Patent Application
20180204647
The invention relates to a method of analyzing a specimen using X-rays, comprising the steps of:
The invention also relates to such a method when performed as part of an X-ray tomographic imaging procedure.
The invention particularly relates to such a method when performed in a Charged Particle Microscope.
In tomographic imaging (also referred to as Computed Tomography (CT)) as referred to above, the source and (diametrically opposed) detector are used to look through the specimen along different lines of sight, so as to acquire penetrative observations of the specimen from a variety of perspectives; these are then used as input to a mathematical procedure that produces a reconstructed “volume image” of (part of) the (interior of) the specimen. In order to achieve a series of different lines of sight as alluded to here, one can, for example, choose to:
As regards the mathematical reconstruction technique used to produce a tomogram from a series of input images, use can be made of algorithms such as SIRT (Simultaneous Iterative Reconstruction Technique), ART (Algebraic Reconstruction Technique), DART (Discrete ART), SART (Simultaneous ART), MGIR (Multi-Grid Iterative Reconstruction), and many others: see, for example, the summary presented in the following publication:
Tomographic imaging as referred to here can be performed using a standalone apparatus, which is conventionally the case in medical imaging applications, for example, where the specimen (e.g. a human or animal) is macroscopic. Standalone CT tools are also available for performing so-called “micro CT”, in which a micro-focused source is used to image microscopic specimens, e.g. in geology/petrology, biological tissue studies, etc. Continuing this drive toward ever-greater resolution, so-called “nano CT” instruments have also been developed; these may be standalone tools, but, for example, they may also be embodied as (add-on) modules for (a vacant vacuum/interface port of) a charged-particle microscope (CPM), in which case the CPM's charged-particle beam is used to irradiate a (block-like) metal target, causing production of the X-rays used to perform the desired tomography. More information on (some) of these topics can, for example, be gleaned from the following references:
Although prior-art X-ray imaging techniques have produced agreeable results up to now, the current inventors have worked extensively to provide an innovative improvement to conventional approaches. The results of this endeavor are the subject of the current invention.
It is an object of the invention to provide an augmented method as set forth in the opening paragraph above. In particular, it is an object of the invention that such a method should have useful extra functionalities compared to prior-art techniques. Moreover, it is an object of the invention that such a method should lend itself to performing improved X-ray tomographic imaging.
These and other objects are achieved in a method as set forth in the opening paragraph above, which method is characterized in that it comprises the following steps:
The current invention exploits inter alia the following insights:
S˜E No η (1)
whereby:
S˜E N (1a)
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2
S˜E2N (2)
E=f (S, σ2S) (3)
N=g (S2, σ2S) (4)
where f and g are functions. For example:
E˜σ2S/S (3a)
N˜S2/σ2S (4a)
Since these values are calculated per pixel, a plot of their values per pixel respectively renders an E-map (and an N-map) of the specimen, as well as a mean intensity map (comprising conventional X-ray projection data).
The mean intensity depends on the path length travelled through a particular material, but also upon the composition of that material (the X-ray attenuation coefficient). From this data alone, it is not possible to distinguish geometrical information from compositional information (e.g. regions comprising different contaminants and/or dopants, different densities, general transitions from one material to another, etc.). The X-ray attenuation coefficient, in turn, typically depends nonlinearly on the X-ray photon energy. The present invention provides a means to separate two different types of information from the same imagery, rather than having to put up with some forced hybrid/mix of the two. The ability to distill out the E-map becomes particularly interesting, because it allows the extraction of rudimentary compositional information without having to resort to (for example) X-ray spectroscopy (EDS).
Needless to say, the accuracy of the procedure set forth in the previous paragraph will depend inter alia on the number M of accumulated images Ij, i.e. on the cardinality M of the set {Ij}. This is a matter of choice, and the skilled artisan will be able to decide how to manage the tradeoff between greater accuracy (in the calculated values of S and σS) on the one hand, and the competing issue of throughput on the other hand. Another issue that may need to be considered is the total radiation dose delivered to the specimen, and an acceptable upper limit in this regard may place a restriction on the value of M. However, the inventors have observed that, because the current invention is predominantly interested in the differences between images in the set {Ij} more than on the direct content of the images Ij themselves, there are situations in which it is possible to lower the dose per image so as to (partially) compensate for the multiplicity of images acquired thus decreasing the cumulative radiation dose for the whole image set {Ij}. Moreover, in calculating values of S and σ2S from analysis of the set {Sij}, intelligent algorithms can be used to produce more refined values of S and σ2S from smaller data sets, e.g. by extrapolating/interpolating data from a cluster of ancillary (neighboring) pixels adjacent to any given subject pixel; in this way, one can suffice with a smaller value of M to start off with. By way of non-limiting example, provided for general guidance purposes only, the inventors have achieved satisfactory results with the current invention using a value of M in the range 50-300, for instance.
