DE102013218325A1 - Generation of a measure of the influence of metal artefacts on reconstructed density values - Google Patents

Abstract

The invention relates to the generation of a measure of the influence of metal artefacts on reconstructed density values. The following steps are carried out:
a) generation of a plurality of projections of an object by means of X-ray technology,
b) reconstruction of a tomographic data set from the multiplicity of x-ray images,
c) identification of a metallic region in the tomographic data set by means of segmentation,
d) determination of the shadow areas of the individual projections for the multiplicity of projections,
e) replacement of the recorded projection values in the shadow area by interpolation of values outside the shadow area for the plurality of projections,
f) generating differential projections for the plurality of projections by forming the difference between the recorded projections and the projections with replaced shadow area values, and;
g) reconstruction of a difference volume from the difference projections.

Description

The invention relates to a method, a device and a computer program for generating a measure of the influence of metal artifacts on reconstructed density values.

In the field of X-ray imaging, it is nowadays known, particularly in computed tomography and computed tomography-like methods, to record low-dimensional projection images using different projection directions. From these low-dimensional projection images, a higher-dimensional image data set can be calculated by reconstruction, for which analytical methods, for example filtered backprojection, and iterative (algebraic) methods are known. For example, when using detector lines in computed tomography, so-called sinograms can be recorded, that is to say sets of one-dimensional projection images which are described by the respective recording angle and the position along the line. From these sinograms, a two-dimensional cross-sectional image can be reconstructed. However, it is also known to record two-dimensional projection images with computer tomographs, in order to reconstruct a three-dimensional image data set, thus a volume. The latter is also used in other x-ray systems, e.g. C-arm x-ray devices or tomography devices designed for tomosynthesis performed.

An essential component of these tomographic reconstruction methods is the so-called computed tomography data model (CT data model), which provides the following formal description of the relationship between the target object to be acquired and the acquired projection image data: A projection image data of the projection image data set (after corresponding data preprocessing) thus corresponds to the integral via the object density function (FIG. Object weakening function) along the beam between the X-ray source and the corresponding pixel of the X-ray detector. The reconstruction of the target object from the projection image data then takes place by means of inversion of this model, in particular with the aid of analytical or iterative algorithms.

Problems occur in the reconstruction always when projection image data can not be described by the CT data model, because they are distorted ("contaminated") by scattered radiation, beam hardening or radiation-blocking metal or have been detected in other ways, for example by faulty detector defects. Such image data loaded with data inconsistencies, i. Projection values which are not justifiable with the data model underlying the reconstruction generate artifacts in the tomographic 3D imaging in the image data sets obtained as a result of the reconstruction. A prominent example of such artifacts are metal artifacts. These are artifacts caused by metal objects in the patient, such as screws or prostheses. Metal artifacts are often of low frequency, but have high intensity, leading to a dramatic deviation in the calculated density values. This is due to the fact that metals either completely block or reduce the X-ray radiation applied in the patient so much that the signal-to-noise ratio in the metal shadow area in the projection images becomes unacceptable.

In the two-dimensional projection images recorded, for example, in the context of computed tomography imaging, the image defects caused by metals are still local, that is to say that projection image data in the immediate vicinity of the metal shadows are, in principle, unadulterated. By a tomographic reconstruction, however, these aberrations are distributed over larger areas of the reconstructed volume, ie the image data set. Metal artifacts therefore also contaminate volume areas in the immediate vicinity. For a clinical application, this is a great disadvantage, since, for example, a control of the correct positioning of a surgical implant is only possible inaccurately.

Methods have already been proposed in the prior art for reducing such artefacts based on data inconsistencies. In the following, principal methods of metal artifact correction are outlined.

A first method is based on the segmentation of the metal areas in the projections or in the volume. The metal shadow areas in the projections are removed by replacing these areas with projection values interpolated from the neighborhood. From the thus modified projections, a metal-free reconstruction and thus artifact-reduced reconstruction can be carried out. Finally, the metal areas in volume supplemented with the help of the segmentation information.

