CN110717954B - Image reconstruction method and device, electronic equipment and storage medium - Google Patents
Image reconstruction method and device, electronic equipment and storage medium Download PDFInfo
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- CN110717954B CN110717954B CN201910923194.5A CN201910923194A CN110717954B CN 110717954 B CN110717954 B CN 110717954B CN 201910923194 A CN201910923194 A CN 201910923194A CN 110717954 B CN110717954 B CN 110717954B
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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
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- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
- G06T2211/421—Filtered back projection [FBP]
Abstract
The invention discloses an image reconstruction method and device, electronic equipment and a storage medium. The image reconstruction method is applied to image reconstruction equipment of the CT system; the image reconstruction method comprises the following steps: determining a first filter matrix; acquiring a back projection image, wherein the back projection image is a convolution result of a second filter matrix and projection data, and the function bandwidth of the first filter matrix is smaller than that of the second filter matrix; and carrying out convolution processing on the back projection image based on the first filter matrix to obtain a CT image. The invention has small calculated amount and high efficiency.
Description
Technical Field
The present invention relates to the field of medical imaging technologies, and in particular, to an image reconstruction method and apparatus, an electronic device, and a storage medium.
Background
The CT (Computed Tomography, i.e. computerized tomography) image can be obtained by back-projection calculation of the projection data acquired by the CT scanner and a predetermined convolution kernel, and different imaging effects (such as highlighting bone tissue, soft tissue, etc.) can be obtained by using different convolution kernels, so as to meet different clinical requirements.
In the medical diagnosis process, the requirement of checking different tissue parts or checking different imaging effects exists, and different convolution kernels are required to be switched in the CT scanning process. At present, for each switching of convolution kernels, convolution processing needs to be performed on original projection data based on convolution check, so that calculation is large and time is consumed.
Disclosure of Invention
The invention provides an image reconstruction method and device, electronic equipment and a storage medium, which are used for solving the defect of large calculated amount when a convolution kernel is switched in the prior art.
Specifically, the invention is realized by the following technical scheme:
in a first aspect, an image reconstruction method is provided, the image reconstruction method being applied to an image reconstruction device of a CT system;
the image reconstruction method comprises the following steps:
determining a first filter matrix;
acquiring a back projection image, wherein the back projection image is a convolution result of a second filter matrix and projection data, and the function bandwidth of the first filter matrix is smaller than that of the second filter matrix;
and carrying out convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
In a second aspect, an image reconstruction apparatus is provided, the image reconstruction apparatus being applied to an image reconstruction device of a CT system;
the image reconstruction apparatus includes:
a determining module, configured to determine a first filtering matrix;
the acquisition module is used for acquiring a back projection image, wherein the back projection image is a convolution result of a second filter matrix and projection data, and the function bandwidth of the first filter matrix is smaller than that of the second filter matrix;
and the imaging module is used for carrying out convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
In a third aspect, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the image reconstruction method of the first aspect when executing the computer program.
In a fourth aspect, a computer readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, implements the steps of the image reconstruction method according to the first aspect.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
in the embodiment of the invention, a user needs to check CT images with different imaging vision FOVs or different imaging effects, directly calls the back projection image, and uses the first filter matrix to carry out convolution processing on the back projection image instead of carrying out convolution on projection data (original sampling data), and the data volume of the initial back projection image is far smaller than that of the projection data, so that the speed of convolution operation can be greatly increased, and the imaging speed is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1A is a flow chart of an image reconstruction method according to an exemplary embodiment of the present invention;
FIG. 1B is a flowchart illustrating another image reconstruction method according to an exemplary embodiment of the present invention;
FIG. 1C is a flowchart illustrating one method step of step 103 of FIG. 1B in accordance with an exemplary embodiment of the present invention;
FIG. 1D is a flowchart illustrating another image reconstruction method according to an exemplary embodiment of the present invention;
FIG. 1E is a flowchart illustrating another image reconstruction method according to an exemplary embodiment of the present invention;
FIG. 2A is a graph illustrating a ramp function for determining a first filter matrix according to an exemplary embodiment of the present invention;
FIG. 2B is a graph illustrating an adjustment function according to an exemplary embodiment of the present invention;
FIG. 2C is a schematic diagram of an adjustment objective adjustment function according to an exemplary embodiment of the present invention;
FIG. 3 is a block diagram of an image reconstruction apparatus according to an exemplary embodiment of the present invention;
fig. 4 is a schematic structural view of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not meant to be all implementations consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the invention. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.
