CN106651986A - Computed tomography artifact correction method - Google Patents
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- 238000012937 correction Methods 0.000 title claims abstract description 117
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000002591 computed tomography Methods 0.000 title claims abstract description 19
- 238000001914 filtration Methods 0.000 claims abstract description 3
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- 238000012545 processing Methods 0.000 abstract description 8
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- 238000003384 imaging method Methods 0.000 description 5
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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
- G06T11/008—Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
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- G—PHYSICS
- 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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- G—PHYSICS
- 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
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
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Abstract
The invention discloses a computed tomography artifact correction method, comprising the steps of receiving a to-be-corrected image; carrying out artifact correction on the to-be-corrected image, thereby obtaining a first corrected image; obtaining an error image of the to-be-corrected image relative to the first corrected image; obtaining information entropy of the first corrected image according to the error image and determining the degree of introducing artifact into the first corrected image according to the information entropy; adjusting the weight of the error image according to the degree of introducing artifact into the first corrected image and removing the error image of which weight is adjusted from the to-be-corrected image, thereby obtaining a second corrected image; and carrying out self-adaption filtering processing on the to-be-corrected image and carrying out frequency segmentation and integration on the processed to-be-corrected image and the second corrected image, thereby obtaining a third corrected image. According to the method and the device provided by the invention, on the basis of the existing artifact removing method, the newly introduced artifact is effectively inhibited, and the image quality is further improved.
Description
The application be the applying date on January 21st, 2016, Application No. " 201610038856.7 ", entitled " one
The divisional application of the application for a patent for invention of kind computer tomography artifact correction method and device ".
【Technical field】
The present invention relates to Computed tomography field, more particularly to a kind of correction of computer tomography artifact
Method and device.
【Background technology】
Computer tomography is that the privileged site of human body is scanned by certain thickness aspect with ray, according to not
Same tissue is different to the absorbability of ray, and to scan data the technology of fault plane image is gone out using computer reconstruction.
During computed tomography, because causing reconstruction containing metal or other high-density matters in scanned object
Artifact is referred to as metal artifacts present in image afterwards.The presence of metal artifacts can reduce picture quality, and may affect
The diagnosis of doctor.Therefore, in the imaging of computed tomography, go metal artifacts (Metal Artifact Reduction,
MAR it is) metal artifacts reduction, to be solved exactly to remove the artifact introduced due to the presence of metal, recovery is broken by artifact
Tissue that is bad or covering, in order to user's observation.
In prior art, there is the bearing calibration of various metal artifacts. these bearing calibrations can substantially be divided into iteration weight
Construction method (iterative method) and projection interpolation method (interpolation method).But no matter in prior art which kind of form artifact correction, have
New artifact may be introduced, the artifact that this part newly introduces can equally affect picture quality, so that being corrected to it.
It is, therefore, desirable to provide a kind of new computer tomography artifact correction method, in existing artifact minimizing technology
On the basis of, effectively suppress because artifact correction newly introduces artifact, further improve picture quality.
【The content of the invention】
What the present invention was solved is occur newly drawing in existing computer tomography image artifacts bearing calibration implementation process
Enter the problem of artifact.
To solve the above problems, the present invention proposes a kind of computer tomography artifact correction method, including：School is treated in reception
Positive image；Artifact correction is carried out to the image to be corrected, to obtain the first correction after image；Obtain image to be corrected relative to
The error image of image after first correction；The degree of artifact is introduced according to image after the first correction, the error image is adjusted
Weight, and from the image to be corrected remove adjustment weight after error image, to obtain the second correction after image；To described
Image carries out frequency division and fusion after image to be corrected and the second correction, obtains image after the 3rd correction.
Alternatively, image introduces the degree of artifact, the comentropy of image after being corrected by described first after first correction
It is determined.
Alternatively, the acquisition of described information entropy includes：Divide the neighbour of the error image and each pixel of image to be corrected
Domain matrix；The weight of the error image neighborhood of pixels matrix is adjusted, and according to the error image pixel after adjustment weight
The difference of Neighborhood matrix and the image pixel Neighborhood matrix to be corrected obtains described information entropy.
Alternatively, obtaining makes the weight of the corresponding error image Neighborhood matrix of described information entropy minimum phase, by this
Weight of the weight as the error image.
Alternatively, the degree for being removed according to artifact in the error image, determines the size of the Neighborhood matrix.
Alternatively, the metal image form in the image to be corrected, determines the size of the Neighborhood matrix.