In an advantageous embodiment, the inventive method is performed as part of an X-ray tomographic imaging procedure. As set forth above, tomographic reconstruction synthesizes a series of two-dimensional (2D) images taken along different lines of sight into a three-dimensional (3D) image. The present invention can be applied for each of (a selected subset of) these individual lines of sight LK, whereby the value of the abovementioned cardinality M may be the same or different for different lines of sight. Since the process of tomographic reconstruction is effectively a process of mathematical deconvolution/disentanglement, and since the invention already produces a certain information disentanglement along each line of sight (the aforementioned separation of E (compositional) and N (topographical) information), use of the invention to input statistically processed 2D images into a 3D tomographic reconstruction will expedite/enrich the reconstruction process. In particular, use of the invention in conjunction with a tomographic imaging procedure allows the following:
(I) Beam Hardening correction:
Beam Hardening (BH) is a phenomenon whereby, in a polychromatic beam of X-rays, lower-energy photons tend to be selectively “filtered” from the beam when it passes through a material, thereby altering the energy distribution in the beam. This selective removal of photons is due to various (energy-specific) interactions in the material—such as the photoelectric effect and Compton scattering—and generally involves some form of interplay between the photons in the beam and one or more of atoms, ions, phonons and plasmons in the material. This phenomenon tends to produce unwanted visual artifacts in the reconstructed tomogram, particularly along interfaces between structures/materials in the imaged specimen. The current invention can address this issue by using the above-mentioned E-map as a basis for estimating the energy shift per ray direction through the specimen. See, for example, Embodiment 2 below.
(II) Determination/estimation of at least one of the specimen's material density (p) and atomic number (Z) per voxel.
As is set forth in more detail in Embodiment 2 below, for example, Beam Hardening effects are closely associated with p and Z. Using an attenuation model in conjunction with the present invention, it is possible to derive values for p and/or Z.
It should be noted that, according to the current invention, there are different manners in which the set {Ij} can be produced. For example:
(i) In one approach, the set {Ij} is produced by iteratively repeating (with a total of M iterations) a procedure in which an entire nth image In is captured before proceeding to capture an entire (n+1)th image In+1. In this case, the set {Ij} is basically a stack of M individual pre-assembled images Ij.
Such an approach can, for example, be enacted using detection scheme (a) or (b) below.
(ii) In an alternative approach, the set {Ij} is produced by iteratively repeating a procedure in which, at an nth pixel position, a plurality M of different detector samples is collected before proceeding to an (n+1)th pixel position. In this case, the set {Ij} is basically assembled by juxtaposing pixel stacks on a two-dimensional floor area—somewhat like mini-skyscrapers with M floors arranged side-by-side; a given image In is then the cumulative floor area made up of all nth floors of the individual skyscrapers in question. So, in this case, individual images Ij can be regarded as post-assembled rather than pre-assembled. In this scenario, whether or not one actually takes the trouble to explicitly “resolve” (assemble) individual images Ij out of the “unresolved” set {Ij} is not of particular relevance to the current invention: the main object is to accrue the dataset {Sij} and the associated values of S and σ2S via the set {Ij} (resolved or not). Such an approach can, for example, be enacted using detection scheme (a) below.
(iii) If desired, one can conceive various hybrids/mixes of approaches (i) and (ii).
As regards the detection schemes alluded to above, the following possibilities can be considered:
(a) Scan-based detection, whereby:
In this scenario, a narrow beam of input X-rays irradiates only a small region of the specimen at any given time, and the employed detector intercepts (a portion of) the flux of X-rays emerging from the irradiated region in question, so as to create a component image sub-section. This process is repeated at successive regions on the specimen (following a scan path), and a full image can then be assembled by “tiling” the obtained component image sub-sections together. An analogous procedure is commonly employed in a SEM, for example.
(b) “Full-field” detection. Here, the full specimen (or a relatively large area thereof) is irradiated using a relatively broad beam of input X-rays, and a pixelated detector (such as a CCD or CMOS array, for example) is used to capture X-rays emanating from the whole irradiated zone on the specimen, leading to outright formation of a two-dimensional image. An analogous procedure is commonly employed in a TEM, for example.