In CT imaging, medical personnel have a need to view the location of the FOV of different imaging fields. Currently, for each imaging field FOV selected by a healthcare worker, convolution processing is performed on the acquired projection data, thereby obtaining a CT image of the imaging field FOV. In the medical diagnosis process, if a medical staff needs to check and display the CT image of the bone tissue and the CT image of the soft tissue, two different convolution checks need to be used for carrying out two convolution operations on the projection data. If n (n is greater than 2) CT images of different imaging fields FOV are required to be obtained, n convolution operations are required to be performed on the projection data, and the calculated amount is multiplied, which is time-consuming.
In order to solve the above problems, an embodiment of the present invention provides an image reconstruction method to reduce the calculation amount of image reconstruction when the FOV of the imaging field is switched. The image reconstruction method is applied to image reconstruction equipment of a CT system, the CT system further comprises CT scanning equipment, and the CT scanning equipment comprises a detector, a ray source and a rotating mechanism. The relative positions of the detector and the ray source are fixed, the detector and the ray source rotate around the rotation center on the rotation mechanism, and the position of the detector and the ray source changes relative to the measured body, so that the detector can detect CT projection data of different positions of the measured body.
Fig. 1A is a flowchart of an image reconstruction method according to an exemplary embodiment of the present invention, the method comprising the steps of:
step 101, determining a first filter matrix.
Wherein the first filter matrix is determined by the user selected adjustment function and the field of view FOV to be viewed. The user may select the corresponding adjustment function and/or the field of view FOV for different tissue sites to be viewed, with different imaging effects.
Step 102, acquiring a back projection image.
The back projection image is a convolution result of the second filter matrix and the projection data, and the function bandwidth of the first filter matrix is smaller than that of the second filter matrix. Generally, the function bandwidth of the first filter matrix is required to be large, so that it hardly suppresses projection data of any sampling frequency, and the first filter matrix can be determined by using, but is not limited to, a ramp function, and a schematic diagram of the ramp function is shown in fig. 2A. The ramp function is typically stored in the CT scanning device in the form of discrete points, with the abscissa representing the point number of the discrete points and the ordinate representing the magnitude of the curve.
In one embodiment, the steps 101 and 102 may be performed synchronously to further increase the imaging speed.
And 103, carrying out convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
In this embodiment, the user needs to view CT images with different view field FOVs or different image effects, directly call the back projection image, and use the first filter matrix to perform convolution processing on the back projection image, without convoluting the projection data (original sampling data), and because the data size of the initial back projection image is far smaller than that of the projection data, the speed of convolution operation can be greatly increased, so that the image capturing speed is improved.
On the basis of the flow chart of the image reconstruction method shown in fig. 1A, referring to fig. 1B, another embodiment of the flow chart of the image reconstruction method is shown, wherein step 101 comprises:
step 101-1, determining an object FOV and an object adjustment function from preset imaging field FOV and adjustment functions according to the received selection instruction.
A plurality of preset adjusting functions are stored in the common CT scanning equipment, so that medical staff can select the adjusting functions required by image reconstruction by themselves, and the adjusting functions selected by the medical staff are determined to be target adjusting functions. The CT scanning device also provides a preset FOV range for medical staff to select a required target FOV.
Step 101-2, calculating an image cut-off frequency according to the target FOV.
Wherein the image cut-off frequency corresponds to the frequency required for the FOV field of view, the image cut-off frequency can be calculated using, but not limited to, the following formula:
wherein f cutoff Characterizing an image cut-off frequency; l characterizes the FOV in cm; n represents the number of sampling points of the imaging matrix. For example, for a CT image, the imaging matrix is 512×512 pixels, if the FOV is 32.1cm, i.e., l=32.1; n=512, image cut-off frequencyUnits are pairs of wires per centimeter (lp/cm).
And step 101-3, adjusting parameters of the target adjustment function according to the image cut-off frequency to obtain a convolution kernel function.
