CN109472836A - Artifact correction method in a kind of CT iterative approximation - Google Patents
Artifact correction method in a kind of CT iterative approximation Download PDFInfo
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- CN109472836A CN109472836A CN201811067675.2A CN201811067675A CN109472836A CN 109472836 A CN109472836 A CN 109472836A CN 201811067675 A CN201811067675 A CN 201811067675A CN 109472836 A CN109472836 A CN 109472836A
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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
Abstract
Artifact correction method in a kind of CT iterative approximation disclosed by the invention is specifically included the digital chromatography imaging device Raw projection data y of acquisition, is rebuild using data for projection y of the filter back-projection algorithm to acquisition, obtains correction image I1, to correction image I1It is pre-processed, obtains image rectification template I2, to image rectification template I2Forward projection is carried out, projection correction template y is obtained1, utilize projection correction template y1Raw projection data y is corrected, is projected after being correctedAnd to being projected after correctionIt is iterated reconstruction, obtains final required six steps of image μ rebuild, artifact correction method in CT iterative approximation of the invention, compared with prior art, it is easy to operate, the artifact as caused by rFOV in CT iterative approximation can be effectively removed, and reconstructed results do not cause the deviation of image CT value.
Description
Technical field
The invention belongs to the image processing method technical fields of medical image, are related to a kind of CT images bearing calibration, specifically
It is related to artifact correction method in a kind of CT iterative approximation.
Background technique
Filtered back projection's (FBP) algorithm is always the main method for reconstructing of commercialization CT, but FBP algorithm is not particularly suited for weight
Build the projection (i.e. low-dose CT data) with critical noisy.Therefore iterative reconstruction algorithm (Iterative
Reconstruction, IR) become research hotspot in recent years, because IR algorithm can pass through the reasonable statistics mould of building
Type, while can introduce different prior informations according to the difference of acquisition data, assists iterative approximation, it is available it is outstanding at
Image quality amount.In the past few years, different CT manufacturers issued several business IR technologies (such as GE Healthcare
The SAFIRE of ASIR, Siemens Healthcare).
But in CT imaging device (for example, medical CT equipment) reconstruction process, some parts of body are (for example, shoulder
Or pelvis) may beyond rebuild the visual field (reconstruction field of view, rFOV), if directly to original projection into
(Iterative Reconstruction, IR) is rebuild in row iteration, may be in iterative reconstruction process, outside rFOV
Tissue cause between Current projection and measurement projection there are inconsistencies, and therefore result in the artifact in reconstruction image, with
And there is the deviation of CT value.
Summary of the invention
The present invention provides artifact correction method in a kind of CT iterative approximation, specifically comprises the following steps:
Step 1 obtains digital chromatography imaging device Raw projection data y；
Step 2 rebuilds the data for projection y obtained in step 1 using filter back-projection algorithm, obtains correction image
I1；
Step 3, to obtained in step 2 correct image I1It is pre-processed, obtains image rectification template I2；
Step 4, to image rectification template I obtained in step 32Forward projection is carried out, projection correction template y is obtained1；
Step 5 utilizes projection correction's template y obtained in step 41Raw projection data y is corrected, is corrected
After project
Step 6, to obtained in step 5 correct after projectIt is iterated reconstruction, obtains the final required image rebuild
μ。
The characteristics of this method, also resides in,
When being rebuild described in step 2 using filter back-projection algorithm, when rebuilding the visual field and projection data acquisitions
Scan vision it is identical.
Step 3 specifically comprises the following steps:
3.1, to correction image I1In be located at iterative approximation area of visual field in pixel zero setting, and retain rebuild the visual field except
Pixel value, obtain preliminary corrections image
3.2, to the preliminary corrections imageThreshold process is carried out, image rectification template I is obtained2；
The concrete mode of threshold process is formula (1):
Wherein, t indicates adjustable threshold parameter.
Step 5 uses formula (2):
Iterative approximation described in step 6 specifically uses the punishment weighted least-squares based on non local similitude regularization
Method.
The beneficial effects of the present invention are: artifact correction method in CT iterative approximation of the invention, compared with prior art, behaviour
Make simply, the artifact as caused by rFOV in CT iterative approximation can be effectively removed, and reconstructed results do not cause image CT value
Deviation.
Detailed description of the invention
Fig. 1 is the flow diagram of artifact correction method in a kind of CT iterative approximation of the invention；
Fig. 2 a is the result rebuild using existing FBP algorithm to fan-beam；
Fig. 2 b is the result rebuild using iterative algorithm to fan-beam；
Fig. 2 c is the knot rebuild using artifact correction method in a kind of CT iterative approximation of the invention to fan-beam
Fruit.