These interpolation methods have the disadvantage that while they reduce metal artifacts, they can not completely reconstruct the information lost by metal shadows. This often results in volume data that looks optically artifact-free, but nevertheless reproduces structures incorrectly, without this being made visible to the viewer.

An improved overall data set can be obtained by reconstructing a metal and artifact-reduced volume using the first method and adaptively fusing it to a total data set with an artifact-bound volume obtained by reconstructing the original projections.

Finally, there is the possibility of iterative reconstruction in which either lower level metal shadow projection values influence the overall result, or in which the physical attenuation effects of the metal objects are more accurately modeled during forward projection. Iterative methods, however, are an order of magnitude more computationally expensive than the other alternatives mentioned.

To produce clinically meaningful tomographic images, an effective algorithm for reducing metal artifacts is needed. In addition, it is desirable to obtain information about how much the quality of an image is still affected by metal artifacts.

The invention aims to efficiently generate a measure of the impact of metal artifacts on reconstructed density values.

This object is achieved by a method according to claim 1, an apparatus according to claim 6 and a computer program according to claim 7.

The generation according to the invention of a measure of the influence of metal artefacts on reconstructed density values is based on a multiplicity of projections of an object, which are generated by means of X-ray technology. This can be done for example by means of a computer tomograph, a C-arm or designed for Tomosysthese mammography device.

From the plurality of X-ray images, a tomographic data set, e.g. is reconstructed using a FBP (filtered back projection) and a metallic region in the tomographic data set is identified by segmentation (e.g., thresholding). For each of the plurality of projections, the shadow region of the projection is determined, the shadow region being defined by the fact that X-ray radiation transmitted through it has penetrated the metallic region during the generation of the projection. In the shadow area, the recorded projection values are replaced by interpolation of values outside the shadow area. For the large number of projections, differential projections are generated by forming the difference between the recorded projections and the projections with replaced shadow area values. As a measure of the impact of metal artifacts on reconstructed density values, a difference volume (e.g., by backprojection) is reconstructed from the difference projections. This difference volume represents a confidence mask insofar as it indicates for image values how strong the influence of metal artifacts is.

According to a further development, a difference in scattering volume is generated by eliminating (e.g., zeroing) the values of the difference volume in the metallic region.

In addition, in view of the low-frequency character of typical metal artifacts, low-pass filtering of the scattering difference volume is additionally performed.

According to one embodiment, a metal artefact-reduced image volume is generated simultaneously by the difference between the reconstructed tomographic data set and the scattered difference volume (possibly after low-pass filtering), simultaneously with the measure for the influence of metal artefacts.

In this way, in addition to the image quality improvement, this embodiment additionally, implicitly and thus simultaneously generates a 3D confidence mask for the reconstruction volume in which the degree of algorithmic correction is coded in each volume point.

The reconstructed tomographic data set or the metal artifact-reduced image volume can be superimposed with the floor difference volume in order to give the user an impression of the reliability of the data or information displayed.

The invention also includes an apparatus and a computer program for generating a measure of the impact of metal artifacts on reconstructed density values. The erfindungsgenmäße device includes an X-ray system, and hardware and software comprehensive computing resources.

The invention will be described in more detail below in the context of an embodiment with reference to figures. Show it:

1 : the basic structure of a computer tomography arrangement in a cross-sectional representation,

2 a flowchart of a method according to the invention, and

3 : Images of a phantom generated to illustrate the invention.

