CN107730455B - Method and device for obtaining MAR image - Google Patents
Method and device for obtaining MAR image Download PDFInfo
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- CN107730455B CN107730455B CN201610655886.2A CN201610655886A CN107730455B CN 107730455 B CN107730455 B CN 107730455B CN 201610655886 A CN201610655886 A CN 201610655886A CN 107730455 B CN107730455 B CN 107730455B
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 239000002184 metal Substances 0.000 claims abstract description 111
- 238000012545 processing Methods 0.000 claims abstract description 57
- 238000012937 correction Methods 0.000 claims abstract description 14
- 238000000354 decomposition reaction Methods 0.000 claims description 8
- 238000010606 normalization Methods 0.000 claims description 8
- 230000003044 adaptive effect Effects 0.000 claims description 7
- 230000009467 reduction Effects 0.000 claims description 7
- 230000011218 segmentation Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 abstract description 6
- 238000002591 computed tomography Methods 0.000 description 10
- 239000000463 material Substances 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 101000911772 Homo sapiens Hsc70-interacting protein Proteins 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 210000004394 hip joint Anatomy 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910052755 nonmetal Inorganic materials 0.000 description 1
- 238000005510 radiation hardening Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
Abstract
The present invention relates to a method of obtaining a metal artifact reduced image (MAR image) and a corresponding apparatus. The method comprises the following steps: back projecting the original sinusoidal curve to obtain an original image; obtaining a metal mask from the original image, and obtaining a metal track based on the metal mask; performing metal-ray-hardening correction (BHC) on the original image to obtain a metal BHC image; obtaining a Projection Complete (PC) image from the metal track; performing operation processing on the metal BHC image and the PC image to obtain a priori image; forward projecting the prior image to obtain a prior sinusoidal curve; and processing the prior sinusoid to finally obtain a MAR image. By the method and the device, streak artifacts caused by high-density substances can be reduced in the finally obtained MAR image.
Description
Technical Field
The present invention relates to the field of image processing, and in particular to a method and apparatus for obtaining Metal Artifact Reduction (MAR) images.
Background
Metal artifacts are the general term used in the measurement arts to describe artifacts that result from radiation hardening, scattering, noise, and nonlinear partial volume effects caused by high density objects. Metal artifacts appear as thin banding artifacts as well as bright-dark banding artifacts, which severely undermine image quality in Computed Tomography (CT) images and thus often reduce the diagnostic value of these CT images. There are many MAR techniques that treat all of the metal-affected parts of the original data as unreliable data and thus replace them. While these sinusoidal repair methods have generally been successful in eliminating metal artifacts, they also reduce the resolution of the corrected image, in particular, they fail to accurately recover the replaced true values, and thus the corrected image will appear blurred. For example, in a Normalized MAR (NMAR) that replaces corrupted original data while suppressing most of the new artifacts, it still uses a completely replacement scheme, which results in a loss of data.
To balance the artifact and resolution, a hybrid approach is often used. Specifically, adaptive Normalized Metal Artifact Reduction (ANMAR), an algorithm based on raw data, is introduced that combines raw projections with NMAR or MAR projections. In other words, to increase the resolution of the MAR image, an original image that did not pass through the MAR is introduced. At the same time, however, metal artifacts in the original image are also introduced into the final MAR image. FIG. 1 is a diagram showing MAR images obtained by the conventional blending method as described above, reflecting that after MAR of an original CT image (e.g., obtained by CT scanning of a human hip joint), white streak-like artifacts caused by high density material in the original CT image remain.
Disclosure of Invention
It is an aim of exemplary embodiments of the present invention to overcome the above and/or other problems of the prior art. Accordingly, exemplary embodiments of the present invention provide a method and apparatus for obtaining a MAR image capable of reducing banding artifacts caused by high density substances in the MAR image.
According to an exemplary embodiment, a method of obtaining a MAR image comprises the steps of: back projecting the original sinusoidal curve to obtain an original image; obtaining a metal mask from the original image, and obtaining a metal track based on the metal mask; performing metal-ray-hardening correction (BHC) on the original image to obtain a metal BHC image; obtaining a Projection Complete (PC) image from the metal track; performing operation processing on the metal BHC image and the PC image to obtain a priori image; forward projecting the prior image to obtain a prior sinusoidal curve; and processing the prior sinusoid to finally obtain a MAR image.