Specific embodiment
With reference to the accompanying drawing and specific embodiment the present invention is described in detail.
The invention discloses artifact correction methods in a kind of CT iterative approximation, as shown in Figure 1, specifically comprising the following steps:
Step 1 obtains digital chromatography imaging device Raw projection data y；
Step 2 rebuilds the data for projection y obtained in step 1 using filter back-projection algorithm, obtains correction image
I1；It is identical as scan vision when projection data acquisitions that it rebuilds the visual field.
Step 3, to obtained in step 2 correct image I1It is pre-processed, obtains image rectification template I2；It is specifically divided into
Following two step:
3.1, to correction image I1In be located at iterative approximation area of visual field in pixel zero setting, and retain rebuild the visual field except
Pixel value, obtain preliminary corrections image
3.2, to the preliminary corrections imageThreshold process is carried out, image rectification template I is obtained2；
The concrete mode of threshold process is formula (1):
Wherein, t indicates adjustable threshold parameter.
Step 4, to image rectification template I obtained in step 32Forward projection is carried out, projection correction template y is obtained1；
Step 5 utilizes projection correction's template y obtained in step 41Raw projection data y is corrected, is corrected
After projectSpecifically use formula (2):
Step 6 sends out weighted least-squares method to obtained in step 5 using the punishment based on non local similitude regularization
It is projected after correctionIt is iterated reconstruction, obtains the final required image μ rebuild.
Embodiment
By taking fan-beam as an example:
Step 1 obtains digital chromatography imaging device Raw projection data y, which acquires gained by detector；
Step 2 rebuilds the data for projection y obtained in step 1 using filter back-projection algorithm (FBP), obtains school
Positive image I1；It is identical as scan vision when projection data acquisitions that it rebuilds the visual field.
Specifically handled according to formula (3):
Wherein, D ' is weight factor, and γ and θ respectively indicate the corresponding beam angulation of detector cells and X-ray exposure angle
Degree, hf an() is the convolution kernel used when filtering in fladellum FBP.
Step 3, to obtained in step 2 correct image I1It is pre-processed, obtains image rectification template I2；It is specifically divided into
Following two step:
3.1, to correction image I1In be located at iterative approximation area of visual field in pixel zero setting, and retain rebuild the visual field except
Pixel value, obtain preliminary corrections image
3.2, to the preliminary corrections imageThreshold process is carried out, image rectification template I is obtained2；The purpose of this step
Being willIn the pixel value of air section be set to zero, to reduce the error introduced in FBP reconstruction process.
The concrete mode of threshold process is formula (1):
Wherein, t indicates adjustable threshold parameter.
Step 4, to image rectification template I obtained in step 32Forward projection is carried out, projection correction template y is obtained1；Its
Formula is expressed as y1=G × I2, wherein G is the projection operator in CT imaging；
Step 5 utilizes projection correction's template y obtained in step 41Raw projection data y is corrected, is corrected
After projectSpecifically use formula (2):
Step 6, using the punishment weighted least-squares method based on non local similitude regularization to school obtained in step 5
Just project afterwardsIt is iterated reconstruction, obtains the final required image μ rebuild；
The target equation of the step are as follows:
Wherein, μ indicates that image to be reconstructed, W are weighting matrix, and diagonal element is falling for the variance in data for projection
Number, β are adjustable punishment parameter, wjkFor weight factor, can be obtained according to the non local Similarity measures of image.
It should be noted that step 6 after obtained correction to projectingWhen being iterated reconstruction, any iterative algorithm
Using the punishment weighting minimum two based on non local similitude regularization including but not limited to used in the embodiment of the present invention
Multiplication.
By the above-mentioned means, artifact correction method in a kind of CT iterative approximation of the invention, effectively removes CT iteration
The artifact as caused by rFOV in reconstruction, and reconstructed results do not cause the deviation of image CT value, compared with the conventional method, such as scheme
Shown in 2a~Fig. 2 c, Fig. 2 a be using existing FBP algorithm to fan-beam rebuild as a result, in the reconstructed results, figure
As the presence of no artifact, but contain noise；Fig. 2 b be using iterative algorithm to fan-beam rebuild as a result, the reconstructed results
In, picture noise is reduced, but occurs artifact at vision periphery；Fig. 2 c is using artifact school in a kind of CT iterative approximation of the invention
Correction method to fan-beam rebuild as a result, in the reconstructed results, picture noise is reduced, and artifact is eliminated.
