CN116671946A - Method for reconstructing dynamic image based on SPECT dynamic acquisition data - Google Patents
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Abstract
The application relates to the technical field of medical image processing, in particular to a method for reconstructing a dynamic image based on SPECT dynamic acquisition data.
Description
Technical Field
The application relates to the technical field of medical image processing, in particular to a method for reconstructing a dynamic image based on SPECT dynamic acquisition data.
Background
Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) are imaging of the temporal and spatial distribution of a radio-imaging drug taken into the body, reflecting information about the interaction of the drug with certain organs or tissues, for diagnosis of disease. Most radiopharmaceuticals undergo both uptake (wash in) and washout (wash out) in the organs or tissues of the human body, with blood circulation, at a relatively rapid rate of change, typically in seconds or 10 seconds, and washout, at a relatively slow rate of change, typically in 10 minutes or hours. Therefore, PET and SPECT line tomographic imaging generally perform data acquisition for an washout process, the acquired data generally becomes projection data, and a process of calculating three-dimensional spatial distribution of a drug based on the projection data is called tomographic image reconstruction, abbreviated as reconstruction.
The dynamic change of the uptake process of certain drugs has important reference value for clinical diagnosis; other drugs may also change at a faster rate during the wash-out process in the human body. Dynamic imaging is therefore required. The PET can realize complete projection data acquisition without rotation, so that the PET has inherent dynamic tomography capability. The most commonly used dual-probe SPECT systems in clinic generally require a rotating probe to acquire projection data at multiple angles to achieve reconstruction of a three-dimensional tomographic image, and a geometric schematic of the process of acquiring data from three-dimensional distribution projections of an imaging target to a detector is shown in fig. 1. When dynamic change occurs in the distribution of the target medicine in the process of rotary sampling, data at different angles are inconsistent, namely, correspond to different three-dimensional distributions, so that a correct image cannot be reconstructed by a conventional tomographic reconstruction method.
Thus, in this case, there are generally three methods. Firstly, the probe is fixed at a certain angle to acquire a dynamic plane image, so that dynamic change information can be reflected to a certain extent. However, superimposing the three-dimensional spatial distribution on the two-dimensional projection data causes a loss of spatial information and quantitative accuracy. Secondly, for the dynamic process of which the change speed is in 10 seconds, the probe is rapidly rotated in a reciprocating way to sample, so that the acquisition of dynamic multi-angle projection data and the reconstruction of dynamic tomographic images are realized. However, fast rotational sampling results in too high a noise level of the projection data for each angle, which affects the quality of the reconstructed image, such as signal to noise ratio, etc., and this approach is also ineffective for faster dynamic processes (e.g., in seconds) where the rotational speed of the probe is not achievable. In the third method, projection data are acquired through slow rotation, the acquisition time of each angle is basically equivalent to or slightly lower than the unit time of the dynamic change process, and meanwhile, the dynamic change of an image is modeled, so that a dynamic tomographic image (four-dimensional space-time distribution) is reconstructed. However, the number of unknown variables (values of all image voxels at different time points) of the main problem of the method far exceeds the number of acquired projection data, and the conventional three-dimensional tomographic reconstruction method is difficult to ensure that a correct and meaningful result is obtained, so that a model of a dynamic change process of a target image needs to be simplified, the degree of freedom of change is reduced, and the number of the unknown variables is remarkably reduced, so that the distribution of a dynamic image close to the correct is obtained.
Thus, known correlation methods based on voxel dynamic change model constraints still face many unknown variables (functions describing each voxel over time) or model inconsistencies with the actual target process, resulting in difficulty in accurately matching the reconstructed image to the target image. Some SPECT systems of arc or ring detectors can acquire projection data simultaneously based on multiple detector modules at different angles to achieve inherent dynamic tomographic imaging capability, but for complex spatial distributions of the target and rapid changes, it is still difficult to completely avoid the negative impact of data noise from rapid acquisition on reconstructed image quality.
Disclosure of Invention
The application aims to solve the problems of poor effect and low safety of the existing dynamic image data acquisition, image quantitative analysis and clinical diagnosis.