According to another exemplary embodiment, an apparatus for obtaining a MAR image includes: the original image acquisition module is used for carrying out back projection on the original sinusoidal curve to acquire an original image; the metal track acquisition module is used for acquiring a metal mask from the original image and acquiring a metal track based on the metal mask; the metal BHC module is used for carrying out metal BHC on the original image to obtain a metal BHC image; the PC image acquisition module is used for acquiring a PC image from the metal track; the operation processing module is used for carrying out operation processing on the metal BHC image and the PC image to obtain a priori image; the prior sinusoidal curve acquisition module is used for forward projecting the prior image to obtain a prior sinusoidal curve; and the prior sinusoidal curve processing module is used for processing the prior sinusoidal curve to finally obtain the MAR image.
Other features and aspects will become apparent from the following detailed description, the accompanying drawings, and the claims.
Drawings
The invention may be better understood by describing exemplary embodiments thereof in conjunction with the accompanying drawings, in which:
FIG. 1 shows MAR images obtained by the prior art;
FIG. 2 is a flowchart of a method for obtaining MAR images according to an exemplary embodiment of the present invention;
FIG. 3 is a flowchart illustrating one embodiment of the steps for deriving a PC image from the metal track in a method for obtaining a MAR image in accordance with an exemplary embodiment of the present invention;
FIG. 4 is a flowchart illustrating one embodiment of the steps of performing an arithmetic process on the metallic BHC image and the PC image to obtain a prior image in a method for obtaining a MAR image in accordance with an exemplary embodiment of the present invention;
FIG. 5 is a flowchart of one embodiment of the steps in a method for obtaining a MAR image for processing the a priori sinusoids to ultimately obtain a MAR image in accordance with an exemplary embodiment of the present invention;
FIG. 6 is a flowchart of another embodiment of the steps of processing the a priori sinusoids to ultimately obtain a MAR image in a method for obtaining a MAR image in accordance with an exemplary embodiment of the present invention;
FIG. 7 is a schematic block diagram of an apparatus for obtaining MAR images in accordance with an exemplary embodiment of the present invention;
FIG. 8 illustrates a comparison result between a conventional MAR image acquisition method and a current MAR image acquisition method according to an exemplary embodiment of the present invention;
fig. 9 shows an image obtained without passing through a MAR, an image obtained with a MAR of a conventional art, and an image obtained with a current MAR according to an exemplary embodiment of the present invention, respectively.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
In the following, specific embodiments of the present invention will be described, and it should be noted that in the course of the detailed description of these embodiments, it is not possible in the present specification to describe all features of an actual embodiment in detail for the sake of brevity. It should be appreciated that in the actual implementation of any of the implementations, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Unless defined otherwise, technical or scientific terms used in the claims and specification should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like in the description and in the claims, are not used for any order, quantity, or importance, but are used for distinguishing between different elements. The terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. The word "comprising" or "comprises", and the like, is intended to mean that elements or items that are immediately preceding the word "comprising" or "comprising", are included in the word "comprising" or "comprising", and equivalents thereof, without excluding other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, nor to direct or indirect connections.
According to an embodiment of the present invention, a method of obtaining a MAR image is provided.
Referring to FIG. 2, FIG. 2 is a flowchart of a method 200 for obtaining MAR images according to an exemplary embodiment of the present invention. The method 200 may include the following steps 210 to 270.
As shown in fig. 2, in step 210, the original sinusoid is backprojected to obtain the original image. The original sinusoid is the sinusoid obtained by a CT scan of the scanned object. The scan object may particularly be a part of a human body, such as a human hip joint.
In step 220, a metal mask is obtained from the original image and a metal track is obtained based on the metal mask. Specifically, the metal mask is obtained by threshold extraction of the original image. And then, carrying out forward projection on the metal mask to obtain the metal track.
In step 230, a metal-ray-hardening correction (BHC) is performed on the original image to obtain a metal BHC image. By this step, metal ray hardening artifacts in the original image are reduced.
In step 240, a Projection Complete (PC) image is obtained from the metal track obtained in step 220.
In one embodiment of the present invention, referring to fig. 3, step 240 may further include the following sub-steps 241 to 242.
In sub-step 241, a PC sinusoid is first obtained from the metal track obtained in step 220.
Next, in sub-step 242, the obtained PC sinusoid is backprojected to obtain the PC image.
Returning to fig. 2, next in step 250, the metal BHC image obtained in step 230 and the PC image obtained in step 240 are subjected to arithmetic processing to obtain a priori images.
