WO2016106990A1

WO2016106990A1 - Ct imaging method and system

Info

Publication number: WO2016106990A1
Application number: PCT/CN2015/075669
Authority: WO
Inventors: 张蕴婉; 梁栋; 胡战利; 郑海荣
Original assignee: 深圳先进技术研究院
Priority date: 2014-12-30
Filing date: 2015-04-01
Publication date: 2016-07-07
Also published as: CN104637033B; CN104637033A

Abstract

A CT imaging method, comprising: scanning a target object to acquire a projection of all rays passing through the target object and a reconstruction system parameter; constructing an image reconstruction model; constructing an image restoration model; and alternately performing image reconstruction and image restoration according to the image reconstruction model and the image restoration model to obtain a reconstructed image. Further provided is a corresponding CT imaging system.

Description

CT imaging method and system

[Technical Field]

The present invention relates to a CT imaging method and system, and more particularly to an imaging method and imaging system that alternately perform image reconstruction and image restoration using alternating iterations.

【Background technique】

In recent years, research on high-quality reconstruction of CT images is mainly based on Compress Sensing (CS) algorithm. Studies have shown that if a signal is sparsely represented, the Total Variation (TV) minimization method can be used to accurately reconstruct the signal from a small amount of measurement data.

The CS-based iterative reconstruction algorithm is divided into two types: Algebraic Reconstruction Technique (ART) and Statistical Iterative Reconstruction (SIR). Among them, the core idea of the ART reconstruction algorithm is to regard the quadratic projection in the tomographic measurement as a projection set of unknown target functions through the cross section (from the literature: Gordon R, Bender R and Herman G T. Algebraic Reconstruction Techniques (Art) For 3-Dimensional Electron Microscopy and X-Ray Photography [J]. Journal of theoretical biology, 1970, 29: 471-81.).

As shown in FIG. 1, FIG. 1a is an image reconstructed by the ART-TV method for low-dose projection data; FIG. 1b and FIG. 1c are partial enlarged views of the reconstruction.

The most significant disadvantage of the ART algorithm is that it does not statistically model the data noise, and the reconstruction result is greatly affected by noise. In practical applications, especially in the case of low dose scanning, the data contains a lot of noise. Compared to the ART reconstruction algorithm, the SIR method is usually applied to the physical effects of the system model and the projection data and noise. The statistical Poisson property of the sound establishes the data fidelity term, and the introduction of the prior information through the regularization constraint item can improve the pathological reconstruction problem to a certain extent, and has better performance in reducing the image noise. The most typical SIR model is the Penalized Weighted Least-Square (PWLS) model. The PWLS model is constructed based on the independence of the projection channel projection data and the maximum a posteriori criterion (from the literature: Wang J, Li T, Lu H, et al. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography [J]. IEEE Trans Med Imaging, 2006, 25(10): 1272-83.).

As shown in FIG. 2, FIG. 2a shows an image reconstructed by the PWLS-TV method for low-dose projection data; and FIG. 2b and FIG. 2c are partial enlarged views of the reconstruction.

However, whether it is the ART algorithm or the SIR algorithm, while suppressing noise and artifacts, the original detail structure of the image is lost, and the image boundary is blurred. Therefore, maintaining the original boundary information while suppressing noise artifacts will become a need for high-quality reconstruction of CT images.

[Summary of the Invention]

Based on this, it is necessary to provide a simple and accurate CT imaging method and system.

A CT imaging method comprising:

Scan the target object, acquire all ray projections through the target object, and reconstruct system parameters;

Constructing an image reconstruction model;

Construct an image restoration model;

Image reconstruction and image restoration are alternately performed according to the image reconstruction model and the image restoration model to obtain a reconstructed image.

A CT imaging system comprising:

a scanning subsystem for scanning a target object, acquiring all ray projections through the target object, and reconstructing system parameters;

An image reconstruction subsystem for performing image reconstruction according to an image reconstruction model;

An image repair subsystem for performing image repair according to an image repair model;

An iterative subsystem for determining whether the image reconstruction subsystem and the image restoration subsystem alternately perform image reconstruction and image restoration have reached a predetermined number of alternating iterations; and for outputting the reconstructed image.

A CT internal region of interest imaging method, comprising:

Scanning the inner region of interest, acquiring all ray projections through the inner region of interest and reconstructing system parameters;

Constructing an image reconstruction model of the internal region of interest;

Constructing an image repair model of the internal region of interest;

According to the internal region of interest image reconstruction model and the internal region of interest image restoration model, the region of interest image reconstruction and the region of interest image restoration are alternately performed to obtain a region of interest reconstructed image.