The preset adjustment function may be expressed, for example, as:
Func＝mtf*enh；
wherein f 1 、f 2 Representing the upper and lower cut-off frequencies of a preset adjusting function; a. b, c, d represent parameters of a preset adjustment function, and different shapes of adjustment function curves can be generated by adjusting a, b, c, d.
In addition, the adjustment function curve is typically stored in the form of discrete points in the CT scanning device. Taking the adjustment function curve shown in fig. 2B as an example, the abscissa in the figure represents the point number, and the ordinate represents the curve amplitude. In the figure, 4096 discrete points are used for representing the adjustment function curve, and the length of the adjustment function curve in the x axis can be changed by changing the number of the discrete points of the adjustment function curve; wherein the last point represents the cut-off frequency f of the adjustment function curve cutoff The cut-off frequency of the adjustment function curve is set by changing the value size.
Step 101-4, determining a first filter matrix according to the convolution kernel.
On the basis of the flow chart of the image reconstruction method shown in fig. 1B, a possible implementation of adjusting the parameters of the target adjustment function is provided below, see fig. 1C, and step 101-3 includes:
step 101-31, comparing the image cutoff frequency with the cutoff frequency of the target adjustment function.
In step 101-31, if the cut-off frequency of the image is equal to the cut-off frequency of the target adjustment function, it is indicated that the current target adjustment function meets the requirement, and the second filter matrix is determined according to the target adjustment function and can be directly used for performing convolution operation on the initial image, then step 101-32 is executed;
step 101-32, determining the target adjustment function as a convolution kernel function.
In step 103-31, if the cut-off frequency of the image is greater than the cut-off frequency of the target adjustment function, it is indicated that the cut-off frequency of the target adjustment function is smaller, the convolution processing is performed on the initial image by using the current target adjustment function, and a part of the required high-frequency components in the image are suppressed, so that the cut-off frequency of the current target adjustment function needs to be adjusted, and step 101-33 is executed;
and 101-33, performing interpolation processing on the target adjustment function to enable the cutoff frequency of the target adjustment function to be equal to the image cutoff frequency, and determining the target adjustment function subjected to the interpolation processing as a convolution kernel function.
Optionally, in steps 101-33, the target adjustment function may be interpolated using an interpolation function to suppress unwanted high frequency components in the image. In particular, the interpolation function may be, but is not limited to, a standard spline interpolation function.
In step 101-31, if the cut-off frequency of the image is smaller than the cut-off frequency of the target adjustment function, it is indicated that the cut-off frequency of the target adjustment function is too high, the convolution processing is performed on the initial image using the current target adjustment function, and the image includes more unwanted high frequency components, so that the cut-off frequency of the current target adjustment function needs to be adjusted, and step 101-34 is performed.
Step 103-34, intercepting a part with the frequency smaller than or equal to the image cut-off frequency in the target adjustment function as a convolution kernel function.
Optionally, in steps 103-34, the portion of the target adjustment function having a frequency less than or equal to the cut-off frequency of the image may be truncated using the adjustment coefficients as a convolution kernel to suppress unwanted high frequency components in the image.
For example, for a convolution kernel, the magnitude of its adjustment curve is 1. After calculating the image cut-off frequency, the portion larger than the image cut-off frequency is gradually transitioned to almost 0 through an adjustment range. Tuning range = tuning coefficient x cut-off frequency, specifically, referring to fig. 2C, the tuning curve in the figure is represented by 4096 discrete points, where the maximum frequency corresponding to the last point is 26.12. The frequency of the initial cut of the adjustment curve is 5.12, corresponding to point 803 (4096×5.12/26.12), the adjustment range is 160=0.2×803 (the adjustment coefficient used here is 0.2), i.e. the adjustment curve starts to adjust from 803 th point, adjusts 160 points, and finally adjusts to 0. Cosine transition is adopted in the adjustment range:
where Y is the final calculation result, Y0 is the initial value of adjustment, X corresponds to a point on the abscissa, X0 represents the initial value of the coordinate at which adjustment is started, i.e., a point corresponding to the cut-out frequency, and l is the entire adjustment range. In general, the desired frequency range is cut off, the adjustment value is changed to 0 by the transition, and finally points outside the adjustment range are set to 0.