1 shows in a schematic way the basic structure of an arrangement for computed tomography in a cross-sectional view using the example of a so-called cone beam geometry commonly used today. In this arrangement, the projection images are two-dimensional, and the course of the X-rays from the X-ray source ( 1 . 11 ) to the detector ( 3 . 13 ) is cone-shaped ( 2 . 12 ). The X-ray source ( 1 . 11 ) is placed on a circular or spiral path around the examination object ( 4 ) moves around, occupying successively different positions, of which in 1 only two ( 1 . 11 ) are shown by way of example. For each position on the track for which a digital projection image is stored, a projection image corresponding to this position, that is to say a two-dimensional field of sensor data, which results in an evaluation computer ( 5 ) is supplied. This evaluation computer, a "computer" that essentially determines the name of the entire process, is used for image reconstruction. It is usually equipped with a display unit ( 6 ) to display the calculated images and often with a memory unit ( 7 ), in which, for example, so-called filters, ie auxiliary quantities for the calculation and similar quantities, can be stored.

If an object to be examined now contains structures that are characterized by a high or very high absorption of X-rays, then a very strong "hardening" (ie an absorption of "soft" X-radiation) or even a complete absorption of the X-radiation often takes place within these structures , Important examples of such situations are massive bone structures or metal objects in the body of a patient, for example metal prostheses in the region of the hip, the knee or the oral cavity. In the case of metals, the reflection (scattering) of the X-ray radiation is very much added to the absorption. These phenomena then lead to artifacts in the reconstructed image, in the case of metal objects to so-called "metal artefacts".

The following describes a computationally efficient and high-quality method for the reduction of metal artefacts in the tomographic image, which implicitly generates a confidence mask during the computation. This mask illustrates the degree of the metal artifact in each pixel, and should be superimposed during visualization on the actual tomographic data set (either before or after the artifact reduction).

The procedure can be divided into the following steps ( 2 ):

Step S1: Reconstruction of a tomographic data set F0 from the original projections P0 with standard reconstruction methods, for example the filtered backprojection method according to the Feldkamp algorithm, i. F0 = FBP {P0}, where FBP represents the reconstruction operator.

Step S2: Segmentation of the metal region in the image volume F0, for example by applying a threshold value method and subsequent refinement of the segmentation boundaries by means of the projection image information P0. Such a refined metal segmentation is a known concept (see, eg DE 10 2007 016 319 A1 ), and here part of the method presented in the embodiment.

Step S3: Replacing the metal shadow areas in the original projections P0 by interpolating values outside the shadow areas, for example by linear interpolation, and thus generating modified metal-free projections P1.

Step S4: Calculate the difference P2 = P0 - P1, whereby projection images P2 arise, in which essentially only the attenuation integrals of the metal objects are mapped.

Step S5: Reconstruction of a volume F2 from the projections P2 (F2 = FBP {P2}), again by means of a standard reconstruction method, and clearing (zeroing) the entries of F2 located in the segmented metal area. The image volume F2 thus contains only the far-reaching artifacts emanating from the metal objects.

Step S6: Low-pass filtering of the image volume F2, either by sequential, layer-by-layer application of a 2D low-pass filter, or by means of a 3D low-pass filter. In particular, an isotropic Gaussian function with standard deviation S (the standard deviation S is a free parameter to be optimized) is suitable as a low-pass filter kernel. In step S6, therefore, the prior knowledge comes in that metal artefacts are of low-frequency nature, and thus correspond to high-frequency components in F2 with a high probability of actual object structure. The result from step S6 now forms the 3D artifact mask, which on the one hand can be used to correct the initial reconstruction F0, but on the other hand simultaneously encodes the local influence of the metal objects on the reconstruction results. The mask thus reflects a confidence value which reflects the distribution of the artifact and thus a reliability of the respective density values.

Step S7: Calculation of a Metal Artifact-Reduced Image Volume F3 = F0-F2, ie by subtracting the artifact image from the original reconstruction.

• • das korrigierte Volumen F3 darzustellen, optional überlagert mit der Konfidenzmaske F2, oder
• • das unkorrigierte Volumen F0 darzustellen, überlagert mit der Konfidenzmaske F2.