In one embodiment of the present invention, referring to fig. 4, step 250 may further include the following sub-steps 251 to 255. In sub-step 251, the metal BHC image obtained in step 230 is subjected to a total variation process and a gaussian frequency decomposition process to obtain a first high frequency component and a first low frequency component.
In sub-step 252, the PC image obtained in step 240 is subjected to gaussian frequency decomposition processing to obtain a second high frequency component and a second low frequency component.
In sub-step 253, an intermediate variable is derived based on the first high frequency component, the first low frequency component, the second high frequency component, and the second low frequency component.
In sub-step 254, the PC image and the intermediate variable are separately segmented to obtain a first image and a second image.
In sub-step 255, the first image is corrected and the second image is applied to the corrected first image to obtain the prior image. With continued reference to fig. 2, next, in step 260, the prior image is forward projected to obtain a prior sinusoid.
The a priori sinusoids are then processed to ultimately obtain a MAR image in step 270.
In one embodiment of the present invention, referring to fig. 5, step 270 may further include the following sub-steps 2711 to 2712.
In sub-step 2711, the a priori sinusoids obtained in step 260 are interpolated and denormalized to obtain Adaptive Normalized MAR (ANMAR) sinusoids.
In sub-step 2712, the ANMAR sinusoid is backprojected to obtain the final MAR image.
It should be noted that the implementation of step 270 may be implemented in other manners. For example, in another embodiment of the present invention, referring to fig. 6, step 270 may further include the following sub-steps 2721 through 2722.
In sub-step 2721, the a priori sinusoids obtained in step 260 are weighted summed and denormalized to obtain an ANMAR sinusoid. Specifically, in the process of carrying out weighted summation processing on the prior sinusoidal curve, two non-metal points closest to the metal track on the prior sinusoidal curve are selected for weighted summation.
In sub-step 2722, the ANMAR sinusoid is backprojected to obtain the final MAR image.
A method of obtaining a MAR image according to an exemplary embodiment of the present invention has been described. As can be seen from the comparison result shown in fig. 8, the MAR image obtained by the conventional method has a significant white streak-like artifact caused by a high-density substance in its subsequent prior image because of the metal-ray hardening artifact in its original image which has not been MAR-processed, and thus remains in its final MAR image; with the MAR image acquisition method of the present invention, however, since the original image is subjected to metal-ray-hardening correction (BHC) in particular before it is MAR-processed, i.e., the image subjected to BHC processing and kept at low contrast resolution is used to generate the prior image, the white banding artifacts caused by the high density material in the subsequent prior image are completely eliminated, and accordingly the white banding artifacts caused by the high density material in the resulting MAR image are also eliminated. Therefore, compared with the prior art, the MAR image obtaining method can reduce metal artifacts more remarkably under the condition of not reducing the resolution of the MAR image, thereby greatly improving the quality of CT images and being beneficial to doctors to make diagnosis results more accurately.
Similar to the method, the invention also provides a corresponding device.
FIG. 7 is a schematic block diagram of an apparatus for obtaining MAR images according to an exemplary embodiment of the present invention.
As shown in fig. 7, the apparatus 800 may include: an original image acquisition module 810 for back-projecting the original sinusoid to acquire an original image; a metal track acquisition module 820 for acquiring a metal mask from the original image and acquiring a metal track based on the metal mask; a metal BHC module 830, configured to perform metal BHC on the original image to obtain a metal BHC image; a PC image acquisition module 840 for obtaining a PC image from the metal track; the operation processing module 850 is configured to perform operation processing on the metal BHC image and the PC image to obtain a priori image; the prior sinusoidal curve obtaining module 860 is configured to forward project the prior image to obtain a prior sinusoidal curve; and a prior sinusoid processing module 870 for processing the prior sinusoids to finally obtain a MAR image.
In one embodiment of the present invention, the metal track acquisition module 820 obtains the metal mask by performing threshold extraction on the original image, and then obtains the metal track by performing forward projection on the metal mask.
In one embodiment of the present invention, the PC image acquisition module 840 may further include: the PC sinusoidal curve acquisition module is used for acquiring a PC sinusoidal curve from the metal track; and the back projection module is used for carrying out back projection on the PC sinusoidal curve so as to obtain the PC image.
