METHOD OF METAL ARTEFACT REDUCTION IN X-RAY DENTAL VOLUME TOMOGRAPHY

Abstract

The present invention relates to a method of metal artefact reduction in x-ray dental volume tomography, the method comprising: a step (S1) of obtaining two-dimensional x-ray images (1) or a sinogram (2) of at least part (v) of a patient jaw (3a), acquired through relatively rotating an x-ray source (4) and a detector (5) around the patient jaw (3a); the method being characterized by further comprising: a step (S2) of detecting metal objects (6) in the two-dimensional x-ray images (1) or the sinogram (2) by using at least a trained artificial intelligence algorithm to generate 2D masks (7) which represent the metal objects (6) in the two-dimensional x-ray images (1) or 3D masks which represent the metal objects (6) in the sinogram (2), respectively; and a step (S4; S5) of reconstructing a three dimensional tomographic image (8) respectively based on two-dimensional x-ray images (1) or the sinogram (2) and the 2D masks (7) or the 3D masks as generated.

Claims

1. A method of metal artefact reduction in x-ray dental volume tomography, the method comprising: a step (S1) of obtaining two-dimensional x-ray images (1) or a sinogram (2) of at least part (V) of a patient jaw (3a), acquired through relatively rotating an x-ray source (4) and a detector (5) around the patient jaw (3a); the method being characterized by further comprising: a step (82) of detecting metal objects (6) in the two-dimensional x-ray images (1) or the sinogram (2) by using at least a trained artificial intelligence algorithm to generate 2D masks (7) which represent the metal objects (6) in the two-dimensional x-ray images (1) or 3D masks which represent the metal objects (6) in the sinogram (2), respectively; and a step (84 S5) of reconstructing a three dimensional tomographic image (8) respectively based on the two-dimensional x-ray images (1) or the sinogram (2) and 2D masks (7) or the 3D masks as generated.

2. The method according to claim 1, characterized in that in the reconstruction step (S5), the three-dimensional tomographic image (8) is reconstructed respectively based on the two-dimensional x-ray images (1) or the sinogram (2) as originally obtained and the 2D masks (7) or the 3D masks as generated.

3. The method according to claim 1, characterized by further comprising: a step (S3) of correcting the two-dimensional x-ray images (1) or the sinogram (2) at least by means of the generated 2D masks (7) or the 3D masks respectively, and wherein in the reconstruction step (S4), the three dimensional tomographic image (8) is reconstructed based n the corrected two-dimensional x-ray images (1) or the corrected sinogram (2).

4. The method according to claim 3, characterized in that in the correction step (S3), the two-dimensional x-ray images (1) or the sinogram (2) are corrected through classical image processing.

5. The method according to claim 3, characterized in that in the correction step (S3), the two-dimensional x-ray images (1) or the sinogram (2) are corrected through another trained artificial intelligence algorithm.

6. The method according to claim 3, characterized in that in the reconstruction step (S4), the reconstruction of the three-dimensional tomographic image (8) is further based on the corrected and the uncorrected two-dimensional x-ray images (1) or the corrected and the uncorrected sinogram (2).

7. The method according to claim 1, characterized by further comprising: a step of training the artificial intelligence algorithm by using data pairs, wherein each data pair includes a two-dimensional x-ray image (1′) and an associated 2D mask (7′) which represents the location of any metal object (6′) in the associated two-dimensional image (1′). or wherein each data pair includes a sinogram (2′) and an associated 1) mask which represents the location of any metal object (6′) in the sinogram (2′), wherein the two-dimensional x-ray images (1′) and the sinogram (2′) of the data pairs correspond to parts of or the entire patient jaws (3a) of one or more patients (3′ which have been generated through an x-ray source (4′) and a detector (5′) or through simulation techniques and cover a plurality of viewing angles, wherein at least one of the data pairs comprises at least one metal object (6).

8. The method according to claim 7, characterized in that the 2D masks (7′) or the 3D mask used in the training steps are generated by analyzing a three-dimensional tomographic image (8′) in which metal artifacts have not been corrected.

9. The method according to claim 1, characterized in that the two-dimensional x-ray images (1) or the sinogram (2) obtained corresponds to a part (V) of the patient jaw (3a) which is flee of metal objects (6) or includes at least one metal object (6b), and wherein the remaining part of the patient jaw (3a) includes at least one metal object (6a).

10. An x-ray dental volume tomography system comprising: an x-ray unit (9) comprising an acquisition means adapted to acquire two-dimensional x-ray images (1) or a sinogram (2) of at least part (V) of a patient jaw (3a) through relatively rotating an x-ray source (4) and a detector (5) completely around the patient jaw (3a); and tomographic reconstruction unit comprising an image processing means adapted to detect metal objects (6) in the two-dimensional x-ray images (1) or the sinogram (2) acquired by the acquisition means, the system being characterized in that the image processing means is further adapted to detect the metal objects (6) by using a trained artificial intelligence algorithm which generates 2D masks (7) that represent the metal objects (6) in the two-dimensional x-ray images (1) or 3D masks that represent the metal objects (6) the sinogram (2) respectively, and to reconstruct a three dimensional tomographic image (8) respectively based on two-dimensional x-ray images (1) or the sinogram (2) and the 2D masks (7) or the 3D masks as generated.