Alternatively, the size range of the Neighborhood matrix is 9-31 unit pixels.
Alternatively, also include according to setting visual field to the image to be corrected, error image, first correction after image,
At least one after second correction in image is compressed.
Alternatively, the frequency division and fusion includes：It is partitioned into HFS image and the institute of the image to be corrected
The low frequency part image of image after the second correction is stated, and the HFS image and low frequency part image are merged.
The present invention also provides a kind of computer tomography artifact correction device, it is characterised in that include：
Input block, for receiving image to be corrected；First processing units, for treating correction chart picture artifact school is carried out
Just, to generate the first correction after image；Second processing unit, for obtaining image to be corrected relative to image after the first correction
Error image, and the degree of artifact is introduced according to image after the first correction, the weight of the error image is adjusted, and treat from described
In correction chart picture remove adjustment weight after error image, to obtain the second correction after image；3rd processing unit, for obtaining
The low frequency part image of image after the HFS image of the image to be corrected and second correction, and to the radio-frequency head
Partial image and low frequency part image are merged, and obtain image after the 3rd correction.
The present invention program introduces the degree of artifact, the weight of alignment error image according to original artifact correction, and is treating school
Image after the error image after adjustment weight is removed in positive image to form new correction, effectively inhibits the generation of new artifact,
Improve picture quality；Simultaneously the program realizes that process is low to former artifact correction method and device dependence, thus the scope of application
Extensively, can realize on the basis of any existing metal artifacts reduction method and device that may introduce new artifact；Further, frequency
Rate split and fusion reduce the image-mosaics phenomenon caused due to the difference of weight coefficient so that after correction image more from
So；Further, image is compressed, and the degree removed according to artifact in error image is divided to Neighborhood matrix,
The amount of calculation of system is reduced, correction rate is improved.
【Description of the drawings】
Fig. 1 is the structural representation of the computed tomography imaging system of the present invention；
Fig. 2 is artifact correction method schematic flow sheet in one embodiment of the invention；
Fig. 3 is that image information entropy asks for schematic flow sheet after the first correction in one embodiment of the invention；
Fig. 4 is artifact correction apparatus structure schematic diagram in one embodiment of the invention.
【Specific embodiment】
It is understandable to enable the above objects, features and advantages of the present invention to become apparent from, below in conjunction with the accompanying drawings to the present invention
Specific embodiment be described in detail.
Fig. 1 is a kind of structural representation of computed tomography imaging system, as shown in figure 1, computed tomography imaging system
100 include frame 110, and the frame 110 has around the rotatable part 130 of system axis rotation.Rotatable part
130 x-ray systems with the x-ray source 131 and X-ray detector 132 being oppositely arranged.
Computed tomography imaging system 100 also has examination couch 120, and when being checked, patient is on the examination couch 120
Can be pushed in scanning cavity along Z-direction.X-ray source 131 rotates around S axles, and detector 132 is relative to x-ray source
131 move together, and with acquired projections measurement data, these data are used for reconstruction image after.Spiral can also be carried out to sweep
Retouch, during helical scanning, rotated with while x-ray source 131 along continuously moving for S axles by patient, x-ray source 131
Helical trajectory is produced relative to patient.
The computed tomography imaging system 100 can also include control unit and image reconstruction unit, the control list
Unit is used in scanning process according to each part of specific scan protocols control computer computed tomography (SPECT) system 100.The figure
As reconstruction unit is used to go out image according to the data reconstruction to be corrected of the sampling of detector 132.
More than, only illustrating by way of example can use the computerized tomography of metal artifacts reduction method provided by the present invention
Imaging device, it will be appreciated by those skilled in the art that the C-arm system equipment of X-ray is such as used, or combined type medical image system
(for example：Combined type positron emission tomography-computer tomography, Positron Emission Tomography-
Computed tomography Tomography, PET-CT), or the Laminographic device using other type rays etc.,
Applicable bearing calibration of the present invention and device, the present invention does not do to the type of computer tomography equipment with structure to be had
Body is limited.
When detected object is scanned imaging in the computer tomography equipment of any of the above-described species, because of metal or
The presence of high-density objects can cause the presence of artifact, affect image image quality and cause user to read figure inconvenience, thus need to be right
Such artifact is corrected.
Fig. 2 is artifact correction method schematic flow sheet in one embodiment of the invention.Reference Fig. 2, in this embodiment：
Execution step S1, receives image to be corrected.The image to be corrected is by above computer Laminographic device scanning weight
Build acquisition.
