CN110717954A - 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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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 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 performing convolution processing on the back projection image based on the first filter matrix to obtain a CT image. The invention has small calculation 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) image can be obtained by back-projection calculation of projection data acquired by a CT scanning device and a preset convolution kernel, and different image creating effects (for example, highlighting bone tissues, soft tissues, etc.) can be obtained by using different convolution kernels so as to meet different clinical requirements.
In the medical diagnosis process, different tissue parts or different image creating effects need to be checked, and different convolution kernels need to be switched in the CT scanning process. At present, for each switching of convolution kernels, convolution processing needs to be carried out on original projection data based on convolution kernels, 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 aim to overcome the defect of large calculation 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, which 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;
and performing 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, which is applied to an image reconstruction device of a CT system;
the image reconstruction apparatus includes:
a determining module for determining a first filter matrix;
the acquiring 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 performing convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the image reconstruction method according to 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, carries out the steps of the image reconstruction method of the first aspect.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
in the embodiment of the invention, a user needs to check CT images with different image-building visual fields FOV or different image-building effects, directly calls the back projection image, and uses the first filter matrix to carry out convolution processing on the back projection image without carrying out convolution on projection data (original sampling data).
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 illustrating a method of image reconstruction according to an exemplary embodiment of the present invention;
FIG. 1B is a flow chart illustrating another method of image reconstruction 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 flow chart illustrating another method of image reconstruction according to an exemplary embodiment of the present invention;
FIG. 1E is a flow chart illustrating another method of image reconstruction according to an exemplary embodiment of the present invention;
FIG. 2A is a graphical illustration of a ramp function for determining a first filter matrix in accordance with an exemplary embodiment of the present invention;
FIG. 2B is a graphical illustration of an adjustment function in accordance with an exemplary embodiment of the present invention;
FIG. 2C is a diagram illustrating an adjustment target adjustment function according to an exemplary embodiment of the present invention;
FIG. 3 is a block diagram of an image reconstruction device according to an exemplary embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended 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 and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to 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 present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
During CT imaging, medical personnel have a need to view different FOV sites of the imaging field of view. Currently, for each imaging field of view FOV selected by a medical staff, convolution processing is performed on acquired projection data to obtain a CT image of the imaging field of view FOV. In the medical diagnosis process, if medical care personnel need to check the CT image highlighting the bone tissue and the CT image highlighting the soft tissue, two convolution operations need to be carried out on the projection data by using two different convolution kernels. If n (n is more than 2) CT images of different imaging field FOVs need to be obtained, convolution operation needs to be carried out on projection data for n times, the calculated amount is multiplied, and the time is consumed.
In order to solve the above problem, an embodiment of the present invention provides an image reconstruction method to reduce the amount of calculation for 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 also comprises CT scanning equipment, and the CT scanning equipment comprises a detector, a ray source and a rotating mechanism. The detector and the ray source are fixed in relative position, rotate around the rotation center on the rotation mechanism, and change in position relative to the measured object, so that the detector can detect CT projection data at different positions of the measured object.
Fig. 1A is a flowchart illustrating an image reconstruction method according to an exemplary embodiment of the present invention, the method including the steps of:
Wherein the first filter matrix is determined by the user selected adjustment function and the imaged field of view FOV to be viewed. The user may select the corresponding adjustment function and/or the imaging field of view FOV for different tissue sites to be viewed and different imaging effects.
And 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 bandwidth requirement of the first filter matrix is large, so that the first filter matrix will hardly suppress projection data of any sampling frequency, but the first filter matrix can be determined by, but not limited to, using a ramp function, see fig. 2A, which shows a graph diagram of a ramp function. The ramp function is typically stored in the CT scanner as discrete points, with the abscissa representing the point sequence number of the discrete points and the ordinate representing the curve amplitude.
In one embodiment, the above steps 101 and 102 can be performed synchronously to further increase the imaging speed.
And 103, performing convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
In this embodiment, a user needs to check CT images with different image-creating field of views FOV or different image-creating effects, directly call the back projection image, and perform convolution processing on the back projection image by using the first filter matrix without performing convolution on projection data (original sampling data).