(c) If desired, one can conceive various hybrids/mixes of schemes (a) and (b).
The skilled artisan will be able to grasp these points and choose for himself the manner in which he wishes to accumulate the set {Sij} via the set {Ij}.
The invention will now be elucidated in more detail on the basis of exemplary embodiments and the accompanying schematic drawings, in which:
In the Figures, where pertinent, corresponding parts may be indicated using corresponding reference symbols. It should be noted that, in general, the Figures are not to scale.
The particle-optical column 3 comprises an electron source 17 (such as a Schottky emitter), (electrostatic/magnetic) lenses 19, 21 (in general, more complex in structure than the schematic depiction here) to focus the electron beam 5 onto the specimen 13, and a deflection unit 23 to perform beam deflection/scanning of the beam 5. When the beam 5 impinges on/is scanned across the specimen 13, it will precipitate emission of various types of “stimulated” radiation, such as backscattered electrons, secondary electrons, X-rays and cathodoluminescence (infra-red, visible and/or ultra-violet photons); one or more of these radiation types can then be sensed/recorded using one or more detectors, which may form an image, spectrum, diffractogram, etc., typically by assembling a “map” (or “matrix”) of detector output as a function of scan position on the specimen. The present Figure shows two such detectors, 25, 27, which may, for example, be embodied as follows:
The microscope 1 further comprises a controller/computer processing unit 31 for controlling inter alia the lenses 19 and 21, the deflection unit 23, and detectors 25, 27, and displaying information gathered from the detectors 25, 27 on a display unit 33 (such as a flat panel display); such control occurs via control lines (buses) 31′. The controller 31 (or another controller) can additionally be used to perform various mathematical processing, such as combining, integrating, subtracting, false colouring, edge enhancing, and other processing known to the skilled artisan. In addition, automated recognition processes (e.g. as used for particle analysis) may be included in such processing.
Also depicted is a vacuum port 7′, which may be opened so as to introduce/remove items (components, specimens) to/from the interior of vacuum chamber 7, or onto which, for example, an ancillary device/module may be mounted (not depicted). A microscope 1 may comprise a plurality of such ports 7′, if desired.
In the context of performing X-ray tomography, the microscope 1 can also comprise an in situ CT module 7″ as shown in
In the specific context of the current invention, the controller 31 and/or a dedicated separate processing unit (not shown) can be used to perform the following actions:
It should be noted that many refinements and alternatives of such a set-up will be known to the skilled artisan, including, but not limited to:
An example will now be given as to how the present invention can be used to perform a correction for Beam Hardening effects:
(A) Perform an averaging operation on the set {Ij} of pixeled images to produce a “mean image”.
(B) Compile an E-map as set forth above.
(C) From the mean image resulting from step (A), construct a “normal” tomogram. This tomogram will be subject to Beam Hardening effects.
(D) From the E-map resulting from step (B), construct an “energy-shift” tomogram TΔ. This tomogram effectively indicates how much the mean energy will shift per ray going through a particular point.
(E) Compute a mean X-ray energy Em along a given ray direction (s) by calculating an integral using source mean energy Eo as an initial condition:
E
m
=E
o
+∫T
66
ds
(F) A generally accepted model for attenuation (p) is the Alvarez model:
μ(E)˜K1ρZ3/E3+K2ρ (5)
in which:
μ(Em)=μ(E) (Em/Eo)3.
This allows construction of a set of simulated projections in which, for each ray direction, one uses an X-ray attenuation map initially reconstructed from measured projection data, but subsequently corrected using the inventive E-map along the ray direction in question. These simulated projections can then be used in a normal tomographic reconstruction to obtain a tomogram with greatly reduced (ideally zero) Beam Hardening effects.
(G) If desired, at least one of Z and p can be derived from expression (5), using values of K1, K2 obtained from a calibration series and/or tabulated references.
As an alternative to the approach set forth in Embodiment 2, one can consider the inventive E-map as an energy-weighted spectrum (more strictly: σ2S can be regarded as an energy-weighted image, with E˜σ2SS). In conjunction with one of the images in the set {Ij} (or a “mean image” as referred to in (A) above), one now has two inputs into a dual-energy reconstruction algorithm. In this case, the E-map is effectively a normal image that has been skewed to higher energies. Some additional information on dual-energy reconstruction algorithms can, for example, be gleaned from the following sources:
| Number | Date | Country | Kind |
|---|---|---|---|
| 17151666.9 | Jan 2017 | EP | regional |