In one embodiment of the present invention, the operation processing module 850 may further include: the metal BHC image processing module is used for performing total variation processing and Gaussian frequency decomposition processing on the metal BHC image to obtain a first high-frequency component and a first low-frequency component; the PC image processing module is used for carrying out Gaussian frequency decomposition processing on the PC image to obtain a second high-frequency component and a second low-frequency component; an intermediate variable obtaining module, configured to obtain an intermediate variable based on the first high-frequency component, the first low-frequency component, the second high-frequency component, and the second low-frequency component; the segmentation module is used for respectively carrying out segmentation processing on the PC image and the intermediate variable to obtain a first image and a second image; and a prior image acquisition module, configured to correct the first image, and apply the second image to the corrected first image to obtain the prior image.
In one embodiment of the present invention, the a priori sinusoid processing module 870 may further include: an Adaptive Normalized MAR (ANMAR) sinusoid acquisition module configured to perform interpolation and de-normalization on the prior sinusoid to obtain an ANMAR sinusoid; and a MAR image acquisition module for back-projecting the ANMAR sinusoid to obtain the MAR image.
In another embodiment of the present invention, the a priori sinusoid processing module 870 may further include: the ANMAR sinusoidal curve acquisition module is used for carrying out weighted summation processing and de-normalization processing on the prior sinusoidal curve to obtain an ANMAR sinusoidal curve; and a MAR image acquisition module for back-projecting the ANMAR sinusoid to obtain the MAR image.
An apparatus for obtaining a MAR image according to an exemplary embodiment of the present invention has been described. As can be seen from the comparison result shown in fig. 8, the MAR image obtained with the conventional apparatus, because there is a metal-ray hardening artifact in the original image thereof which has not been MAR-processed, there is a significant white streak-like artifact caused by a high-density substance in the subsequent prior image thereof, and thus the white streak-like artifact is also retained in the MAR image obtained finally thereof; with the MAR image obtaining apparatus of the present invention, however, since the original image thereof is subjected to metal-ray-hardening correction (BHC) in particular before being MAR-processed, that is, the image subjected to BHC processing and kept at low contrast resolution is used for generating the prior image, the white streak-like artifact caused by the high-density substance in the subsequent prior image thereof is completely eliminated, and accordingly, the white streak-like artifact caused by the high-density substance in the final MAR image is also eliminated. Therefore, compared with the prior art, the MAR image acquisition device can remarkably reduce metal artifacts under the condition of not reducing the resolution of the MAR image, thereby greatly improving the quality of CT images and being beneficial to doctors to make diagnosis results more accurately.
Fig. 9 shows an image obtained without passing through a MAR, an image obtained with a MAR of a conventional art, and an image obtained with a current MAR according to an exemplary embodiment of the present invention, respectively. It can be seen that the improved MAR image acquisition method and apparatus of the present invention completely eliminates the clear visible white banding artifacts in images acquired without MAR and images acquired with MAR using conventional techniques. The inventor of the present invention has obtained the method and apparatus for obtaining the MAR image according to the exemplary embodiments of the present invention by unexpectedly combining the physical characteristics of the X-ray with the conventional image restoration algorithm, which greatly improves the reduction of the artifact of the high-density material compared with the prior art, thereby greatly improving the quality of the CT image, and facilitating the doctor to make the diagnosis more accurately.
Some exemplary embodiments have been described above. However, it should be understood that various modifications may be made. For example, suitable results may be achieved if the described techniques were performed in a different order and/or if components in the described systems, architectures, devices or circuits were combined in a different manner and/or replaced or supplemented by additional components or equivalents thereof. Accordingly, other embodiments are within the scope of the following claims.
Claims (12)
1. A method of obtaining an image with reduced metal artifacts, comprising the steps of:
back projecting the original sinusoidal curve to obtain an original image;
obtaining a metal mask from the original image, and obtaining a metal track based on the metal mask;
performing metal ray hardening correction on the original image to obtain a metal ray hardening correction image;
obtaining a projection finished image from the metal track;
performing total variation processing and Gaussian frequency decomposition processing on the metal ray hardening correction image to obtain a first high-frequency component and a first low-frequency component;
carrying out Gaussian frequency decomposition processing on the projection finished image to obtain a second high-frequency component and a second low-frequency component;
obtaining an intermediate variable based on the first high frequency component, the first low frequency component, the second high frequency component, and the second low frequency component;
dividing the projection completed image and the intermediate variable respectively to obtain a first image and a second image;
correcting the first image, and applying the second image to the corrected first image to obtain a priori image;
forward projecting the prior image to obtain a prior sinusoidal curve; and
the a priori sinusoids are processed to ultimately obtain an image with reduced metal artifacts.
