CN109472836B - Artifact correction method in CT iterative reconstruction - Google Patents

Artifact correction method in CT iterative reconstruction Download PDF

Info

Publication number
CN109472836B
CN109472836B CN201811067675.2A CN201811067675A CN109472836B CN 109472836 B CN109472836 B CN 109472836B CN 201811067675 A CN201811067675 A CN 201811067675A CN 109472836 B CN109472836 B CN 109472836B
Authority
CN
China
Prior art keywords
projection
image
iterative reconstruction
reconstruction
corrected
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811067675.2A
Other languages
Chinese (zh)
Other versions
CN109472836A (en
Inventor
马建华
徐宗本
陶熙
王永波
靖稳峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xi'an Institute Of Big Data And Artificial Intelligence
Original Assignee
Xi'an Institute Of Big Data And Artificial Intelligence
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xi'an Institute Of Big Data And Artificial Intelligence filed Critical Xi'an Institute Of Big Data And Artificial Intelligence
Priority to CN201811067675.2A priority Critical patent/CN109472836B/en
Publication of CN109472836A publication Critical patent/CN109472836A/en
Application granted granted Critical
Publication of CN109472836B publication Critical patent/CN109472836B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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

The invention discloses an artifact correction method in CT iterative reconstruction, which specifically comprises the steps of obtaining original projection data y of a digital tomography device, reconstructing the obtained projection data y by adopting a filtering back-projection algorithm, and obtaining a corrected image I1For the corrected image I1Preprocessing to obtain an image correction template I2To the image correction template I2Carrying out forward projection to obtain a projection correction template y1Using a projection correction template y1Correcting the original projection data y to obtain the corrected projection
Figure DDA0001798718920000011
And corrected projection
Figure DDA0001798718920000012
Compared with the prior art, the artifact correction method in CT iterative reconstruction is simple to operate, can effectively remove the artifact caused by rFOV in CT iterative reconstruction, and does not cause the deviation of the CT value of the image in the reconstruction result.