11. The system according to claim 10, characterized in that an image processing means is further adapted to reconstruct the three-dimensional tomographic image (8) based on the two-dimensional x-ray images (1) or the sinogram (2) as originally obtained and the 2D masks (7) or the 3D masks as generated.

12. The system according to claim 10, characterized in that an image processing means is further adapted to correct the two-dimensional x-ray images (1) or the sinogram (2) by means of the generated 2D masks (7) or the 3D masks respectively, and to reconstruct the three-dimensional tomographic image (8) based on the corrected two-dimensional x-ray images (1) or the corrected sinogram (2).

13. The system according to claim 12, characterized in that the image processing means is further adapted to correct the two-dimensional x-ray images (1) or the sinogram (2), through classical image processing.

14. The system according to claim 12, characterized in that the image processing means is further adapted to correct the two-dimensional x-ray images (1) or the sinogram (2) through another trained artificial intelligence algorithm.

15. The system according to claim 12, characterized in that the image processing means is further adapted to reconstruct the three-dimensional tomographic image (8) based on the corrected and the uncorrected two-dimensional x-ray images (1) or the corrected and the uncorrected sinogram (2).

16. The system according to claim 10, characterized in that tomographic reconstruction unit has an input means for retrieving the trained artificial intelligence algorithm.

17. The system according to claim 10, characterized in that the acquisition means is adapted to user-selectably and adjustably acquire the two-dimensional x-ray images (1) or the sinogram (2) that corresponds to one of a plurality of different user-selectable and adjustable parts of the patient jaw (3a) which have different volumes.

18. A computer-readable program comprising codes for causing a computer-based x-ray dental volume tomography system to perform the method according to any one of claims 1 to 9.

19. A computer-readable storage which stores the computer-readable program according to claim 18.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In the subsequent description, the present invention will be described in more detail by using exemplary embodiments and by referring to the drawings, wherein

[0018] FIG. 1—is a tomogram with metal artefacts according to the prior art;

[0019] FIG. 2—is a tomogram corresponding to the same section in FIG. 1 reconstructed with the metal artefact reduction method according to the prior art as shown in FIG. 3;

[0020] FIG. 3—is a flowchart showing the steps of a metal artefact reduction method according to the prior art;

[0021] FIG. 4—is a two-dimensional x-ray image acquired with an x-ray unit according to the present invention;

[0022] FIG. 5a—is a sinogram of two-dimensional x-ray images acquired with the x-ray unit according to the present invention;

[0023] FIG. 5b—is a cross sectional view of the sinogram in FIG. 5a, when viewed along the vertical direction;

[0024] FIG. 6—is a schematic cross sectional top view of the x-ray unit of the x-ray dental volume tomography system according to the present invention;

[0025] FIG. 7—is a tomogram according to the prior art with metal artefacts that are caused by metal objects outside the reconstructed volume;

[0026] FIG. 8—is a tomogram corresponding to the same section in FIG. 7 reconstructed with a metal artefact reduction method according to the prior art as shown in FIG. 3;

[0027] FIG. 9a—is a flowchart showing the steps of a metal artefact reduction method according to a first alternative embodiment of the present invention;

[0028] FIG. 9b—is a flowchart showing the steps of a metal artefact reduction method according to a second alternative embodiment of the present invention;

[0029] FIG. 10—illustrates a data pair used for training the artificial intelligence algorithm for generating masks according to the present invention, wherein the data pair includes a two-dimensional x-ray image and an associated 2D mask;

[0030] FIG. 11—is a tomogram corresponding to the same section in FIG. 7, reconstructed with the metal artefact reduction method according to the present invention.

[0031] The reference numbers shown in the drawings denote the elements as listed below and will be referred to in the subsequent description of the exemplary embodiments. [0032] 1. 2D x-ray image [0033] 2. Sinogram [0034] 3. Patient [0035] 3a. Patient jaw [0036] 4. X-ray source [0037] 5. Detector [0038] 6. Metal object [0039] 6a. Metal object (outside a small volume (v)) [0040] 6b. Metal object (inside the small volume (v)) [0041] 7. 2D mask [0042] 8. 3D tomographic image [0043] 9. X-ray unit

[0044] V: Part of the patient jaw (3a) or a small volume

[0045] FIG. 6 shows an x-ray unit (9) according to an embodiment of the present invention. The x-ray unit (9) has an acquisition means for acquiring two-dimensional x-ray images (1) or a sinogram (2) of at least part (V) of a patient jaw (3a) i.e., a small volume, through relatively rotating an x-ray source (4) and a detector (5) around the patient jaw (3a). The x-ray unit (9) is connectable to a tomographic reconstruction unit which has an image processing means which is adapted to execute the method of metal artefact reduction in x-ray dental volume tomography according to the present invention. The x-ray unit (9) and the tomographic reconstruction unit together configure an x-ray dental volume tomography system according to the present invention.