2. The method according to claim 1, wherein in the step of obtaining a metal mask from the original image and obtaining a metal track based on the metal mask, the metal mask is obtained by threshold extraction of the original image.
3. The method of claim 2, wherein the metal track is obtained by forward projecting the metal mask.
4. The method of claim 1, wherein the step of deriving a projection-completed image from the metal track further comprises:
obtaining a projection-completed sinusoidal curve from the metal track; and
and carrying out back projection on the projection-completed sinusoidal curve to obtain the projection-completed image.
5. The method of claim 1, wherein the step of processing the a priori sinusoids to ultimately result in an image with reduced metal artifacts further comprises:
performing interpolation processing and de-normalization processing on the prior sinusoidal curve to obtain a sinusoidal curve with reduced self-adaptive normalized metal artifacts; and
back projecting the adaptively normalized metal artifact reduced sinusoid to obtain the metal artifact reduced image.
6. The method of claim 1, wherein the step of processing the a priori sinusoids to ultimately result in an image with reduced metal artifacts further comprises:
performing weighted summation processing and de-normalization processing on the prior sinusoids to obtain adaptively normalized sinusoids with reduced metal artifacts; and
back projecting the adaptively normalized metal artifact reduced sinusoid to obtain the metal artifact reduced image.
7. An apparatus for obtaining an image with reduced metal artifacts, comprising:
the original image acquisition module is used for carrying out back projection on the original sinusoidal curve to acquire an original image;
the metal track acquisition module is used for acquiring a metal mask from the original image and acquiring a metal track based on the metal mask;
the metal ray hardening correction module is used for carrying out metal ray hardening correction on the original image to obtain a metal ray hardening correction image;
the projection completion image acquisition module is used for acquiring a projection completion image from the metal track;
the operation processing module is used for carrying out operation processing on the metal ray hardening correction image and the projection completion image to obtain a priori image;
the prior sinusoidal curve acquisition module is used for forward projecting the prior image to obtain a prior sinusoidal curve; and
a prior sinusoidal processing module for processing the prior sinusoidal to finally obtain an image with reduced metal artifact,
wherein, the operation processing module further comprises:
the metal ray hardening correction image processing module is used for carrying out total variation processing and Gaussian frequency decomposition processing on the metal ray hardening correction image so as to obtain a first high-frequency component and a first low-frequency component;
the projection completion image processing module is used for carrying out Gaussian frequency decomposition processing on the projection completion image to obtain a second high-frequency component and a second low-frequency component;
an intermediate variable obtaining module, configured to obtain an intermediate variable based on the first high-frequency component, the first low-frequency component, the second high-frequency component, and the second low-frequency component;
the segmentation module is used for respectively carrying out segmentation processing on the projection completed image and the intermediate variable to obtain a first image and a second image; and
and the prior image acquisition module is used for correcting the first image and applying the second image to the corrected first image to obtain the prior image.
8. The apparatus of claim 7, wherein the metal track acquisition module obtains the metal mask by thresholding the original image.
9. The apparatus of claim 8, wherein the metal track acquisition module obtains the metal track by forward projecting the metal mask.
10. The apparatus of claim 7, wherein the projection-complete-image acquisition module further comprises:
the projection completion sinusoidal curve acquisition module is used for obtaining a projection completion sinusoidal curve from the metal track; and
and the back projection module is used for carrying out back projection on the projection-completed sinusoidal curve to obtain the projection-completed image.
11. The apparatus of claim 7, wherein the a priori sinusoid processing module further comprises:
the adaptive normalization sinusoidal curve acquisition module is used for carrying out interpolation processing and de-normalization processing on the prior sinusoidal curve to obtain an adaptive normalization sinusoidal curve with reduced metal artifact; and
and the image acquisition module is used for carrying out back projection on the self-adaptive normalized metal artifact reduction sinusoidal curve to obtain the metal artifact reduction image.
12. The apparatus of claim 7, wherein the a priori sinusoid processing module further comprises:
the adaptive normalized metal artifact reduced sinusoidal curve acquisition module is used for carrying out weighted summation processing and de-normalization processing on the prior sinusoidal curve to obtain an adaptive normalized metal artifact reduced sinusoidal curve; and
and the image acquisition module is used for carrying out back projection on the self-adaptive normalized metal artifact reduction sinusoidal curve to obtain the metal artifact reduction image.
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| CN103190928A (en) * | 2011-08-10 | 2013-07-10 | 西门子公司 | Method, computing unit, CT system and C-arm system for reducing metal artifacts |
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