Description

Artifact correction method in CT iterative reconstruction
Technical Field
The invention belongs to the technical field of image processing methods of medical images, relates to a CT image correction method, and particularly relates to an artifact correction method in CT iterative reconstruction.
Background
The Filtered Back Projection (FBP) algorithm has been the dominant reconstruction method for commercial CT, but the FBP algorithm is not suitable for reconstructing projections with severe noise (i.e., low dose CT data). Therefore, Iterative Reconstruction (IR) becomes a research hotspot in recent years, because the IR algorithm can assist Iterative Reconstruction by constructing a reasonable statistical model and introducing different prior information according to different acquired data, excellent imaging quality can be obtained. Over the past few years, several commercial IR technologies (e.g., ASIR by GE Healthcare, SAFIRE by Siemens Healthcare) have been released by different CT vendors.
However, during the Reconstruction process of a CT imaging apparatus (e.g., a medical CT apparatus), some parts of the body (e.g., the shoulder or the pelvis) may exceed the Reconstruction field of view (rFOV), and if the original projection is directly subjected to Iterative Reconstruction (IR), there may be inconsistency between the current projection and the measured projection due to the tissue outside the rFOV during the Iterative Reconstruction process, and thus artifacts in the reconstructed image and deviations of the CT values may occur.
Disclosure of Invention
The invention provides an artifact correction method in CT iterative reconstruction, which specifically comprises the following steps:
step 1, acquiring original projection data y of digital tomography equipment;
step 2, reconstructing the projection data y obtained in the step 1 by adopting a filtering back projection algorithm to obtain a corrected image I1；
Step 3, correcting the image I obtained in the step 21Preprocessing to obtain an image correction template I2；
Step 4, correcting the image template I obtained in the step 32Carrying out forward projection to obtain a projection correction template y1；
Step 5, utilizing the projection correction template y obtained in the step 41Correcting the original projection data y to obtain the corrected projection
Figure BDA0001798718900000021
Step 6, projecting the corrected projection obtained in the step 5
Figure BDA0001798718900000022
And carrying out iterative reconstruction to obtain a final image mu to be reconstructed.
The method is also characterized in that the method,
when the filtering back projection algorithm is adopted for reconstruction in the step 2, the reconstructed visual field is the same as the scanning visual field during projection data acquisition.
The step 3 specifically comprises the following steps:
3.1, for the corrected image I1Zeroing the pixels in the iterative reconstruction visual field region, and reserving the pixel values outside the reconstruction visual field to obtain a preliminary correction image
Figure BDA0001798718900000023
3.2, to the preliminary correction image
Figure BDA0001798718900000024
Carrying out threshold processing to obtain an image correction template I2；
The specific way of the threshold processing is formula (1):
Figure BDA0001798718900000025
where t represents an adjustable threshold parameter.
Step 5 adopts formula (2):
Figure BDA0001798718900000031
the iterative reconstruction described in step 6 specifically employs a penalty weighted least squares method based on non-local similarity regularization.
The invention has the beneficial effects that: compared with the prior art, the artifact correction method in CT iterative reconstruction is simple to operate, can effectively remove the artifact caused by rFOV in CT iterative reconstruction, and does not cause the deviation of the CT value of the image in the reconstruction result.
Drawings
FIG. 1 is a schematic flow chart of an artifact correction method in CT iterative reconstruction according to the present invention;
FIG. 2a shows the result of a fan beam CT reconstruction using a prior art FBP algorithm;
FIG. 2b shows the result of a fan-beam CT reconstruction using an iterative algorithm;
fig. 2c shows the result of reconstructing a fan-beam CT by using the artifact correction method in CT iterative reconstruction according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses an artifact correction method in CT iterative reconstruction, which specifically comprises the following steps as shown in figure 1:
step 1, acquiring original projection data y of digital tomography equipment;
step 2, reconstructing the projection data y obtained in the step 1 by adopting a filtering back projection algorithm to obtain a corrected image I1(ii) a The reconstructed field of view is the same as the scan field of view at the time of projection data acquisition.
Step 3, correcting the image I obtained in the step 21Preprocessing to obtain an image correction template I2(ii) a The method comprises the following steps:
3.1, for the corrected image I1Zeroing the pixels in the iterative reconstruction visual field region, and reserving the pixel values outside the reconstruction visual field to obtain a preliminary correction image
Figure BDA0001798718900000041
3.2, to the preliminary correction image
Figure BDA0001798718900000042
Carrying out threshold processing to obtain an image correction template I2；
The specific way of the threshold processing is formula (1):
Figure BDA0001798718900000043
where t represents an adjustable threshold parameter.
Step 4, correcting the image template I obtained in the step 32Carrying out forward projection to obtain a projection correction template y1；
Step 5, utilizing the projection correction template y obtained in the step 41Correcting the original projection data y to obtain the corrected projection
Figure BDA0001798718900000044
Specifically, formula (2) is adopted:
Figure BDA0001798718900000045
step 6, adopting a punishment weighted least square method based on non-local similarity regularization to perform projection after correction obtained in the step 5
Figure BDA0001798718900000046
And carrying out iterative reconstruction to obtain a final image mu to be reconstructed.
Examples
Taking fan-beam CT as an example:
step 1, acquiring original projection data y of digital tomography equipment, wherein the data is acquired by a detector;
step 2, reconstructing the projection data y obtained in the step 1 by adopting a filtering back projection algorithm (FBP) to obtain a corrected image I1(ii) a The reconstructed field of view is the same as the scan field of view at the time of projection data acquisition.
Specifically, the treatment is performed according to the formula (3):
Figure BDA0001798718900000051
wherein D' is a weight factor, gamma and theta respectively represent the corresponding beam angle and X-ray exposure angle of the detector unit, and hf an(. cndot.) is the convolution kernel used in filtering in the fan-beam FBP.
Step 3, correcting the image I obtained in the step 21Preprocessing to obtain an image correction template I2(ii) a The method comprises the following steps:
3.1, for the corrected image I1In iterationZeroing the pixels in the reconstructed visual field region, and reserving the pixel values outside the reconstructed visual field to obtain a preliminary correction image
Figure BDA0001798718900000052
3.2, to the preliminary correction image
Figure BDA0001798718900000053
Carrying out threshold processing to obtain an image correction template I2(ii) a The purpose of this step is to
Figure BDA0001798718900000054
The pixel value of the air region in (b) is set to zero to reduce the error introduced in the FBP reconstruction process.
The specific way of the threshold processing is formula (1):
Figure BDA0001798718900000055
where t represents an adjustable threshold parameter.
Step 4, correcting the image template I obtained in the step 32Carrying out forward projection to obtain a projection correction template y1(ii) a Its formula is expressed as y1＝G×I2Wherein G is a projection operator in CT imaging;
step 5, utilizing the projection correction template y obtained in the step 41Correcting the original projection data y to obtain the corrected projection
Figure BDA0001798718900000056
Specifically, formula (2) is adopted:
Figure BDA0001798718900000057
step 6, adopting a punishment weighted least square method based on non-local similarity regularization to perform projection after correction obtained in the step 5
Figure BDA0001798718900000058
Carrying out iterative reconstruction to obtain a final image mu to be reconstructed;
the target equation for this step is:
Figure BDA0001798718900000061
wherein mu represents the image to be reconstructed, W is a weighting matrix, the diagonal elements of which are the inverse of the variance in the projection data, beta is an adjustable penalty parameter, and W is an adjustable penalty parameterjkThe weighting factor can be calculated according to the non-local similarity of the images.
In step 6, the obtained corrected projection image is subjected to
Figure BDA0001798718900000062
In iterative reconstruction, any iterative algorithm may be used, including but not limited to penalty weighted least squares based on non-local similarity regularization used in embodiments of the present invention.
Through the mode, the artifact correction method in CT iterative reconstruction effectively removes the artifact caused by rFOV in CT iterative reconstruction, and the reconstruction result does not cause the deviation of the CT value of the image, compared with the prior method, as shown in fig. 2 a-2 c, fig. 2a is the result of reconstructing fan-beam CT by adopting the prior FBP algorithm, and the image has no artifact but contains noise in the reconstruction result; FIG. 2b shows the result of a fan-beam CT reconstruction with an iterative algorithm, in which the image noise is reduced but artifacts appear at the edges of the field of view; fig. 2c shows the result of reconstructing a fan-beam CT by using the artifact correction method in CT iterative reconstruction of the present invention, in which the image noise is reduced and the artifact is eliminated.