For visualization it is suggested to either:

• • represent the corrected volume F3, optionally superimposed with the confidence mask F2, or
• • represent the uncorrected volume F0, superimposed with the confidence mask F2.

In both cases, the overlay can be done either by color channel coding (F2 as a red channel, density values as a grayscale image), or by plotting iso lines, each representing certain confidence thresholds quantifiable by Hounsfield Units (HU uncertainty, 100 HU uncertainty, etc .).

In 3 is an illustration of the above steps using the Shepp-Logan phantom shown. On the left is a reconstruction without metal artefact reduction, ie F0. In the middle, the calculated confidence mask or the artifact image is reproduced, ie the result after step S6. Finally, the right image shows the reconstruction result after using the metal artifact reduction method given in the text, ie F3.

For the final visualization, it is suggested that the confidence mask be the one on the right side of 3 to superimpose, for example Semitransparent.

• 1. Zur Subtraktion der Artefakte in der Erstrekonstruktion, um dort eine signifikante Bildqualitätsverbesserung zu erzielen und
• 2. Zur präzisen Visualisierung der Unsicherheit in den rekonstruierten Dichtewerten, beispielsweise als semitransparente Überlagerung, als Darstellung in einem anderen Farbkanal, oder als Isolinien.

Thus, an efficient algorithm for the reduction of metal artifacts in tomographic images has been proposed, which in an intermediate step calculates a precise artifact image, which can be used in two ways:

• 1. To subtract the artifacts in the Erstrekonstruktion, in order to achieve a significant image quality improvement there and
• 2. For the precise visualization of the uncertainty in the reconstructed density values, for example as a semi-transparent overlay, as a representation in another color channel, or as isolines.

The invention is particularly applicable in medical imaging, but not limited to this scope. For example, artifacts in the course of non-destructive testing of workpieces can be used according to the invention.

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited patent literature

• DE 102007016319 [0031]

Claims (7)

Method for generating a measure of the influence of metal artefacts on reconstructed density values, in which A plurality of projections of an object is generated by means of X-ray technology, - a tomographic data set is reconstructed from the multitude of x-ray images, A metallic area in the tomographic data set is identified by means of segmentation, For each of the plurality of projections, the shadow region of the projection is determined for which the x-radiation transmitted by the object during the generation of the projection has penetrated the metallic region, For the plurality of projections, each in the shadow area, the recorded projection values are replaced by interpolation of values outside the shadow area, For the plurality of projections, differential projections are generated by forming the difference between the recorded projections and the projections with replaced shadow range values; and - A difference volume (by backprojection) is reconstructed from the difference projections. Konfidenzmaske A method according to claim 1, characterized in that - from the difference volume, a scattering difference volume is generated by elimination of the values of the difference volume in the metallic region (zero setting). Method according to one of claims 2, characterized in that a low-pass filtering of the scattering difference volume is performed. A method according to claim 2 or 3, characterized in that a metal artfaktreduziertes image volume by subtraction between the reconstructed tomographic data set and the scattering difference volume (after low-pass) is generated. Method according to one of the preceding claims, characterized in that the reconstructed tomographic data set or the metal artifact-reduced image volume and the Steudifferenzvolumen be superimposed superimposed.  Device for generating a measure of the influence of metal artifacts on reconstructed density values, with An X-ray system for generating a plurality of projections of an object, and - reconstruction software and computing resources for a) reconstruction of a tomographic data set from the plurality of X-ray images, b) identification of a metallic region in the tomographic data set by means of segmentation, c) determination of the shadow areas of the individual projections for the multiplicity of projections, d) replacement of the recorded projection values in the shadow area by interpolation of values outside the shadow area for the plurality of projections, e) generating differential projections for the plurality of projections by forming the difference between the recorded projections and the projections with replaced shadow area values, and; f) reconstruction of a difference volume from the difference projections. A computer program executing a method according to any one of claims 1 to 5 when executed on a computer.

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