[0046] FIG. 9a shows the steps (S1,S2,S3,S4) of the method of metal artefact reduction in x-ray dental volume tomography according to a first alternative embodiment of the present invention.

[0047] In a first step (S1), two-dimensional x-ray images (1) or a sinogram (2) of at least part (V) of a patient jaw (3a) is obtained from the acquisition means of the x-ray unit (9). In the second step (S2), the metal objects (6) in the two-dimensional x-ray images (1) or the sinogram (2) are detected by using a trained artificial intelligence algorithm which generates either 2D masks (7) that represent the metal objects (6) in the two-dimensional x-ray images (1) or 3D masks which represent the metal objects (6) in the sinogram (2), respectively. In addition, other 2D masks or 3D masks obtained from a classical algorithm can be optionally used in combination to those generated through the trained artificial intelligence algorithm to optimize the detection. In a third step (S3), the two-dimensional x-ray images (1) or the sinogram (2) are corrected at least by means of the generated 2D masks (7) or the 3D masks respectively. This correction may be performed through classical image processing. Alternatively, the correction may be performed through another trained artificial intelligence algorithm. In step (S4), a three-dimensional tomographic image (8) based on the corrected two-dimensional x-ray images (1) or the corrected sinogram (2) is reconstructed. Alternatively, the reconstruction of the three-dimensional tomographic image (8) may be based on the corrected and the uncorrected two-dimensional x-ray images (1) or the corrected and the uncorrected sinogram (2) so that the metal objects (6), if any, are also shown in the three-dimensional tomographic image (8).

[0048] FIG. 9b shows the steps (S1,S2,S5) of the method of metal artefact reduction in x-ray dental volume tomography according to a second alternative embodiment of the present invention. In the second alternative embodiment, the third step (S3) is dispensed with. And the three-dimensional tomographic image (8) is reconstructed respectively based on the two-dimensional x-ray images (1) or the sinogram (2) as originally obtained and the 2D masks (7) or the 3D masks as generated.

[0049] For comparison, FIG. 7 shows a tomogram according to the prior art with metal artefacts that are caused by metal objects (6a) outside the reconstructed small volume (V). FIG. 11 shows a tomogram corresponding to the same section in FIG. 7, reconstructed with the metal artefact reduction method according to the present invention.

[0050] The artificial intelligence algorithm is trained by using data pairs. In the subsequent description, the reference signs having a prime denote the elements similar to those without the prime, but which have been used in the training step of the method. Each data pair includes a two-dimensional x-ray image (1′) and an associated 2D mask (7′) which represents the 2D location of any metal object (6′) in the associated two-dimensional image (1′). Alternatively, each data pair includes a sinogram (2′) and an associated 3D mask which represents the 3D location of any metal object (6′) in the sinogram (2′). Herein the two-dimensional x-ray images (1′) and the sinogram (2′) of the data pairs correspond, preferably to the entire patient jaw (3a′) of an arbitrary patient (3′), which have been generated through an x-ray source (4′) and a detector (5′) through a relative revolution preferably completely around the patient jaw (3a′). The 2D masks (7′) and the 3D mask used in the training step may be obtained by analyzing a three-dimensional tomographic image (8′) in which the metal artifacts have not be corrected. For instance, the 2D masks (7′) and/or the 3D masks used for the training can be obtained from the prior art method in FIG. 3 as a by-product which result from the analyzing step. Alternatively, the 2D masks (7′) and/or the 3D masks used in the training step may be manually generated from two-dimensional images (1′) and/or sinograms (2′) in which metal artefacts have not be corrected.

[0051] In another embodiment, the tomographic reconstruction unit has an input means for retrieving the trained artificial intelligence algorithm for generating the 2D masks (7) and the 3D masks. The input means may be a wireless connection or wired connections that can be connected to a network or the like for data retrieval.

[0052] In another embodiment, the acquisition means is adapted to user-selectively acquire two-dimensional x-ray images (1) or the sinogram (2) that corresponds to one of a plurality of different parts (V) of the patient jaw (3a) which have respectively different volumes. The user can select the parts (V) to be irradiated and adjust the size thereof.

[0053] In another embodiment, the method is provided in form of a computer-readable program which has codes for causing a computer-based x-ray dental volume tomography system to perform the above-described steps of the metal artefact reduction method in x-ray dental volume tomography.

[0054] In another embodiment, the computer-readable program is stored on a computer-readable storage.