Execution step S2, treating correction chart picture carries out artifact correction, obtains image after the first correction.In this step, not
Limit the specific implementation of the artifact correction.Because the purpose of the present invention program is to solve various gold present in prior art
New introducing artifact defect existing for the bearing calibration of category artifact, any one removes metal artifacts algorithm or various removes metal artifacts
Algorithm combination (no matter iterative method or interpolation method, also or other species artifact correction method), introduce whenever possible new
Artifact, can be applicable to this.Treating correction chart picture carries out that after artifact correction, image after the first correction can be obtained.
Execution step S3, obtains error image of the image to be corrected relative to image after the first correction.In the present embodiment,
Definition Iori be image to be corrected, IcorrFor image after the first correction, IerrFor error image.Then：
Ierr=Iori-Icorr
That is, can after image relatively more to be corrected is corrected with first image difference obtaining error image.
Execution step S4, according to image after the first correction the degree of artifact is introduced, the weight of alignment error image, and from treating
The error image after adjustment weight is removed in correction chart picture, image after the second correction is obtained.Image introduces artifact after first correction
Degree can be drawn by the judgement to error image, therefore, by each pixel to error image distribute weight (point
The principle matched somebody with somebody is, distributes the new artifact region for introducing few weight, many weights of the original artifact distribution of image to be corrected),
Remove the error image after adjustment weight in image to be corrected again, you can realize suppressing the new effect for introducing artifact.
In the present embodiment, image newly introduces the degree of artifact after the first correction, the letter of image after can correcting by first
Breath entropy is determined.What Fig. 3 showed image information entropy after the first correction asks for flow process：
First, execution step S401, divides the Neighborhood matrix of error image and each pixel of image to be corrected.Specifically,
Such as the image I of N × N pixel, to each pixel of its image, (projected angle is p, and passage is k) one neighbour of calculating
Domain matrix NP, k(I).Find through test, the size (MxM) of the Neighborhood matrix not only affects arithmetic speed, and can be direct
After having influence in step weight distribution, so the optional mode for taking empirical value of division of Size of Neighborhood determines.For example, at this
In embodiment, the size of neighborhood distributes different values according to the degree that artifact in error image is removed.Generally, for step
Correction of image artifacts is treated in S2 and removes more application scenarios, neighborhood divide can with slightly larger, otherwise then can be with slightly smaller.Further
Ground, the span of neighborhood can be between 9-31 unit pixels.Additionally, when Neighborhood matrix is taken to image edge pixels, can be with
In image border, image border is expanded in zero filling.
Herein, as a change case of the present embodiment, the size of neighborhood can be carrying out really according to metal form information
It is fixed.Metal form information herein, refers to the shape information of metallics or higher density material in image to be corrected, its direct shadow
Ring the degree to data corruption in image to be corrected：In theory, it is existing only when metal or high-density objects are a regular circle shapes
Correcting scheme can effectively remove metal artifacts (newly introducing artifact less) by way of replacing legacy data completely.And it is actual
In little well-regulated object, the shape of such as common backbone nail implant is more to be presented with irregular form.
If metal form information can be characterized with morphological indices：In a certain fault plane, ray passes through the fault plane
No matter the circular object of one rule, then projected from unspecified angle, and its area is consistent, can be thrown on this preferable metal
Shadow domain area is defined as preferable area, and used as a reference of metal form is judged, it obtains formula can be：
In the formula, SImetalRefer to number of pixels in metal image；SpacechannelFor channel resolution,
SpacepixelFor pixel resolution, NviewFor projected angle number, SIdealPmetalRefer to regular metal image (metal image domain pixel
Number is identical with the metal pixel number of actual metal image) non-zero pixels number (i.e. preferable area) after projection.
Metal form index can be obtained by comparing the preferable area of metal image data for projection with real area.Further
Ground, if SPmetalFor the non-zero pixels number (i.e. metal image real area) after actual metal image projection, then metal form refers to
Number RD acquisition formula be：
According to an alternate embodiment of the present embodiment, the artifact correction method in step S2 is projection domain weighted correction：
Metal form information influences whether the projection domain weighted correction of the step S2 whether intensity for carrying out and weighting, and then affects the first school
The quality of image after just, thus, the size of neighborhood when image information entropy is asked for after the first correction can be entered according to metal form information
Row is divided.In general, if metal image real area is closer to (as metal form index RD is less than or connects with preferable area
It is bordering on 1), then carry out less intensity weights the weighting that even intensity is zero, the larger neighborhood of corresponding selection；If with preferable area
Difference larger (if metal form index RD is 2 or so), then carry out the weighted correction of higher-strength, the less neighbour of corresponding selection
Domain.