A CT internal region of interest imaging system, comprising:

a scanning subsystem for scanning an internal region of interest, acquiring all ray projections through the internal region of interest, and reconstructing system parameters;

The image reconstruction subsystem is configured to perform image reconstruction of the internal region of interest according to the image reconstruction model of the internal region of interest;

The image repairing subsystem is configured to perform image repair of the internal region of interest according to the image repairing model of the internal region of interest;

An iterative subsystem is configured to determine whether the image reconstruction subsystem and the image restoration subsystem alternately perform image reconstruction of the region of interest and image restoration of the region of interest has reached a predetermined number of alternating iterations; and output the region of interest to reconstruct the image.

The CT imaging method and system of the invention scans a target object, acquires all ray projections through the target object, and reconstructs system parameters. By modeling the noise, constructs a CT statistical iterative reconstruction model, and performs detail repair and restoration loss in the iterative process. The boundary information is thus achieved for accurate reconstruction. The CT imaging method and system of the present invention are robust to noisy data and the reconstructed image boundaries are sharper.

[Description of the Drawings]

Figure 1 is an image reconstructed using the ART-TV method for low-dose projection data and an internal region of interest;

2 is an image reconstructed by the PWLS-TV method for low-dose projection data and an internal region of interest;

3 is a flow chart of a CT imaging method according to an embodiment of the present invention;

4 is an image reconstructed by a CT imaging method according to an embodiment of the present invention and an internal region of interest;

FIG. 5 is a flowchart of step 308 of FIG. 3 according to an embodiment of the present invention; FIG.

FIG. 6 is a schematic structural diagram of a CT imaging system according to an embodiment of the present invention.

【detailed description】

The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

It will be understood that the terms "first", "second" and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first client may be referred to as a second client, and similarly, a second client may be referred to as a first client, without departing from the scope of the present invention. Both the first client and the second client are clients, but they are not the same client.

As shown in FIG. 3, it is a flow of a CT imaging method according to an embodiment of the present invention.

Step 302, scanning the target object, acquiring all ray projections through the target object and reconstructing system parameters.

Specifically, the scanning method of the CT imaging system may be a parallel beam, a fan beam, or a cone beam; the scanning track may be a circular orbit, a spiral scanning track, or a multi-source static scanning track. The scanning dose can be a normal dose, either a low dose or a sparse angle (Sparse-view).

According to an embodiment of the invention, the reconstruction system parameter refers to a geometric parameter required for image reconstruction to construct a CT system matrix.

According to an embodiment of the invention, the target object refers to an inner region of interest of the scanned object or the scanned object (ie, the ROI region of the scanned object). For example, in step 302 above, the inner region of interest is scanned, all ray projections through the inner region of interest are acquired, and system parameters are reconstructed.

According to an embodiment of the invention, all of the ray projections through the target object are: all ray projections that are emitted from the light source, pass through the target object, and reach the detector.

In step 304, an image reconstruction model is constructed. For example, if the target object refers to the internal region of interest, an internal image reconstruction model of the region of interest is constructed, wherein the image reconstruction model for the internal region of interest can be referred to the related description of equation (1) and the image reconstruction model below. If the target object refers to the scanned object, an image reconstruction model of the scanned object is constructed, and an image reconstruction model relating to the scanned object can be referred to the related description of the formula (1) and the image reconstruction model hereinafter.

Specifically, according to an embodiment of the present invention, the image reconstruction model is obtained by the following formula (1):

Where y is the acquired ray projection data through the target object, x is the currently reconstructed CT image, A is the system matrix determined by the acquired system parameters, and B is a weighting factor containing noise statistics. T represents the transposition operation of the matrix, TV(x) represents the total variation function of the variable x, and λ is the regularization coefficient.

In an alternative other embodiment, to construct an image reconstruction model, the weight value matrix B can be set according to the CT machine projection data noise variance characteristic.

In alternative embodiments, to construct an image reconstruction model, TV(x) may be in a general form:

Where s and t respectively represent the number of rows and columns of the pixel of the image, wherein δ is a preset constant; it may also be other forms such as high-order TV.

By adding noise statistical information to the reconstruction objective function, noise filtering and artifact removal are simultaneously performed in the optimization solution process, so that the CT imaging method of the present invention is robust to noise, and the image reconstruction in the low dose scanning mode is effective. Better.