On the basis of the flow chart of the image reconstruction method shown in fig. 1A, referring to fig. 1D, another embodiment of the flow chart of the image reconstruction method is shown, wherein step 102 comprises: the pre-stored backprojected image is retrieved.
In this embodiment, before step 102, the method further includes:
and 100, carrying out convolution processing on projection data based on the second filter matrix to obtain a back projection image.
The back projection image obtained in step 100 is stored as a basis for the calculation of the user to switch between different field of view FOVs or different imaging effects. In this embodiment, step 101 is performed in synchronization with step 102.
In another implementation, when the user performs first image creation, in step 102, convolution processing is performed on the projection data based on the second filter matrix to obtain a back projection image. And directly calling the calculated back projection image after each image is built.
Taking the example that the user views the bone tissue and the soft tissue of the thigh part respectively, the technical scheme of the embodiment of the invention is further described, and referring to fig. 1E, the method comprises the following steps:
step 101a, determining a first target FOV and a first target adjustment function from preset imaging field FOV and adjustment functions according to the received selection instruction, and determining a first filter matrix according to the first target FOV and the first target adjustment function.
If the user selects to view the CT image of the bone tissue first, the corresponding target FOV and target adjustment function are selected first.
And 102a, carrying out convolution processing on the projection data based on the second filter matrix to obtain a back projection image.
Similar to step 102, the back-projection image of step 102a is a convolution result of the second filter matrix and the projection data, and the detailed implementation process is not described herein.
And 103a, carrying out convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
The CT image in step 103a is the CT image of the bone tissue required by the user.
Step 104a, determining a second target FOV and a second target adjustment function from the preset imaging field FOV and adjustment function according to the received selection instruction, and determining a third filter matrix according to the second target FOV and the second target adjustment function.
It should be noted that, the implementation of determining the third filter matrix is similar to that of determining the first filter matrix, and will not be repeated here. The FOV and the adjustment function in step 101a and step 104a are not the same at the same time, so the third filter matrix is not the same as the first filter matrix, e.g. the cut-off frequencies of the two are different.
And 105a, carrying out convolution processing on the back projection image based on the third filter matrix to obtain a CT image.
The CT image in step 105a is the soft tissue CT image required by the user.
In this embodiment, whether the bone tissue CT image or the soft tissue CT image is obtained, the back projection image is directly called and convolved to obtain the CT image with the desired effect, which significantly improves the calculation efficiency compared with the prior art in which the projection data is convolved each time.
Fig. 3 is a schematic structural view of an image reconstruction apparatus according to an exemplary embodiment of the present invention, which is applied to an image reconstruction device of a CT system, which further includes a CT scanning device. As shown in fig. 3, the image reconstruction apparatus includes: a determination module 31, an acquisition module 32 and an imaging module 33.
The determining module 31 is configured to determine a first filtering matrix;
the obtaining module 32 is configured to obtain a back-projection image, where the back-projection image is a convolution result of the second filter matrix and the projection data, and a function bandwidth of the first filter matrix is smaller than a function bandwidth of the second filter matrix;
the imaging module 33 is configured to perform convolution processing on the back-projection image based on the first filter matrix to obtain a CT image.
Optionally, the determining module is specifically configured to:
according to the received selection instruction, determining an object FOV and an object adjustment function from the preset imaging field FOV and adjustment functions;
calculating an image cut-off frequency according to the target FOV;
adjusting parameters of the target adjustment function according to the image cut-off frequency to obtain a convolution kernel function;
and determining the first filter matrix according to the convolution kernel function.
Optionally, when adjusting the parameter of the target adjustment function according to the image cut-off frequency, the determining module is configured to:
determining the target adjustment function as the convolution kernel function when the image cutoff frequency is equal to the cutoff frequency of the target adjustment function;
when the cut-off frequency of the image is larger than the cut-off frequency of the target adjustment function, carrying out interpolation processing on the target adjustment function to enable the cut-off frequency of the target adjustment function to be equal to the cut-off frequency of the image, and determining the target adjustment function subjected to interpolation processing as the convolution kernel function;
and under the condition that the image cutoff frequency is smaller than the cutoff frequency of the target adjustment function, intercepting a part of the target adjustment function, of which the frequency is smaller than or equal to the image cutoff frequency, as the convolution kernel function.