Claims (5)
1. artifact correction method in a kind of CT iterative approximation, which is characterized in that specifically comprise the following steps:
Step 1 obtains digital chromatography imaging device Raw projection data y；
Step 2 rebuilds the data for projection y obtained in step 1 using filter back-projection algorithm, obtains correction image I1；
Step 3, to obtained in step 2 correct image I1It is pre-processed, obtains image rectification template I2；
Step 4, to image rectification template I obtained in step 32Forward projection is carried out, projection correction template y is obtained1；
Step 5 utilizes projection correction's template y obtained in step 41Raw projection data y is corrected, is thrown after being corrected
Shadow
Step 6, to obtained in step 5 correct after projectIt is iterated reconstruction, obtains the final required image μ rebuild.
2. artifact correction method in a kind of CT iterative approximation according to claim 1, it is characterised in that: described in step 2
Using filter back-projection algorithm rebuild when, rebuild the visual field it is identical as scan vision when projection data acquisitions.
3. artifact correction method in a kind of CT iterative approximation according to claim 1, it is characterised in that: the step 3
Specifically comprise the following steps:
3.1, to correction image I1In be located at iterative approximation area of visual field in pixel zero setting, and retain rebuild the visual field except picture
Element value, obtains preliminary corrections image
3.2, to the preliminary corrections imageThreshold process is carried out, image rectification template I is obtained2；
The concrete mode of threshold process is formula (1):
Wherein, t indicates adjustable threshold parameter.
4. artifact correction method in a kind of CT iterative approximation according to claim 1, which is characterized in that step 5 is using public
Formula (2):
5. artifact correction method in a kind of CT iterative approximation according to claim 1, it is characterised in that: described in step 6
Iterative approximation specifically use the punishment weighted least-squares method based on non local similitude regularization.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110335325A (en) * | 2019-06-27 | 2019-10-15 | 深圳安科高技术股份有限公司 | A kind of CT image rebuilding method and its system |
| CN111080740A (en) * | 2019-12-27 | 2020-04-28 | 上海联影医疗科技有限公司 | Image correction method, device, equipment and medium |
| CN112991482A (en) * | 2021-04-12 | 2021-06-18 | 明峰医疗系统股份有限公司 | GPU-based rapid reconstruction imaging method and device and readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101777177A (en) * | 2009-12-29 | 2010-07-14 | 上海维宏电子科技有限公司 | Attenuation filter-based metal artifact removing mixed reconstruction method for CT images |
| CN103310432A (en) * | 2013-06-25 | 2013-09-18 | 西安电子科技大学 | Computerized Tomography (CT) image uniformization metal artifact correction method based on four-order total-variation shunting |
| CN103617598A (en) * | 2013-11-10 | 2014-03-05 | 北京工业大学 | Track-based CT image metal artifact removing method |
| US20160125625A1 (en) * | 2014-10-30 | 2016-05-05 | Institute For Basic Science | Method for reducing metal artifact in computed tomography |
| CN106683144A (en) * | 2016-12-30 | 2017-05-17 | 上海联影医疗科技有限公司 | Image iteration reconstruction method and device |
-
2018
- 2018-09-13 CN CN201811067675.2A patent/CN109472836B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101777177A (en) * | 2009-12-29 | 2010-07-14 | 上海维宏电子科技有限公司 | Attenuation filter-based metal artifact removing mixed reconstruction method for CT images |
| CN103310432A (en) * | 2013-06-25 | 2013-09-18 | 西安电子科技大学 | Computerized Tomography (CT) image uniformization metal artifact correction method based on four-order total-variation shunting |
| CN103617598A (en) * | 2013-11-10 | 2014-03-05 | 北京工业大学 | Track-based CT image metal artifact removing method |
| US20160125625A1 (en) * | 2014-10-30 | 2016-05-05 | Institute For Basic Science | Method for reducing metal artifact in computed tomography |
| CN106683144A (en) * | 2016-12-30 | 2017-05-17 | 上海联影医疗科技有限公司 | Image iteration reconstruction method and device |
Non-Patent Citations (1)
| Title |
|---|
| 柯丽 等: "《基于滤波反投影的脑磁感应迭代重建算法研究》", 《仪器仪表学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110335325A (en) * | 2019-06-27 | 2019-10-15 | 深圳安科高技术股份有限公司 | A kind of CT image rebuilding method and its system |
| CN111080740A (en) * | 2019-12-27 | 2020-04-28 | 上海联影医疗科技有限公司 | Image correction method, device, equipment and medium |
| CN111080740B (en) * | 2019-12-27 | 2023-06-16 | 上海联影医疗科技股份有限公司 | Image correction method, device, equipment and medium |
| CN112991482A (en) * | 2021-04-12 | 2021-06-18 | 明峰医疗系统股份有限公司 | GPU-based rapid reconstruction imaging method and device and readable storage medium |
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