Complete to divide after error image and the Neighborhood matrix of each pixel of image to be corrected, continue as shown in figure 3, performing step
Rapid S402, based on the Neighborhood matrix for dividing image area weighting is carried out.Specifically, for each pixel in each error image
Neighborhood matrix is weighted, and the Neighborhood matrix of image to be corrected makes the difference the Neighborhood matrix for obtaining image after the first correction：
NP, k(Icw)=NP, k(Iori)-wP, k×NP, k(Ierr)
In formula, Icw refers to image after the first correction, wP, kFor the weight of each pixel in Neighborhood matrix, it is right that its size is determined
Artifact increases or mitigates in error image.
Execution step S403, calculates the comentropy of image after the first correction.Is characterized in the present embodiment using comentropy
Structural information after one correction in image.Specifically, the comentropy of image is after the first correction：
Wherein, Entropy (NP, k(Icw) comentropy of image pixel Neighborhood matrix after the first correction, P (N) are representedP, k
(Icw) (function can be obtained the prior probability function of the Neighborhood matrix of image pixel by histogram mode after) expression first is corrected
Take), i represents the pixel subscript in Neighborhood matrix, and n represents sum of all pixels.
According in S402 and S403 first correction after image information entropy ask for process it can be seen from, different pixel weights wP, k
The different comentropy of correspondence, by adjusting wP, k, can obtain makes image information entropy Entropy (N after the first correctionP, k(Icw)) most
The weight of hour is arg w min (Entropy (NP, k(Icw))).Using the weight as the weight after error image adjustment, and
The error image after being adjusted by the weight is removed from image to be corrected, to obtain the second correction after image.
Because being possible to that some mosaic effects can be introduced in image after the second correction by above-mentioned weight Adjustable calculation, because
This, as shown in Fig. 2 also needing execution step S5, treating image after correction chart picture and the second correction carries out frequency division and fusion.
Specifically, can by treating correction chart picture and the second correction after image carry out frequency division, obtain non-correction chart picture
HFS image and correction chart picture low frequency part image and it is merged to realize, for example can select Gauss low pass
Wave filter G (σ) realizes LPF by frequency domain convolution, wherein：
LoriLow=Iori*G (σ)
Icorr2Low=Icorr2*G (σ)
In formula, σ is an empirical value, and optional scope is that e represents the bottom of exponential function, and x represents distance between 1.2-1.3,
Iori represents image to be corrected, and IoriLow represents the low frequency part image of image to be corrected, and Icorr2 represented and scheme after the second correction
Picture, Icorr2Low represents the low frequency part image of image after the second correction.Certainly, also can select other types of LPF
Device.
Preferably, before image to be corrected is filtered, adaptive-filtering can be done and processes thin to eliminate most high frequency
Bar artifact.Obtain image to be corrected and second correction after image low frequency part image after, can be subtracted each other by pixel and obtain right
The HFS image IoriHigh of image after the second correction answered：
IoriHigh=Iori-IoriLow
Merge the high frequency imaging of non-correction chart picture and the low-frequency image of correction chart picture, you can obtain image after the 3rd correction.
During above-mentioned artifact correction, can also according to setting visual field to the image to be corrected, error image, first
At least one after correction after image, the second correction in image is compressed, to improve computational efficiency.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can
Completed with instructing the hardware of correlation by program, the program can be stored in computer-readable recording medium, storage is situated between
Matter can be including but not limited to：Floppy disk, CD, CD-ROM, magneto-optic disk, ROM (read-only storage), RAM (random access memories
Device), EPROM (Erasable Programmable Read Only Memory EPROM), EEPROM (Electrically Erasable Read Only Memory), magnetic card or light
Card, flash memory are suitable to store the other kinds of medium/machine readable media of machine-executable instruction.
Fig. 4 shows the structural representation of one embodiment of the invention Computer fault imaging artifact correction device.Including：
Input block, for receiving image to be corrected；
First processing units, for treating correction chart picture artifact correction is carried out, to generate the first correction after image；
Second processing unit, for obtain image to be corrected relative to first correction after image error image, and according to
Image introduces the degree of artifact after first correction, adjusts the weight of the error image, and removes from the image to be corrected
Adjustment weight after error image, to obtain the second correction after image；
3rd processing unit, for obtain the image to be corrected HFS image and it is described second correction after image
Low frequency part image, and the HFS image and low frequency part image are merged, obtain image after the 3rd correction；
And output unit, for the image after correction to be exported to user or follow-up computing device.