On the basis of the flowchart of the image reconstruction method shown in fig. 1A, referring to fig. 1B, a flowchart of another embodiment of the image reconstruction method is shown, wherein step 101 comprises:
step 101-1, determining a target FOV and a target adjusting function from a preset image-establishing field of view FOV and an adjusting function according to a received selection instruction.
Generally, a plurality of preset adjustment functions are stored in a CT scanner, so that medical staff can select an adjustment function required for image reconstruction by themselves, and the adjustment function selected by the medical staff is determined to be a target adjustment function. The CT scanning device also provides a preset FOV range for the medical staff to select the desired target FOV.
Step 101-2, calculating an image cut-off frequency according to the target FOV.
The image cut-off frequency corresponds to a frequency required by the FOV field of view, but is not limited to the following formula:
in the formula (f)cutoffCharacterizing an image cut-off frequency; l represents FOV in cm; and N represents the number of sampling points of the imaging matrix. For example, in a CT image, the imaging matrix is 512 x 512 pixels, if the FOV is 32.1cm, i.e., L is 32.1; n512, image cut-off frequency
The unit is line pair/centimeter (lp/cm).
And step 101-3, adjusting parameters of the target adjustment function according to the cut-off frequency of the image to obtain a convolution kernel function.
The preset adjustment function may be expressed as:
Func＝mtf*enh；
in the formula (f)1、f2Representing the upper and lower cut-off frequencies of a preset adjusting function; a. b, c and d represent the parameters of the preset adjusting function, and adjusting a, b, c and d can generate adjusting function curves with different shapes.
In addition, the curve of the adjustment function is typically stored in the CT scanner in the form of discrete points. Taking the adjustment function curve shown in fig. 2B as an example, the abscissa of the graph represents the point number and the ordinate represents the curve amplitude. In the figure, 4096 discrete points are used to represent the adjustment function curve, and the length of the adjustment function curve on the x-axis can be changed by changing the number of discrete points of the adjustment function curve; wherein the last point represents the cut-off frequency f of the curve of the adjustment functioncutoffThe cut-off frequency of the adjustment function curve is set by changing the value size.
And step 101-4, determining a first filter matrix according to the convolution kernel function.
On the basis of the flow chart of the image reconstruction method shown in fig. 1B, one possible implementation of adjusting the parameters of the target adjustment function is provided below, see fig. 1C, and step 101-3 includes:
and 101-31, comparing the image cut-off frequency with the cut-off frequency of the target adjusting function.
In the step 101-31, if the cut-off frequency of the image is equal to the cut-off frequency of the target adjustment function, which indicates that the current target adjustment function meets the requirement, a second filter matrix is determined according to the target adjustment function, which can be directly used for performing convolution operation on the initial image, and then the step 101-32 is executed;
and 101-32, determining the target adjusting 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, which indicates that the cut-off frequency of the target adjustment function is smaller, the current target adjustment function is used to perform convolution processing on the initial image, 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, then step 101-33 is executed;
and 101-33, performing 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 the interpolation processing as a convolution kernel function.
Alternatively, in steps 101-33, the target adjustment function may be interpolated using an interpolation function to suppress unwanted high frequency components in the image. Specifically, the interpolation function may be, but is not limited to, a standard spline interpolation function.
In step 101-31, if the image cutoff frequency is smaller than the cutoff frequency of the target adjustment function, which indicates that the cutoff frequency of the target adjustment function is too high, and the initial image is convolved with the current target adjustment function, the image contains many unnecessary high-frequency components, so that the cutoff frequency of the current target adjustment function needs to be adjusted, then step 101-34 is executed.
And 103-34, intercepting the part of the target adjustment function with the frequency less than or equal to the image cut-off frequency as a convolution kernel function.
Alternatively, in steps 103-34, the adjustment coefficients may be used to intercept the portions of the target adjustment function having frequencies equal to or less than the image cutoff frequency as convolution kernel functions to suppress unwanted high frequency components in the image.