Optionally, in performing interpolation processing on the target adjustment function, the determining module is configured to:
performing interpolation processing on the target adjustment function by using an interpolation function;
intercepting a part with the frequency smaller than or equal to the image cut-off frequency in the preset filter function as the third filter function, wherein the method comprises the following steps:
and intercepting a part with the frequency smaller than or equal to the cut-off frequency of the image in the preset filter function by using an adjustment coefficient as the third filter function.
Optionally, the acquiring module is specifically configured to:
invoking a pre-stored back-projection image;
or, convolving the projection data based on a second filter matrix to obtain the back projection image.
Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention, and shows a block diagram of an exemplary electronic device 40 suitable for implementing an embodiment of the present invention. The electronic device 40 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, the electronic device 40 may be embodied in the form of a general purpose computing device, which may be a server device, for example. Components of electronic device 40 may include, but are not limited to: the at least one processor 41, the at least one memory 42, a bus 43 connecting the different system components, including the memory 42 and the processor 41.
The bus 43 includes a data bus, an address bus, and a control bus.
Memory 42 may include volatile memory such as Random Access Memory (RAM) 421 and/or cache memory 422, and may further include Read Only Memory (ROM) 423.
Memory 42 may also include a program tool 425 (or utility) having a set (at least one) of program modules 424, such program modules 424 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 41 executes various functional applications and data processing, such as the image reconstruction method provided by any of the embodiments described above, by running a computer program stored in the memory 42.
The electronic device 40 may also communicate with one or more external devices 44 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 45. Also, model-generated electronic device 40 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet via network adapter 46. As shown, the network adapter 46 communicates with the other modules of the model-generated electronic device 40 via the bus 43. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with model-generating electronic device 40, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of image reconstruction provided by any of the above embodiments.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation manner, the present invention may also be realized in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the image reconstruction method provided by any of the above-mentioned embodiments, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the invention.
Claims (8)
1. An image reconstruction method, characterized in that the image reconstruction method is applied to an image reconstruction device of a CT system;
the image reconstruction method comprises the following steps:
determining a first filter matrix;
acquiring a back projection image, wherein the back projection image is a convolution result of a second filter matrix and projection data, and the function bandwidth of the first filter matrix is smaller than that of the second filter matrix;
performing convolution processing on the back projection image based on the first filter matrix to obtain a CT image;
the first filter matrix is determined by an adjustment function selected by a user and an imaging field of view FOV to be viewed;
the determining a first filter matrix includes:
according to the received selection instruction, determining an object FOV and an object adjustment function from the preset imaging field FOV and adjustment functions;
calculating an image cut-off frequency according to the target FOV;
adjusting parameters of the target adjustment function according to the image cut-off frequency to obtain a convolution kernel function;
determining the first filter matrix according to the convolution kernel function;
the step of adjusting the parameters of the target adjustment function according to the image cut-off frequency to obtain a convolution kernel function comprises the following steps:
determining the target adjustment function as the convolution kernel function when the image cutoff frequency is equal to the cutoff frequency of the target adjustment function;
when the cut-off frequency of the image is larger than the cut-off frequency of the target adjustment function, carrying out interpolation processing on the target adjustment function to enable the cut-off frequency of the target adjustment function to be equal to the cut-off frequency of the image, and determining the target adjustment function subjected to interpolation processing as the convolution kernel function;
and under the condition that the image cutoff frequency is smaller than the cutoff frequency of the target adjustment function, intercepting a part of the target adjustment function, of which the frequency is smaller than or equal to the image cutoff frequency, as the convolution kernel function.
2. The image reconstruction method according to claim 1, wherein interpolating the target adjustment function includes:
performing interpolation processing on the target adjustment function by using an interpolation function;
intercepting a part with the frequency smaller than or equal to the image cut-off frequency in the preset filter function as a third filter function, wherein the method comprises the following steps:
and intercepting a part with the frequency smaller than or equal to the cut-off frequency of the image in the preset filter function by using an adjustment coefficient as the third filter function.