Input block, first processing units and output unit in the present embodiment, can in the prior art any one be pseudo-
Realize on the basis of shadow means for correcting, thus, the artifact correction device of the offer of the present invention possesses good compatible and relatively low
Implementation cost.
In the present invention, each embodiment adopts laddering literary style, emphasis description and the difference of previous embodiment, respectively implements
Same procedure or structure in example same section with reference to the foregoing embodiments.
Although the present invention is disclosed as above with preferred embodiment, it is not for limiting the present invention, any this area
Technical staff without departing from the spirit and scope of the present invention, may be by the methods and techniques content of the disclosure above to this
Bright technical scheme makes possible variation and modification, therefore, every content without departing from technical solution of the present invention, according to the present invention
Technical spirit any simple modification, equivalent variations and modification that above example is made, belong to technical solution of the present invention
Protection domain.
Claims (7)
1. a kind of computer tomography artifact correction method, it is characterised in that include：
Receive image to be corrected；
Artifact correction is carried out to the image to be corrected, to obtain the first correction after image；
Obtain error image of the image to be corrected relative to image after the first correction；
The comentropy of image after the first correction is obtained according to the error image, and image after the first correction is determined according to the comentropy
Introduce the degree of artifact；
The degree for introducing artifact according to image after first correction adjusts the weight of the error image,
And from the image to be corrected remove adjustment weight after error image, to obtain the second correction after image；
Treating correction chart picture carries out adaptive-filtering process, and to process after image to be corrected carry out frequency division, be partitioned into
The HFS image of the image to be corrected, to image after the described second correction frequency division is carried out, and obtains second school
The low frequency part image of image after just, and the HFS image and low frequency part image are merged, to obtain the 3rd
Image after correction.
2. artifact correction method according to claim 1, it is characterised in that described that first school is obtained according to the error image
The comentropy of image includes after just：
Divide the Neighborhood matrix of the error image and each pixel of image to be corrected；
The weight of the error image neighborhood of pixels matrix is adjusted, and according to the error image neighborhood of pixels after adjustment weight
The difference of matrix and the image pixel Neighborhood matrix to be corrected obtains described information entropy.
3. artifact correction method according to claim 2, it is characterised in that acquisition makes described information entropy corresponding when minimum
The weight of the error image Neighborhood matrix, using the weight as the error image weight.
4. artifact correction method according to claim 2, it is characterised in that gone according to metal artifacts in the error image
The degree removed, determines the size of the Neighborhood matrix.
5. artifact correction method according to claim 2, it is characterised in that according to the metal figure in the image to be corrected
As form, the size of the Neighborhood matrix is determined.
6. artifact correction method according to claim 2, the size of the Neighborhood matrix is 9-31 unit pixels.
7. artifact correction method according to claim 2, it is characterised in that also include：According to the visual field for setting to described
At least one after image to be corrected, error image, the first correction after image, the second correction in image is compressed.
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| CN201611246223.1A CN106651986B (en) | 2016-01-21 | 2016-01-21 | Computer tomography artifact correction method |
| CN201610038856.7A CN105528800B (en) | 2016-01-21 | 2016-01-21 | A kind of computer tomography artifact correction method and device |
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| CN201611244253.9A Active CN106651984B (en) | 2016-01-21 | 2016-01-21 | Computer tomography artifact correction method and device |
| CN201611246225.0A Active CN106683143B (en) | 2016-01-21 | 2016-01-21 | Image metal artifact correction method |
| CN201610038856.7A Active CN105528800B (en) | 2015-10-14 | 2016-01-21 | A kind of computer tomography artifact correction method and device |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110084866A (en) * | 2019-04-24 | 2019-08-02 | 上海联影医疗科技有限公司 | A kind of computer tomography method and apparatus |
| CN112819726A (en) * | 2021-02-09 | 2021-05-18 | 嘉兴丰鸟科技有限公司 | Light field rendering artifact removing method |
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| CN106651986B (en) | 2021-05-18 |
| CN106651984B (en) | 2021-05-07 |
| CN106651984A (en) | 2017-05-10 |
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| CN105528800A (en) | 2016-04-27 |
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| CN106683143B (en) | 2021-03-16 |
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