For example, for a convolution kernel, the amplitude of its adjustment curve is 1. After the image cutoff frequency is calculated, the portion larger than the image cutoff frequency gradually transits to almost 0 after passing through an adjustment range. The adjustment range is the adjustment factor cut-off frequency, and in particular, see fig. 2C, the adjustment curve is represented by 4096 discrete points, where the maximum frequency corresponding to the last point is 26.12. The frequency of the start of the adjustment curve is 5.12, corresponding to point 803(4096 × 5.12/26.12), and the adjustment range is 160 × 0.2 × 803 (the adjustment factor used here is 0.2), i.e., the adjustment curve is adjusted from point 803, is adjusted by 160 points, and finally is adjusted to 0. Cosine transition is adopted in the adjusting range:
where Y is the result of the final calculation, Y0 is the initial adjustment value, X corresponds to a point on the abscissa, X0 represents the initial coordinate value at which adjustment is started, i.e., the point corresponding to the clipping frequency, and l is the entire adjustment range. Generally speaking, the required frequency range is intercepted, the adjustment value is transited to 0 through transition, and finally, all points outside the adjustment range are set to 0.
On the basis of the flowchart of the image reconstruction method shown in fig. 1A, referring to fig. 1D, a flowchart of another embodiment of the image reconstruction method is shown, wherein step 102 comprises: retrieving a pre-stored backprojected image.
In this embodiment, before step 102, the method further includes:
and step 100, performing convolution processing on the projection data based on the second filter matrix to obtain a back projection image.
And storing the back projection image obtained in the step 100, and using the back projection image as a calculation basis for switching different imaging field FOVs or different imaging effects of a user. In this embodiment, step 101 and step 102 are executed synchronously.
In another implementation, when the user creates an image for the first time, in step 102, the projection data is convolved based on the second filter matrix to obtain a back projection image. And then, directly calling the back projection image obtained by calculation each time of image building.
The technical solution of the embodiment of the present invention is further described below by taking the example that the user views the bone tissue and the soft tissue of the thigh part, respectively, and referring to fig. 1E, the method includes the following steps:
If the user selects to view the CT image of the bone tissue first, the corresponding target FOV and the target adjusting function are selected first.
And 102a, performing 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 a specific implementation process is not described herein again.
And 103a, performing 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 bone tissue CT image required by the user.
And 104a, according to the received selection instruction, determining a second target FOV and a second target adjustment function from the preset image-establishing FOV and adjustment functions, 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 details are not repeated here. Since the FOV and the adjustment function in step 101a and step 104a are not the same at the same time, the third filter matrix and the first filter matrix are not the same, for example, the cut-off frequencies are different.
And 105a, performing 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 the embodiment, regardless of whether the bone tissue CT image or the soft tissue CT image is obtained, the back projection image is directly called and is subjected to convolution processing to obtain the CT image with the required effect.
Fig. 3 is a schematic structural diagram 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, where the CT system 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 filter matrix;
the obtaining module 32 is configured to obtain a back projection image, where the back projection image is a convolution result of a second filter matrix and 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 a target FOV and a target adjusting function from a preset image-establishing field of view FOV and an adjusting function;
calculating an image cut-off frequency from 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 image cut-off frequency is larger than the cut-off frequency of the target adjustment function, performing interpolation processing on the target adjustment function to enable the cut-off frequency of the target adjustment function to be equal to the image cut-off frequency, and determining the target adjustment function subjected to interpolation processing as the convolution kernel function;
and when the image cut-off frequency is smaller than the cut-off frequency of the target adjustment function, intercepting a part of the target adjustment function, the frequency of which is smaller than or equal to the image cut-off frequency, as the convolution kernel function.
Optionally, when performing interpolation processing on the target adjustment function, the determining module is configured to:
performing interpolation processing on the target adjusting function by using an interpolation function;
intercepting a part of the preset filter function with the frequency less than or equal to the image cut-off frequency as the third filter function, wherein the intercepting comprises the following steps:
and intercepting a part of the preset filter function with the frequency less than or equal to the image cut-off frequency by using an adjusting coefficient to serve as the third filter function.
Optionally, the obtaining module is specifically configured to:
retrieving a pre-stored back projection image;
or performing convolution processing on 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 according to 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 only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in FIG. 4, electronic device 40 may take the form of a general purpose computing device, which may be a server device, for example. The components of electronic device 40 may include, but are not limited to: the at least one processor 41, the at least one memory 42, and a bus 43 connecting the various system components (including the memory 42 and the processor 41).