In step 306, an image repair model is constructed. If the target object refers to the internal region of interest, then an internal region of interest image repair model is constructed. For the image repair model of the internal region of interest, refer to the relevant descriptions of equation (2) and image repair model below. If the target object refers to the object to be scanned, an image repair model of the scanned object is constructed, and an image repair model relating to the scanned object can be referred to the related description of the formula (2) and the image repair model below.

According to an embodiment of the present invention, the image repair model is obtained by the following formula (2):

Θ(x)=Φ(x)+Ψ(x)..............................(2)

Where Φ(x) is the reconstructed image obtained by equation (1), Ψ(x) is the missing detail information, and Θ(x) is the repaired image.

Step 308, performing image reconstruction and image restoration alternately according to the image reconstruction model and the image restoration model to obtain a reconstructed image. For example, if the target object refers to the internal region of interest, then according to the internal region of interest image reconstruction model and the internal region of interest image restoration model, the image reconstruction of the region of interest and the image restoration of the region of interest are alternately performed to obtain the region of interest reconstruction. image. If the target object refers to the scanned object, image reconstruction and image restoration are alternately performed according to the image reconstruction model of the scanned object and the image restoration model of the scanned object to obtain a reconstructed image of the scanned object. The process of "alternating image reconstruction and image restoration to obtain reconstructed images" corresponding to the above two cases can be referred to the following.

According to an optional implementation manner of the present invention, the performing image reconstruction refers to performing an optimization solution by combining the reconstructed image reconstruction model, specifically referring to an iterative solution of the objective function by using a mathematical optimization method until reaching a preset Termination condition.

In one embodiment of the invention, an iterative method based on gradient descent can be employed. The specific form is as follows (3):

Where η represents an acceleration factor,

A TV gradient representing the currently reconstructed CT image x ⁿ , where n is a natural number representing the number of iterative operations.

The iteration initial value x ⁰ may be an all-zero or all-one image, or may be a filtered back-projection (FBP) method to reconstruct an image.

The above formula is executed cyclically, and the iterative operation is stopped when the number of loops reaches a preset number of times, and the obtained iterative operation result is taken as the final reconstructed image.

In addition, iterative solutions can be performed using methods such as conjugate gradient or parabolic substitution.

According to an optional implementation manner of the present invention, the performing image repair refers to repairing the lost details by combining the constructed image repair model, specifically referring to extracting the detailed information Ψ(x) lost during the reconstruction process, and adding the image To rebuild the image.

The extraction algorithm of Ψ(x) can use the following formula (4):

Where ν is the difference image, which may be the difference between the two iterations of the image before and after, or the difference between the images of several iterations, μ is the image structure information, and may be extracted from the original image by using an algorithm such as segmentation.

Represents a point multiplication operation.

In an embodiment of the present invention, the image reconstruction and image restoration are alternately performed, and the number of iterations may be determined according to a specific situation. For example, the reconstruction model can be iterated once and repaired once (where the difference image represents the image difference before and after the reconstruction model is iterated once), and the whole iteration is 50 times; the reconstruction model can also be iterated 10 times and repaired once (where The difference image represents the image difference before and after the reconstruction model is iterated 10 times, and the whole iteration is 20 times.

As shown in FIG. 4, FIG. 4a is an image reconstructed by the CT imaging method of the present invention for low-dose projection data; FIG. 4b and FIG. 4c are partial enlarged views. As can be seen from the arrows in Fig. 4b and Fig. 4c, according to the CT imaging method of the present invention, detail restoration is performed in an iterative process to restore the lost boundary information, so that the reconstructed image boundary is clearer.

As shown in FIG. 5, it is the flow of step 308 in FIG. 3 in accordance with an embodiment of the present invention.

Step 502: Perform image reconstruction according to the image reconstruction model.

Specifically, the image reconstruction may be performed by using the iterative method described in the foregoing formula (3), and the above formula (3) is executed cyclically, and the iterative operation is stopped when the number of loops reaches a preset number of times, and the obtained iterative operation result is obtained. As the final reconstructed image. According to other embodiments of the present invention, iterative solution can also be performed using methods such as conjugate gradient or parabolic substitution.

Step 504, performing image repair according to the image repair model.

Specifically, the detailed information Ψ(x) lost during the reconstruction process can be extracted by using the foregoing formula (4), and added to the reconstructed image, and the image repair is performed according to the image repair model of the foregoing formula (2).