Claims (2)

1. A method for correcting artifacts in CT iterative reconstruction is characterized by comprising the following steps:
step 1, acquiring original projection data y of digital tomography equipment;
step 2, reconstructing the projection data y obtained in the step 1 by adopting a filtering back projection algorithm to obtain a corrected image I1；
When the filtering back projection algorithm is adopted for reconstruction in the step 2, the reconstructed visual field is the same as the scanning visual field during projection data acquisition;
step 3, correcting the image I obtained in the step 21Preprocessing to obtain an image correction template I2；
The step 3 specifically comprises the following steps:
3.1, for the corrected image I1Zeroing the pixels in the iterative reconstruction visual field region, and reserving the pixel values outside the reconstruction visual field to obtain a preliminary correction image
Figure FDA0002576893800000011
3.2, to the preliminary correction image
Figure FDA0002576893800000012
Carrying out threshold processing to obtain an image correction template I2；
The specific way of the threshold processing is formula (1):
Figure FDA0002576893800000013
wherein t represents an adjustable threshold parameter;
step 4, correcting the image template I obtained in the step 32Carrying out forward projection to obtain a projection correction template y1；
Step 5, utilizing the projection correction template y obtained in the step 41Correcting the original projection data y to obtain the corrected projection
Figure FDA0002576893800000014
Step 6, projecting the corrected projection obtained in the step 5
Figure FDA0002576893800000015
And (3) performing iterative reconstruction to obtain a final image mu to be reconstructed, wherein the iterative reconstruction in the step (6) specifically adopts a penalty weighted least square method based on non-local similarity regularization.
2. The method for correcting artifacts in CT iterative reconstruction according to claim 1, wherein the formula (2) is adopted in step 5:
Figure FDA0002576893800000021
CN201811067675.2A 2018-09-13 2018-09-13 Artifact correction method in CT iterative reconstruction Active CN109472836B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811067675.2A CN109472836B (en) 2018-09-13 2018-09-13 Artifact correction method in CT iterative reconstruction