The bus 43 includes a data bus, an address bus, and a control bus.
The 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.
The processor 41 executes various functional applications and data processing, such as the image reconstruction method provided in any of the above embodiments, 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, the model-generated electronic device 40 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via a network adapter 46. As shown, the network adapter 46 communicates with the other modules of the model-generated electronic device 40 over a bus 43. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the 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, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of image reconstruction as provided by any of the above embodiments.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a 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, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps of implementing the image reconstruction method provided in any of the embodiments described above, when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. An image reconstruction method is 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;
and performing convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
2. The image reconstruction method of claim 1, wherein determining the first filter matrix comprises:
according to the received selection instruction, determining a target FOV and a target adjusting function from a preset image-establishing field of view FOV and an adjusting function;
calculating an image cut-off frequency from 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.
3. The image reconstruction method of claim 2, wherein adjusting parameters of the objective adjustment function according to the image cutoff frequency to obtain a convolution kernel function comprises:
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 image cut-off frequency is larger than the cut-off frequency of the target adjustment function, performing interpolation processing on the target adjustment function to enable the cut-off frequency of the target adjustment function to be equal to the image cut-off frequency, and determining the target adjustment function subjected to interpolation processing as the convolution kernel function;
and when the image cut-off frequency is smaller than the cut-off frequency of the target adjustment function, intercepting a part of the target adjustment function, the frequency of which is smaller than or equal to the image cut-off frequency, as the convolution kernel function.
4. The image reconstruction method according to claim 3, wherein the interpolating the target adjustment function includes:
performing interpolation processing on the target adjusting function by using an interpolation function;
intercepting a part of the preset filter function with the frequency less than or equal to the image cut-off frequency as the third filter function, wherein the intercepting comprises the following steps:
and intercepting a part of the preset filter function with the frequency less than or equal to the image cut-off frequency by using an adjusting coefficient to serve as the third filter function.
5. The image reconstruction method of claim 1, wherein acquiring a backprojected image comprises:
retrieving a pre-stored back projection image;
or performing convolution processing on the projection data based on a second filter matrix to obtain the back projection image.
6. An image reconstruction apparatus, characterized in that the image reconstruction apparatus is applied to an image reconstruction device of a CT system;
the image reconstruction apparatus includes:
a determining module for determining a first filter matrix;
the acquiring 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 performing convolution processing on the back projection image based on the first filter matrix to obtain a CT image.
7. The image reconstruction apparatus of claim 6, wherein the determination module is specifically configured to:
according to the received selection instruction, determining a target FOV and a target adjusting function from a preset image-establishing field of view FOV and an adjusting function;
calculating an image cut-off frequency from 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.
8. The image reconstruction device of claim 7 wherein, in adjusting the parameters of the objective adjustment function according to the image cutoff frequency, the determination module is 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 image cut-off frequency is larger than the cut-off frequency of the target adjustment function, performing interpolation processing on the target adjustment function to enable the cut-off frequency of the target adjustment function to be equal to the image cut-off frequency, and determining the target adjustment function subjected to interpolation processing as the convolution kernel function;
and when the image cut-off frequency is smaller than the cut-off frequency of the target adjustment function, intercepting a part of the target adjustment function, the frequency of which is smaller than or equal to the image cut-off frequency, as the convolution kernel function.
9. The image reconstruction apparatus of claim 8 wherein, in interpolating the target adjustment function, the determination module is to:
performing interpolation processing on the target adjusting function by using an interpolation function;
intercepting a part of the preset filter function with the frequency less than or equal to the image cut-off frequency as the third filter function, wherein the intercepting comprises the following steps:
and intercepting a part of the preset filter function with the frequency less than or equal to the image cut-off frequency by using an adjusting coefficient to serve as the third filter function.
10. The image reconstruction device of claim 6, wherein the acquisition module is specifically configured to:
retrieving a pre-stored back projection image;
or performing convolution processing on the projection data based on a second filter matrix to obtain the back projection image.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the image reconstruction method of any one of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the image reconstruction method according to one of claims 1 to 5.
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