3. The image reconstruction method according to claim 1, wherein acquiring the back projection image includes:
invoking a pre-stored back-projection image;
or, convolving the projection data based on a second filter matrix to obtain the back projection image.
4. An image reconstruction device, characterized in that the image reconstruction device is applied to an image reconstruction apparatus of a CT system;
the image reconstruction apparatus includes:
a determining module, configured to determine a first filtering matrix;
the acquisition module is used for acquiring a back projection image, wherein the back projection image is a convolution result of a second filter matrix and projection data, and the function bandwidth of the first filter matrix is smaller than that of the second filter matrix;
the imaging module is used for carrying out convolution processing on the back projection image based on the first filter matrix to obtain a CT image;
the first filter matrix is determined by an adjustment function selected by a user and an imaging field of view FOV to be viewed;
the determining module is specifically configured to:
according to the received selection instruction, determining an object FOV and an object adjustment function from the preset imaging field FOV and adjustment functions;
calculating an image cut-off frequency according to the target FOV;
adjusting parameters of the target adjustment function according to the image cut-off frequency to obtain a convolution kernel function;
determining the first filter matrix according to the convolution kernel function;
in the adjusting the parameters of the target adjustment function according to the image cut-off frequency, the determining module is configured to:
determining the target adjustment function as the convolution kernel function when the image cutoff frequency is equal to the cutoff frequency of the target adjustment function;
when the cut-off frequency of the image is larger than the cut-off frequency of the target adjustment function, carrying out interpolation processing on the target adjustment function to enable the cut-off frequency of the target adjustment function to be equal to the cut-off frequency of the image, and determining the target adjustment function subjected to interpolation processing as the convolution kernel function;
and under the condition that the image cutoff frequency is smaller than the cutoff frequency of the target adjustment function, intercepting a part of the target adjustment function, of which the frequency is smaller than or equal to the image cutoff frequency, as the convolution kernel function.
5. The image reconstruction apparatus according to claim 4, wherein the determination module is configured to, in interpolation processing of the target adjustment function:
performing interpolation processing on the target adjustment function by using an interpolation function;
intercepting a part with the frequency smaller than or equal to the image cut-off frequency in the preset filter function as a third filter function, wherein the method comprises the following steps:
and intercepting a part with the frequency smaller than or equal to the cut-off frequency of the image in the preset filter function by using an adjustment coefficient as the third filter function.
6. The image reconstruction apparatus of claim 4, wherein the acquisition module is specifically configured to:
invoking a pre-stored back-projection image;
or, convolving the projection data based on a second filter matrix to obtain the back projection image.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the image reconstruction method of any one of claims 1 to 3 when executing the computer program.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the image reconstruction method as claimed in any one of claims 1 to 3.
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| US6768782B1 (en) * | 2002-12-16 | 2004-07-27 | University Of Notre Dame Du Lac | Iterative method for region-of-interest reconstruction |
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| CN106651753A (en) * | 2016-09-28 | 2017-05-10 | 沈阳东软医疗***有限公司 | Method and device for improving CT image displaying effect |
| CN107481297A (en) * | 2017-08-31 | 2017-12-15 | 南方医科大学 | A kind of CT image rebuilding methods based on convolutional neural networks |
| CN109509235A (en) * | 2018-11-12 | 2019-03-22 | 深圳先进技术研究院 | Method for reconstructing, device, equipment and the storage medium of CT image |
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|---|---|---|---|---|
| US6768782B1 (en) * | 2002-12-16 | 2004-07-27 | University Of Notre Dame Du Lac | Iterative method for region-of-interest reconstruction |
| CN1586401A (en) * | 2004-08-20 | 2005-03-02 | 东软飞利浦医疗设备***有限责任公司 | 1/4 path deviation interpolation method for CT system |
| CN106651753A (en) * | 2016-09-28 | 2017-05-10 | 沈阳东软医疗***有限公司 | Method and device for improving CT image displaying effect |
| CN107481297A (en) * | 2017-08-31 | 2017-12-15 | 南方医科大学 | A kind of CT image rebuilding methods based on convolutional neural networks |
| CN109509235A (en) * | 2018-11-12 | 2019-03-22 | 深圳先进技术研究院 | Method for reconstructing, device, equipment and the storage medium of CT image |
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