-26 1999-06-14 非破壊試験の為のコンピュータ断層撮影法
AU42875/99A AU4287599A (en) 1998-10-26 1999-06-14 Computerized tomography for non-destructive testing
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