In step 506, it is determined whether the predetermined number of alternating iterations has been reached. If not, step 502 and step 504 are still performed to perform image reconstruction and image restoration alternately.

The number of alternate iterations can be determined on a case-by-case basis. For example, the reconstruction model can be iterated once and repaired once (where the difference image represents the image difference before and after the reconstruction model is iterated once), and the whole iteration is 50 times; the reconstruction model can also be iterated 10 times and repaired once (where The difference image represents the image difference before and after the reconstruction model is iterated 10 times, and the whole iteration is 20 times.

Step 508, if it is determined in step 506 that the predetermined number of alternating iterations has been reached, a reconstructed image is obtained.

With the CT imaging method of the present invention, the reconstructed image is less noisy and the boundary is sharper.

The CT imaging system 600 includes a scanning subsystem 602, an image reconstruction subsystem 604, an image restoration subsystem 606, and an iterative subsystem 608.

The scanning subsystem 602 is used to scan a target object, acquire all ray projections through the target object, and reconstruct system parameters.

Specifically, the scanning subsystem 602 of the CT imaging system 600 may scan in a parallel beam, a fan beam, or a cone beam; the scanning track may be a circular orbit, a spiral scanning track, or a multi-source static scanning track. The scanning dose can be a normal dose, either a low dose or a sparse angle (Sparse-view).

According to an embodiment of the invention, the target object refers to an inner region of interest of the scanned object or the scanned object. For example, if the target object refers to an internal region of interest, then the scanning subsystem described above is used to scan the interior region of interest, acquire all ray projections through the interior region of interest, and reconstruct system parameters. If the target object refers to the object being scanned, then the scanning subsystem is used to scan the scanned object, acquire all ray projections by scanning the object, and reconstruct system parameters.

The image reconstruction subsystem 604 is configured to perform image reconstruction in accordance with the image reconstruction model. If the target object refers to the inner region of interest, the image reconstruction subsystem is configured to reconstruct the image of the inner region of interest according to the image reconstruction model of the inner region of interest. If the target object refers to the scanned object, the image reconstruction subsystem is configured to perform image reconstruction of the scanned object according to the image reconstruction model of the scanned object.

Specifically, according to an embodiment of the present invention, the image reconstruction model is obtained by the above formula (1).

By adding noise statistics to the reconstruction objective function, noise is simultaneously performed in the optimization solution process. Filtering and artifact removal make the CT imaging method of the present invention robust to noise and better when reconstructing in low dose scanning mode.

The image restoration subsystem 606 is configured to perform image restoration according to the image restoration model. For example, if the target object refers to the inner region of interest, the image repair subsystem is configured to perform image repair of the inner region of interest according to the image repair model of the inner region of interest. If the target object refers to the scanned object, the image repair subsystem is configured to perform image restoration of the scanned object according to the image repair model of the scanned object.

According to an embodiment of the present invention, in particular, the above-mentioned formula (4) may be used to extract the detail information Ψ(x) lost during the reconstruction process, and add it to the reconstructed image according to the foregoing formula (2). The image restoration model performs image restoration.

The iterative subsystem 608 is operative to determine if a predetermined number of alternate iterations have been reached, and if not, the iterative subsystem 608 instructs the image reconstruction subsystem 604 and the image restoration subsystem 606 to alternate image reconstruction and image restoration. If the predetermined number of alternating iterations has been reached, the reconstructed image is output. For example, if the target object refers to an internal region of interest, the iterative subsystem is configured to determine whether the image reconstruction subsystem and the image restoration subsystem alternately perform image reconstruction of the region of interest and image restoration of the region of interest has reached a predetermined alternating iteration. The number of times; and the reconstructed image for outputting the region of interest. If the target object refers to the scanned object, the iterative subsystem is configured to determine whether the image reconstruction subsystem and the image restoration subsystem alternately perform scanning object image reconstruction and scan object image restoration has reached a predetermined number of alternating iterations; Outputs a reconstructed image of the scanned object.

With the CT imaging system of the present invention, the reconstructed image is less noisy and the boundaries are sharper.

A person skilled in the art can understand that all or part of the process of implementing the foregoing embodiment can be completed by controlling related hardware by a computer program, and the program can be stored in a computer readable storage medium, the program When executed, the flow of an embodiment of the methods as described above may be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

The above-mentioned embodiments are merely illustrative of several embodiments of the present invention, and the description thereof is more specific and detailed, but is not to be construed as limiting the scope of the invention. It should be noted that a number of variations and modifications may be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, the scope of the invention should be determined by the appended claims.

Claims

Where ν is the difference image and μ is the image structure information,
Represents a point multiplication operation.
The CT imaging method according to claim 7, wherein the difference image ν is a difference between two previous iteration images or a plurality of iteration images.
The CT imaging method according to claim 7, wherein the image structure information μ is extracted from the original image by an algorithm such as segmentation.
The CT imaging method according to claim 1, wherein said alternate image reconstruction and image restoration employ an iterative method based on gradient descent:

x n = x n-1 - η × (A T (B(y-Ax n-1 ))) - λ × ▽ TV (x n-1 );

Where η is the acceleration factor, ▽TV(x n ) is the TV gradient of the currently reconstructed CT image x n , and n is a natural number, indicating the number of iterative operations.
The CT imaging method according to claim 10, wherein the iterative initial value x 0 is any one of the following: an all-zero image, an all-one image, and an image reconstructed by the filtered back projection method.
The CT imaging method according to claim 1, wherein said alternate image reconstruction and image restoration employ an iterative method of conjugate gradient or parabolic substitution.
The CT imaging method according to claim 1, wherein said alternate image reconstruction and image restoration comprises:

Performing image reconstruction according to the image reconstruction model and performing image restoration according to the image repair model;

When the number of loops reaches a preset number of times, the iterative operation is stopped, and the obtained iterative operation result is taken as the obtained reconstructed image.
A CT imaging system, comprising:

a scanning subsystem for scanning a target object, acquiring all ray projections through the object, and reconstructing system parameters;

An image reconstruction subsystem for performing image reconstruction according to an image reconstruction model;

An image repair subsystem for performing image repair according to an image repair model;

An iterative subsystem for determining whether the image reconstruction subsystem and the image restoration subsystem alternately perform image reconstruction and image restoration have reached a predetermined number of alternating iterations; and for outputting the reconstructed image.
The CT imaging system of claim 14 wherein the iterative subsystem determines The image reconstruction subsystem and the image restoration subsystem alternately perform image reconstruction and image restoration without reaching a predetermined number of alternating iterations, and the iterative subsystem instructs the image reconstruction subsystem and the image restoration subsystem to alternate image reconstruction and image repair.
The CT imaging system according to claim 14, wherein if the iterative subsystem determines that the image reconstruction subsystem and the image restoration subsystem alternately perform image reconstruction and image restoration have reached a predetermined number of alternate iterations, outputting the Rebuild the image.
The CT imaging system of claim 14 wherein said ray projections through the target object are: all ray projections that are emitted from the source, pass through the target object, and reach the detector.
The CT imaging system of claim 14 wherein said image reconstruction model is:

Where y is the acquired ray projection data through the target object, x is the currently reconstructed CT image, A is the system matrix determined by the acquired system parameters, and B is a weighting factor containing noise statistics. T represents the transposition operation of the matrix, TV(x) represents the total variation function of the variable x, and λ is the regularization coefficient.
A CT imaging system according to claim 18, wherein said total variation function TV(x) of said variable x is:

Where s and t respectively represent the number of rows and columns of the pixel of the image, where δ is a preset constant.
The CT imaging system of claim 14 wherein the image restoration model is:

Θ(x)=Φ(x)+Ψ(x);

Where Φ(x) is the image reconstruction model, Ψ(x) is the missing detail information, and Θ(x) is the repaired image.
The CT imaging system of claim 14 wherein said performing image restoration comprises extracting missing detail information Ψ(x) during reconstruction and adding it to the reconstructed image.
The CT imaging system according to claim 21, wherein said missing detail information Ψ(x) is extracted by:

Where ν is the difference image and μ is the image structure information,
Represents a point multiplication operation.
The CT imaging system according to claim 21, wherein said difference image ν is a difference between two iterations of the image before or after the iteration or an image of the plurality of iterations.
The CT imaging system according to claim 21, wherein said image structure information μ is extracted from the original image using an algorithm such as segmentation.
The CT imaging system of claim 14 wherein said alternating image reconstruction and image restoration employs an iterative method based on gradient descent:

x n = x n-1 - η × (A T (B(y-Ax n-1 ))) - λ × ▽ TV (x n-1 );

Where η is the acceleration factor, ▽TV(x n ) is the TV gradient of the currently reconstructed CT image x n , and n is a natural number, indicating the number of iterative operations.
The CT imaging system according to claim 24, wherein the iterative initial value x 0 is any one of the following: an all-zero image, an all-one image, and an image reconstructed by the filtered back projection method.
The CT imaging system of claim 14 wherein said alternating image reconstruction and image restoration employs an iterative method of conjugate gradient or parabolic substitution.
A CT internal region of interest imaging method, characterized in that it comprises:

Scanning the inner region of interest, acquiring all ray projections through the inner region of interest and reconstructing system parameters;

Constructing an image reconstruction model of the internal region of interest;

Constructing an image repair model of the internal region of interest;

According to the internal region of interest image reconstruction model and the internal region of interest image restoration model, the region of interest image reconstruction and the region of interest image restoration are alternately performed to obtain a region of interest reconstructed image.
The CT internal region of interest imaging method according to claim 28, wherein all of the ray projections through the inner region of interest are: emitted from the light source, passing through the inner region of interest, and reaching all ray projections of the detector. .
The CT internal region of interest imaging method according to claim 28, wherein the internal region of interest image reconstruction model is:

Where y is the acquired ray projection data through the region of interest, x is the currently reconstructed CT Image, A is the system matrix determined by the acquired system parameters, and B is the weighting factor, which contains noise statistics. T represents the transposition operation of the matrix, TV(x) represents the total variation function of the variable x, and λ is the regularization coefficient.
The CT internal region of interest imaging method according to claim 30, wherein the total variation function TV(x) of the variable x is:

Where s and t respectively represent the number of rows and columns of the pixel of the image, wherein δ is a constant greater than 0 and less than 10 -8 .
The CT internal region of interest imaging method according to claim 28, wherein the internal region of interest image restoration model is:

Θ(x)=Φ(x)+Ψ(x);

Where Φ(x) is an internal image reconstruction model of the region of interest, Ψ(x) is the missing detail information, and Θ(x) is the repaired image.
The CT internal region of interest imaging method according to claim 28, wherein said performing image reconstruction of the region of interest comprises extracting the missing detail information Ψ(x) during the reconstruction process and adding it to the reconstructed image.
The CT internal region of interest imaging method according to claim 33, wherein the missing detail information Ψ(x) is extracted by:

Where ν is the difference image and μ is the image structure information,
Represents a point multiplication operation.
The CT internal region of interest imaging method according to claim 34, wherein the difference image ν is a difference between two previous iteration images or a plurality of iterative images.
The CT internal region of interest imaging method according to claim 34, wherein the image structure information μ is extracted from the original image by using an algorithm such as segmentation.
The CT internal region of interest imaging method according to claim 28, wherein said alternately performing region of interest image reconstruction and region of interest image restoration adopts an iterative method based on gradient descent:

x n = x n-1 - η × (A T (B(y-Ax n-1 ))) - λ × ▽ TV (x n-1 );

Where η is the acceleration factor, ▽TV(x n ) is the TV gradient of the currently reconstructed CT image x n , and n is a natural number, indicating the number of iterative operations.
The CT internal region of interest imaging method according to claim 37, wherein the iterative initial value x 0 is any one of the following: an all-zero image, an all-one image, and an image reconstructed by the filtered back projection method.
The CT internal region of interest imaging method according to claim 28, wherein said alternating iterative method of image reconstruction of the region of interest and image region of interest region using conjugate gradient or parabolic substitution.
The CT internal region of interest imaging method according to claim 28, wherein said alternately performing image reconstruction of the region of interest and image restoration of the region of interest comprises:

Cyclic execution of the image reconstruction of the region of interest according to the image reconstruction model of the internal region of interest and the image restoration of the region of interest according to the image restoration model of the region of interest;

When the number of loops reaches a preset number of times, the iterative operation is stopped, and the obtained iterative operation result is used as the reconstructed image of the obtained region of interest.
A CT internal region of interest imaging system, comprising:

a scanning subsystem for scanning an internal region of interest, acquiring all ray projections through the internal region of interest, and reconstructing system parameters;

The image reconstruction subsystem is configured to perform image reconstruction of the internal region of interest according to the image reconstruction model of the internal region of interest;

The image repairing subsystem is configured to perform image repair of the internal region of interest according to the image repairing model of the internal region of interest;

An iterative subsystem is configured to determine whether the image reconstruction subsystem and the image restoration subsystem alternately perform image reconstruction of the region of interest and image restoration of the region of interest has reached a predetermined number of alternating iterations; and output the region of interest to reconstruct the image.