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811067675.2A CN109472836B (en) 2018-09-13 2018-09-13 Artifact correction method in CT iterative reconstruction

Publications (2)

Publication Number Publication Date
CN109472836A CN109472836A (en) 2019-03-15
CN109472836B true CN109472836B (en) 2021-02-02

Family

ID=65664639

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811067675.2A Active CN109472836B (en) 2018-09-13 2018-09-13 Artifact correction method in CT iterative reconstruction

Country Status (1)

Country Link
CN (1) CN109472836B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110335325B (en) * 2019-06-27 2023-07-21 深圳安科高技术股份有限公司 CT image reconstruction method and system
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
CN112991482B (en) * 2021-04-12 2023-03-24 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 (4)

* Cited by examiner, † Cited by third party
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
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

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101591381B1 (en) * 2014-10-30 2016-02-04 기초과학연구원 Method for reducing metal artifact in computed tomography

Patent Citations (4)

* Cited by examiner, † Cited by third party
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
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)

* Cited by examiner, † Cited by third party
Title
《基于滤波反投影的脑磁感应迭代重建算法研究》;柯丽 等;《仪器仪表学报》;20161115;第37卷(第11期);全文 *

Also Published As

Publication number Publication date
CN109472836A (en) 2019-03-15

Similar Documents

Publication Publication Date Title
CN107481297B (en) CT image reconstruction method based on convolutional neural network
CN106683144B (en) Image iterative reconstruction method and device
La Riviere et al. Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing
CN109472836B (en) Artifact correction method in CT iterative reconstruction
US7840249B2 (en) Clinical micro-CT (CMCT) methods, techniques and apparatus
CN102947861B (en) For reducing the method and system of noise in low dosage computer tomography
US20060285737A1 (en) Image-based artifact reduction in PET/CT imaging
CN105046675B (en) The method that metal artifacts in KVCT images are eliminated based on MVCT images
US8855394B2 (en) Methods and apparatus for texture based filter fusion for CBCT system and cone-beam image reconstruction
WO2008147416A1 (en) Methods and systems to facilitate correcting gain fluctuations in image
CN110458908B (en) Method for iteratively reconstructing hyper-field CT image based on limited angle
CN103810733A (en) Statistical iteration reconstruction method of sparse-angle X-ray CT (electronic computer X-ray tomography technique) image
Fournié et al. CT field of view extension using combined channels extension and deep learning methods
CN112767273B (en) Low-dose CT image restoration method and system applying feature decoupling
CN112070856B (en) Limited angle C-arm CT image reconstruction method based on non-subsampled contourlet transform
CN112070704B (en) Dual regularization limited angle CT image reconstruction method based on tight wavelet frame
CN109685871B (en) Image reconstruction method, computer device, and computer-readable storage medium
KR20200083122A (en) Low Dose Cone Beam Computed Tomography Imaging System Using Total Variation Denoising Technique
CN112116677B (en) Low-dose CT reconstruction method based on low-dimensional manifold priori
Zhou et al. Low-dose CT reconstruction by self-supervised learning in the projection domain
KR102248191B1 (en) Method and apparatus for correcting cone-beam artifacts in a cone-beam computed tomography image, cone-beam computed tomography apparatus including the same
Thanasupsombat et al. Investigation of image quality on short-scan CBCT reconstruction
CN113570586A (en) Method and device for creating and processing CT image of neural network system
Sun et al. A motion compensation approach for dental cone-beam region-of-interest imaging
Choi et al. Statistical image restoration for low-dose CT using convolutional neural networks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant