CN112288762B - Discrete iteration reconstruction method for limited angle CT scanning - Google Patents

Discrete iteration reconstruction method for limited angle CT scanning Download PDF

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CN112288762B
CN112288762B CN202011100141.2A CN202011100141A CN112288762B CN 112288762 B CN112288762 B CN 112288762B CN 202011100141 A CN202011100141 A CN 202011100141A CN 112288762 B CN112288762 B CN 112288762B
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CN112288762A (en
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黄魁东
杨富强
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Northwestern Polytechnical University
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Abstract

The invention provides a discrete iterative reconstruction method of limited angle CT scanning, which can adaptively acquire optimal gray information and a segmentation threshold according to an initial reconstruction image of limited scanning projection, improve edge contour distortion caused by limited angle reconstruction and finish high-quality reconstruction. The method provided by the invention is suitable for the limited angle CT projection reconstruction of the measured object with any complex structure, has good reliability, stability and universality, can reduce reconstruction artifacts and image contour distortion to a great extent, and obviously improves the limited angle CT scanning reconstruction quality.

Description

Discrete iteration reconstruction method for limited angle CT scanning
Technical Field
The invention relates to a discrete iteration reconstruction method for limited angle CT scanning, belonging to the technical field of medical CT imaging and industrial CT nondestructive testing.
Background
Computer tomography (Computed Tomography, CT) is used as an advanced medical imaging and industrial nondestructive detection technology, can detect defects in the object or measure internal dimensions without damaging the object, is visual in imaging and high in resolution, and has unique advantages in nondestructive detection of complex objects, such as medical diagnosis and treatment, safety inspection, product quality detection control and the like.
The actual X-ray imaging detection process is affected by various factors, the health of patients is generally considered in the medical field, the irradiation dose of the patients is reduced as much as possible or the scanning time is shortened as a criterion, the detection site in the industrial field is often limited by the geometric structure of a detection target, the size of a detector, the detection space, the equipment condition and the like, and complete scanning cannot be performed, so that limited-angle scanning reconstruction becomes the focus of attention of CT detection technology. The limited angle CT scanning angle range is smaller than the theoretical requirement of accurate reconstruction, belongs to the problem of incomplete projection reconstruction, and the direct reconstruction result shows the defects of incomplete structure, fuzzy boundary at a specific angle, low contrast resolution, serious reconstruction artifact and the like, and brings great difficulty to CT image diagnosis, defect identification and other applications, so that the realization of the high-quality imaging technology of the limited angle CT scanning reconstruction is particularly important.
To solve the CT limited angle scan reconstruction problem, some prior information, such as non-negativity, sparsity, contour or boundary information, is often used as a constraint to solve the problem. At present, the existing limited angle CT reconstruction algorithm mainly comprises two ideas: one is to use projection data recovery method, and supplement the missing projection data by interpolation or space transformation; the other is to apply constraint limits to the image according to some known conditions in the reconstruction process, such as non-negative limitation of the reconstructed image, limited reconstructed image area, symmetry of the projection image, CAD design model, gradient sparsity, TV constraint or some constraints on structural materials, etc. The iterative algorithm is more effective and practical for solving the incomplete projection problem, the prior knowledge of the image to be reconstructed is converted into the constraint condition, the CT image reconstruction problem is converted into the optimization problem with the constraint condition, and the optimization problem can be solved through a series of mathematical means. The research discovers that the image can be recovered from less projection data by introducing proper constraint conditions and priori knowledge in the process of reconstructing the iterative algorithm, the accuracy of the reconstructed image is improved, the robustness of the algorithm to noise is increased, and remarkable research results are obtained for improving the limited projection reconstruction. According to the sparsity of image gradients, in recent years, it has been found that by using the fact that the material of an object to be reconstructed is limited, discrete gray values are introduced as priori knowledge into reconstruction constraints, images can be recovered from fewer projection data, the accuracy of reconstructed images is improved, and more people begin to pay attention to discrete algebraic reconstruction techniques. In general, image reconstruction algorithms for incomplete projection data have been a research hotspot in the field of image processing. The discrete iteration introduces gray information as constraint into the reconstruction process, and compared with other iterative reconstruction algorithms, the imaging quality is greatly improved, but main technical defects in practical application include:
(1) The prior gray information in the practical application of the discrete iterative algorithm is often difficult to accurately estimate;
(2) The reconstruction algorithm process is greatly influenced by a limited angle range, and a local optimal solution exists in the objective function;
(3) The discrete iterative algorithm relates to image segmentation, and the accuracy of a segmentation threshold value has a large influence on a reconstruction result.
In summary, the existing reconstruction algorithm has certain requirements on the projection range and priori information, and the limited projection reconstruction accuracy is low, so that the actual application requirements of CT high-precision medical imaging and the industrial precision nondestructive detection requirements can not be met.
Disclosure of Invention
Aiming at the practical problems of low image accuracy, large contour error, difficult estimation of gray information and the like of limited angle CT scanning reconstruction, the invention provides a discrete iteration reconstruction method of limited angle CT scanning, which can adaptively acquire optimal gray information and a segmentation threshold value according to an initial reconstruction image of limited scanning projection, improve edge contour distortion caused by limited angle reconstruction, finish high-quality reconstruction and improve reconstruction efficiency.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
step 1: acquiring limited angle CT scanning projection, and reconstructing according to the projection to obtain an initial image f 0 ；
Step 2: for the current image f 0 Performing multi-threshold segmentation to obtain a segmentation threshold tau 1 ,τ 2 ,......,τ l ；
Step 3: calculating the gray average value of different material categories in the current segmented image, and recording as mu 1 ,μ 2 ,......,μ l Wherein subscripts 1,2 are assigned to each of the group, l corresponds to the first material class;
step 4: for gray scale average mu 1 ,μ 2 ,......,μ l Correcting to obtain corrected gray average value
Figure GDA0004124910310000021
Step 5: obtaining optimized gray value by L2 norm minimization according to the forward/backward method, and marking as
Figure GDA0004124910310000022
Step 6: using optimized gray values
Figure GDA0004124910310000023
Obtaining optimized segmentation threshold value by L2 norm minimization according to the forward/backward method>
Figure GDA0004124910310000031
Step 7: using threshold values
Figure GDA0004124910310000032
Dividing the current image to obtain a divided image S;
step 8: selecting an edge point set from S
Figure GDA0004124910310000033
And fixed point set->
Figure GDA0004124910310000034
Will->
Figure GDA0004124910310000035
Endow->
Figure GDA0004124910310000036
Find->
Figure GDA0004124910310000037
Corresponding residual projection r, and finishing edge point set updating by utilizing r to obtain +.>
Figure GDA0004124910310000038
Step 9: merging
Figure GDA0004124910310000039
And->
Figure GDA00041249103100000310
An image, smoothing the image;
step 10: and judging whether the termination condition is met, if not, jumping to the step 2 to continue execution, and if so, outputting a discrete iteration reconstructed image.
In the above step 4, the gray scale value μ is averaged 1 ,μ 2 ,......,μ l Correcting to obtain corrected gray average value
Figure GDA00041249103100000311
The specific steps of (a) include:
(1) Obtaining a projection weight coefficient matrix w= (W) ij ) N×N Where N represents the size of the reconstructed image, i, j represents the pixel coordinates of the reconstructed image;
(2) Using models
Figure GDA00041249103100000312
Calculating a relative projection error Δp, where p ij ，w ij ，f ij Respectively representing the projection, the weight coefficient and the numerical value at the position of the coordinate i and the position of the coordinate j of the reconstructed image;
(3) According to the model
Figure GDA00041249103100000313
A corrected gray average value is obtained.
In the above step 5, the optimized gray value is obtained by L2 norm minimization according to the forward/backward method, and is recorded as
Figure GDA00041249103100000314
The specific steps of (a) include:
(1) Setting the step delta 1 For correcting gray scale average value
Figure GDA00041249103100000315
According to->
Figure GDA00041249103100000316
Performing forward and backward adjustment q times to obtain an adjusted gray level mean +.>
Figure GDA00041249103100000317
Where k=1, 2, &..i.l, m=1, 2, &..2 q; />
(2) Will be
Figure GDA00041249103100000318
Imparting current segmentationAn image, noted->
Figure GDA00041249103100000319
(3) For a pair of
Figure GDA00041249103100000320
Forward projection, i.e.)>
Figure GDA00041249103100000321
Making it differ from the actual projection P by the L2 norm minimization function +.>
Figure GDA0004124910310000041
Obtaining optimized gray level mean->
Figure GDA0004124910310000042
Where W represents the projection matrix.
In the above step 6, the directions are alternated, and the optimized gray value is used
Figure GDA0004124910310000043
Obtaining optimized segmentation threshold value by L2 norm minimization according to the forward/backward method>
Figure GDA0004124910310000044
The specific steps of (a) include:
(1) Setting the step delta 2 Dividing threshold τ 1 ,τ 2 ,......,τ l According to
Figure GDA0004124910310000045
Performing advance and retreat adjustment q times; obtaining an adjusted segmentation threshold +.>
Figure GDA0004124910310000046
Where k=1, 2, &..i.l, m=1, 2, &..2 q;
(2) By means of
Figure GDA0004124910310000047
Dividing the current image, and recording as +.>
Figure GDA0004124910310000048
(3) For a pair of
Figure GDA0004124910310000049
Forward projection, i.e.)>
Figure GDA00041249103100000410
Making it differ from the actual projection P by the L2 norm minimization function +.>
Figure GDA00041249103100000411
Acquiring an optimized segmentation threshold->
Figure GDA00041249103100000412
The beneficial effects of the invention are as follows: the discrete iterative reconstruction method of the limited angle CT scanning, provided by the invention, is suitable for the limited angle CT scanning reconstruction of the measured object with any complex structure, has good reliability, stability and universality, can reduce reconstruction artifacts and image contour distortion to a great extent, and obviously improves the limited angle CT scanning reconstruction quality.
The invention will be described in detail below with reference to the drawings and the detailed description.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention.
Detailed Description
The method is used for continuously sampling a single-material industrial object in a limited angle range by the existing industrial cone beam CT equipment (the X-ray source is MXR-451HP/11 of Comet, the flat panel detector is XRD 1621an15 ES of Perkinelmer), and the method is used for carrying out the following steps on a limited projection cone beam CT reconstruction method:
step 1: through industrial cone beam CT equipment, the voltage of a ray source is selected to be 200kV, the current is selected to be 1.6mA, and the scanning geometrical parameters are as follows: the distance from the ray source to the detector is 1212.6mm, the distance from the ray source to the rotation center is 925.1mm, and the reconstruction resolution is 512×512; for single material industrial parts, the angle is 0 DEG to the ultra-range90 projections of CT projection are obtained within the range of 180 degrees, and an iterative reconstruction SIRT algorithm is selected to obtain an initial image f 0 。
Step 2: for the current image f 0 Dividing by using an OTSU clustering algorithm to obtain a division threshold value tau=0.136;
step 3: calculating the gray average value of the current segmented image to obtain;
step 4: correcting the gray average value mu=0.272 to obtain a corrected gray average value
Figure GDA00041249103100000413
The method comprises the following specific steps:
(1) Obtaining a projection weight coefficient matrix w= (W) ij ) N×N Where n=512 represents the size of the reconstructed image, i, j represents the pixel coordinates of the reconstructed image;
(2) Using models
Figure GDA0004124910310000051
Calculate the relative projection error Δp=0.281, where p ij ，w ij ，f ij Respectively representing the projection, the weight coefficient and the numerical value at the position of the coordinate i and the position of the coordinate j of the reconstructed image;
(3) According to the model
Figure GDA0004124910310000052
A corrected gray average value is obtained.
Step 5: obtaining optimized gray value by L2 norm minimization according to the forward/backward method
Figure GDA0004124910310000053
The method comprises the following steps:
(1) Setting the step delta 1 =0.025 for correction of gray scale mean value
Figure GDA0004124910310000054
According to
Figure GDA0004124910310000055
Performing forward and backward adjustment for 2 times to obtain an adjusted gray level mean +.>
Figure GDA0004124910310000056
Wherein m=1, 2,3,4;
(2) Will be
Figure GDA0004124910310000057
Giving the current segmented image, noted +.>
Figure GDA0004124910310000058
(3) For a pair of
Figure GDA0004124910310000059
Forward projection, i.e.)>
Figure GDA00041249103100000510
Making it differ from the actual projection P by the L2 norm minimization function +.>
Figure GDA00041249103100000511
Obtaining optimized gray level mean->
Figure GDA00041249103100000512
Where W represents the projection matrix.
Step 6: using optimized gray values
Figure GDA00041249103100000513
Obtaining optimized segmentation threshold by L2 norm minimization according to a forward-backward method
Figure GDA00041249103100000523
The specific steps of (a) include:
(1) Setting the step delta 2 Division threshold τ=0.136 as per =0.005
Figure GDA00041249103100000514
Performing advance and retreat adjustment for 2 times; obtaining an adjusted segmentation threshold +.>
Figure GDA00041249103100000515
Wherein m=1, 2,3,4;
(2) By means of
Figure GDA00041249103100000516
Dividing the current image, and recording as +.>
Figure GDA00041249103100000517
(3) For a pair of
Figure GDA00041249103100000518
Forward projection, i.e.)>
Figure GDA00041249103100000519
Making it differ from the actual projection P by the L2 norm minimization function +.>
Figure GDA00041249103100000520
Acquiring an optimized segmentation threshold->
Figure GDA00041249103100000521
Step 7: using threshold values
Figure GDA00041249103100000522
Dividing the current image to obtain a divided image S;
step 8: selecting a 3 x 3 gray scale window to select a set of edge points from the segmented image S
Figure GDA0004124910310000061
And fixed point set->
Figure GDA0004124910310000062
Will be
Figure GDA0004124910310000063
Endow->
Figure GDA0004124910310000064
Find->
Figure GDA0004124910310000065
Corresponding residual projection r, and finishing edge point set updating by utilizing r to obtain +.>
Figure GDA0004124910310000066
Step 9: merging
Figure GDA0004124910310000067
And->
Figure GDA0004124910310000068
Image, smoothing parameter 0.3 is set, through model +.>
Figure GDA0004124910310000069
Finishing the smooth operation;
step 10: setting the iteration times of the loop as 100 times, judging whether the loop is completed or not, if the loop is not completed, jumping to the step 2 to continue execution, and if the loop is completed, outputting a discrete iteration reconstructed image.

Claims (5)

1. A discrete iteration reconstruction method of limited angle CT scanning is characterized by comprising the following steps:
step 1: acquiring limited angle CT scanning projection, and reconstructing according to the projection to obtain an initial image f 0 ；
Step 2: for the current image f 0 Performing multi-threshold segmentation to obtain a segmentation threshold tau 1 ,τ 2 ,......,τ l ；
Step 3: calculating the gray average value of different material categories in the current segmented image, and recording as mu 1 ,μ 2 ,......,μ l Wherein subscripts 1,2 are assigned to each of the group, l corresponds to the first material class;
step 4: for gray scale average mu 1 ,μ 2 ,......,μ l Correcting to obtain corrected gray average value
Figure FDA0004124910300000011
Step 5: obtaining optimized gray value by L2 norm minimization according to the forward/backward method, and marking as
Figure FDA0004124910300000012
Step 6: using optimized gray values
Figure FDA0004124910300000013
Obtaining optimized segmentation threshold by L2 norm minimization according to a forward-backward method
Figure FDA0004124910300000014
Step 7: using threshold values
Figure FDA0004124910300000015
Dividing the current image to obtain a divided image S;
step 8: selecting an edge point set from S
Figure FDA0004124910300000016
And fixed point set->
Figure FDA0004124910300000017
Will->
Figure FDA0004124910300000018
Endow->
Figure FDA0004124910300000019
Find->
Figure FDA00041249103000000110
Corresponding residual projection r, and finishing edge point set updating by utilizing r to obtain +.>
Figure FDA00041249103000000111
Step 9: merging
Figure FDA00041249103000000112
And->
Figure FDA00041249103000000113
An image, smoothing the image;
step 10: and judging whether the termination condition is met, if not, jumping to the step 2 to continue execution, and if so, outputting a discrete iteration reconstructed image.
2. A method of discrete iterative reconstruction of a limited angle CT scan according to claim 1, wherein: in the step 4, the gray scale average value mu 1 ,μ 2 ,......,μ l Correction is performed by first using a model
Figure FDA00041249103000000114
Calculating the relative projection error Δp and then according to the model +.>
Figure FDA00041249103000000115
Obtaining a corrected gray average value, wherein N represents the size of the reconstructed image; wherein p is ij ，w ij ，f ij The values at projection, weight coefficient, reconstructed image coordinates i, j are represented respectively.
3. A method of discrete iterative reconstruction of a limited angle CT scan according to claim 1, wherein: in the step 5, the optimized gray value is obtained by L2 norm minimization according to the advance-retreat method, and the step delta is firstly set 1 For correcting gray scale average value
Figure FDA0004124910300000021
According to->
Figure FDA0004124910300000022
Performing forward and backward adjustment q times to obtain an adjusted gray level mean +.>
Figure FDA0004124910300000023
Where k=1, 2, &..i.l, m=1, 2, &..2 q; then will->
Figure FDA0004124910300000024
The current segmented image is assigned and recorded as
Figure FDA0004124910300000025
Finally pair->
Figure FDA0004124910300000026
Forward projection, i.e.)>
Figure FDA0004124910300000027
Making it differ from the actual projection P by the L2 norm minimization function +.>
Figure FDA0004124910300000028
Obtaining optimized gray scale average
Figure FDA0004124910300000029
4. A method of discrete iterative reconstruction of a limited angle CT scan according to claim 1, wherein: in said step 6, the directions are alternated, using optimized gray values
Figure FDA00041249103000000210
Obtaining optimized segmentation threshold value by L2 norm minimization according to the forward/backward method>
Figure FDA00041249103000000211
First set step delta 2 Dividing threshold τ 1 ,τ 2 ,......,τ l According to
Figure FDA00041249103000000212
Proceeding q times of advancing and retreatingAdjusting; obtaining an adjusted segmentation threshold +.>
Figure FDA00041249103000000213
Where k=1, 2, &..i.l, m=1, 2, &..2 q; then use->
Figure FDA00041249103000000214
Segmenting the current image and recording as
Figure FDA00041249103000000215
Finally pair->
Figure FDA00041249103000000216
Forward projection, i.e.)>
Figure FDA00041249103000000217
Making it differ from the actual projection P by the L2 norm minimization function +.>
Figure FDA00041249103000000218
Obtaining an optimized segmentation threshold
Figure FDA00041249103000000219
5. A method of discrete iterative reconstruction of a limited angle CT scan according to claim 1, wherein: in this embodiment, a discrete iterative reconstruction method for limited angle CT scan is characterized in that:
(1) The algorithm has good reconstruction results for limited angle reconstruction of single-material and multi-material detection objects;
(2) The reconstruction using sparse sampled projections as limited angle projections is equally applicable.
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Publication number Priority date Publication date Assignee Title
CN115359049B (en) * 2022-10-19 2023-03-24 首都师范大学 Finite angle CT image reconstruction method and device based on nonlinear diffusion model

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1300938A (en) * 1999-12-22 2001-06-27 上海交通大学 Imaging method for X-CT finite-angle projection data
CN102103757A (en) * 2010-12-27 2011-06-22 中国科学院深圳先进技术研究院 Cone beam image rebuilding method and device
CN104240272A (en) * 2014-07-16 2014-12-24 中国人民解放军信息工程大学 TV minimization linear computed tomography (LCT) image reconstruction method based on pseudo-polar coordinates
CN106447782A (en) * 2016-08-02 2017-02-22 浙江工业大学 Face skin three-dimensional reconstruction method based on nuclear magnetic resonance image
CN107978005A (en) * 2017-11-21 2018-05-01 首都师范大学 It is a kind of based on protecting boundary diffusion and smooth limited view CT image reconstruction algorithm
CN108010099A (en) * 2017-12-04 2018-05-08 首都师范大学 A kind of limited angle sweep of X-ray multi-power spectrum CT and image iterative reconstruction method
CN109544655A (en) * 2018-11-19 2019-03-29 山东科技大学 A kind of X ray CT method for reconstructing of sea water pipe
CN109920020A (en) * 2019-02-27 2019-06-21 西北工业大学 A kind of Cone-Beam CT morbid state backprojection reconstruction artifact suppressing method
CN110260812A (en) * 2019-05-15 2019-09-20 广东工业大学 One kind determining blind source separating dual channel optical three-dimensional interference method and system based on deficient
CN110717956A (en) * 2019-09-30 2020-01-21 重庆大学 L0 norm optimization reconstruction method guided by finite angle projection superpixel
CN110751702A (en) * 2019-10-29 2020-02-04 上海联影医疗科技有限公司 Image reconstruction method, system, device and storage medium

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015168147A1 (en) * 2014-04-29 2015-11-05 Carl Zeiss X-ray Microscopy, Inc. Segmentation and spectrum based metal artifact reduction method and system

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1300938A (en) * 1999-12-22 2001-06-27 上海交通大学 Imaging method for X-CT finite-angle projection data
CN102103757A (en) * 2010-12-27 2011-06-22 中国科学院深圳先进技术研究院 Cone beam image rebuilding method and device
CN104240272A (en) * 2014-07-16 2014-12-24 中国人民解放军信息工程大学 TV minimization linear computed tomography (LCT) image reconstruction method based on pseudo-polar coordinates
CN106447782A (en) * 2016-08-02 2017-02-22 浙江工业大学 Face skin three-dimensional reconstruction method based on nuclear magnetic resonance image
CN107978005A (en) * 2017-11-21 2018-05-01 首都师范大学 It is a kind of based on protecting boundary diffusion and smooth limited view CT image reconstruction algorithm
CN108010099A (en) * 2017-12-04 2018-05-08 首都师范大学 A kind of limited angle sweep of X-ray multi-power spectrum CT and image iterative reconstruction method
CN109544655A (en) * 2018-11-19 2019-03-29 山东科技大学 A kind of X ray CT method for reconstructing of sea water pipe
CN109920020A (en) * 2019-02-27 2019-06-21 西北工业大学 A kind of Cone-Beam CT morbid state backprojection reconstruction artifact suppressing method
CN110260812A (en) * 2019-05-15 2019-09-20 广东工业大学 One kind determining blind source separating dual channel optical three-dimensional interference method and system based on deficient
CN110717956A (en) * 2019-09-30 2020-01-21 重庆大学 L0 norm optimization reconstruction method guided by finite angle projection superpixel
CN110751702A (en) * 2019-10-29 2020-02-04 上海联影医疗科技有限公司 Image reconstruction method, system, device and storage medium

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CT不完全投影数据重建算法综述;杨富强 等;《物理学报》;20141231;第63卷(第5期);1-12 *
X-ray Micro-Computed Tomography for Nondestructive Three-Dimensional (3D) X-ray Histology;Orestis L. Katsamenis 等;《The American Journal of Pathology》;20190831;第189卷(第8期);1608-1620 *
神经网络应用于有限角CT图像重建;赵绪新 等;《光电子技术与信息》;20050430;第18卷(第2期);74-77 *

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