In order to solve the technical problems, the embodiment of the application provides a method for reconstructing a dynamic image based on SPECT dynamic acquisition data, which comprises the following steps:
s1, acquiring J initial image data acquired at J time points, and dividing each initial image data according to a preset image dividing method to obtain i organ tissue images;
s2, dividing each organ tissue image according to the preset image dividing method to obtain I initial estimated images corresponding to the organ tissue images;
s3, carrying out normalization processing on pixel values of each initial estimated image;
s4, setting iteration parameters for the initial estimated image I obtained by dividing the organ tissue image I when the time point is j
S5, multiplying each pixel value in the initial estimated image by the iteration parameterPerforming image reconstruction to obtain a current dynamic reconstruction estimation image;
s6, updating the iteration parameters according to a preset iteration method according to the initial image data and the current dynamic reconstruction estimation imageThe preset iteration method is a maximum likelihood estimation method or a maximum posterior probability method, and meanwhile, whether the preset iteration method reaches the preset iteration times or not is judged, if yes, the step S7 is executed; if not, iterating to the step S5;
s7, multiplying each pixel value in the initial image data by the iteration parameterAnd carrying out image reconstruction to obtain a final dynamic reconstruction image.
Furthermore, the initial image data is a CT image or a fusion image obtained by fusing the CT image and a SPECT image acquired at the same time point.
Furthermore, the preset image segmentation method is realized based on a deep learning network model or an atlas template registration method.
Still further, step S1 further includes the steps of:
before each piece of initial image data is segmented according to the preset image segmentation method, registration evaluation is carried out on the initial image data, and optimization is carried out according to a preset image registration algorithm.
Furthermore, the SPECT image is a SPECT static tomographic image obtained by a static tomographic acquisition and reconstruction method at a relatively stable period after a dynamic drug change process.
Further, in step S2, the method further includes the steps of: and carrying out pixel value processing on each initial estimated image according to a preset assignment method, wherein the preset assignment method is as follows:
and carrying out assignment processing of 1 or 0 on the pixel value according to whether the pixel point corresponds to human organ tissue or whether the medicine content corresponding to the pixel point reaches/does not reach an average value.
Still further, the maximum a posteriori probability method satisfies the following relationship:
wherein ,representing the initial estimated image, and +.>y j Representing the vector corresponding to the initial image data, A j A system transmission matrix representing the image reconstruction process at said point in time j,/and a method for reconstructing an image at said point in time j>Regarding the iteration parameter +.f representing the organ tissue image i at the time point j differently>Is satisfied, the constraint satisfying:
or:
still further, the deep learning network model is a Unet++ deep learning network model.
Compared with the related art, the application has the following beneficial technical effects: the method for reconstructing the dynamic image based on SPECT dynamic acquisition data solves the problem of solving all voxel values of the conventional tomographic image reconstruction, simplifies the problem of solving the weighting coefficients of the influences of different organs and tissues, reduces the sampling angle and the noise level of projection data required by tomographic data reconstruction at each time point by reducing the number of unknown variables in the image reconstruction process, and further can reconstruct the dynamic tomographic image based on the image distribution of different organs more accurately, thereby facilitating the subsequent quantitative analysis and clinical diagnosis of the image.
Drawings
FIG. 1 is a geometric schematic of a process of acquiring data from a three-dimensional distribution projection of an imaged object onto a detector;
fig. 2 is a flowchart illustrating steps of a method for reconstructing a dynamic image based on SPECT dynamic acquisition data according to an embodiment of the present application;
FIG. 3 is a schematic illustration of a segmentation process of an organ tissue image provided by an embodiment of the present application;
FIG. 4 is a schematic illustration of another segmentation process for an organ tissue image according to an embodiment of the present application;
FIG. 5 is a schematic view of a segmented initial estimated image provided by an embodiment of the present application;
FIG. 6 is a schematic diagram of pixel value statistics according to an embodiment of the present application;
FIG. 7 is an iteration parameter provided by an embodiment of the present applicationA comparison diagram of the solving result and the actual activity curve;
FIG. 8 is a diagram of another iteration parameter provided by an embodiment of the present applicationA comparison diagram of the solving result and the actual activity curve.
Detailed Description
The detailed description/examples set forth herein are specific embodiments of the application and are intended to be illustrative and exemplary of the concepts of the application and are not to be construed as limiting the scope of the application. In addition to the embodiments described herein, those skilled in the art will be able to adopt other obvious solutions based on the disclosure of the claims and specification, including any obvious alterations and modifications to the embodiments described herein, all within the scope of the present application.
Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a method for reconstructing a dynamic image based on SPECT dynamic acquisition data according to an embodiment of the present application, where the method includes the following steps:
s1, acquiring J initial image data acquired at J time points, and dividing each initial image data according to a preset image dividing method to obtain i organ tissue images.
Furthermore, the initial image data is a CT image or a fusion image obtained by fusing the CT image and a SPECT image acquired at the same time point.
Furthermore, the SPECT image is a SPECT static tomographic image obtained by a static tomographic acquisition and reconstruction method at a relatively stable period after a dynamic drug change process.
In one possible embodiment, the initial image data may be acquired data of the same angle, i.e., the SPECT device does not perform rotational sampling, but performs data acquisition only at a fixed angle; the data of one sampling angle can be corresponding to each time point, namely the rotation sampling speed of the SPECT equipment is basically consistent with the dynamic change time unit of the medicine; it is also possible that each time point corresponds to a set of data of sampling angles, i.e. the speed of SPECT rotational sampling is higher than the drug dynamics speed.
Furthermore, the preset image segmentation method is realized based on a deep learning network model or an atlas template registration method.
Still further, the deep learning network model is a Unet++ deep learning network model.
Still further, step S1 further includes the steps of:
before each piece of initial image data is segmented according to the preset image segmentation method, registration evaluation is carried out on the initial image data, and optimization is carried out according to a preset image registration algorithm.
S2, dividing each organ tissue image according to the preset image dividing method to obtain I initial estimated images corresponding to the organ tissue images.
Further, in step S2, the method further includes the steps of: and carrying out pixel value processing on each initial estimated image according to a preset assignment method, wherein the preset assignment method is as follows:
and carrying out assignment processing of 1 or 0 on the pixel value according to whether the pixel point corresponds to human organ tissue or whether the medicine content corresponding to the pixel point reaches/does not reach an average value.
Referring to fig. 3 and fig. 4, fig. 3 and fig. 4 are schematic diagrams of two segmentation processes of an organ tissue image provided by the embodiment of the present application, in which fig. 3 is an image of organ tissue positions such as kidney, liver, pancreas, spleen, etc., fig. 4 is an image of organ positions such as heart, lung, etc., fig. 3a and 4a are the initial image data, in the embodiment of the present application, CT images are shown, fig. 3b and 4b are schematic diagrams of a plurality of the organ tissue images obtained by segmentation, fig. 3c and 4c are schematic diagrams of the initial estimation images obtained by segmentation, further, according to the image of organ positions such as heart, lung, etc., in fig. 4, the specific segmented initial estimation image is shown in fig. 5, in which fig. 5a is the initial image data, fig. 5b is a schematic diagram of a plurality of organ tissue images such as heart, fig. 5c is an organ tissue image of lung, fig. 5d is an organ tissue image of liver, and fig. 5e is an organ tissue image of heart, and the statistics of pixel values is shown in fig. 6.
And S3, carrying out normalization processing on the pixel value of each initial estimated image.
S4, setting iteration parameters for the initial estimated image I obtained by dividing the organ tissue image I when the time point is j
S5, multiplying each pixel value in the initial estimated image by the iteration parameterAnd carrying out image reconstruction to obtain a current dynamic reconstruction estimation image.
S6, updating the iteration parameters according to a preset iteration method according to the initial image data and the current dynamic reconstruction estimation imageThe preset iteration method is a maximum likelihood estimation method or a maximum posterior probability method, and meanwhile, whether the preset iteration method reaches the preset iteration times or not is judged, if yes, the step S7 is executed; if not, iterating to the step S5.
Exemplary embodiments of the present application solve for the iteration parameters using a maximum posterior probability methodThe process of (1) is described:
defining a vector representing the initial image data as y and a vector representing the initial estimated image as x, there are:
Ax＝y(1)；
wherein A represents a system transmission matrix modeling a projection process from an image space to a projection data space, and an element A of an m-th row and an n-th column of the system transmission matrix m，n Normalized contribution probability of signal representing nth voxel of image vector to mth pixel of projection data, element A in practical implementation process m，n The attenuation, scattering, and factors that lead to reduced system resolution, including collimator, detector, focus, motion, etc., should be accurately mathematically modeled.
Since the vector y of the initial image data generally contains a certain level of direct noise, and the data quantity of x and y is large, and is difficult to directly solve, the solution of the relation (1) is converted into a problem of minimizing a cost function, and the following formula (2):
by minimizing the cost function in relation (2) to find an optimal estimate of xWherein the cost function generally comprises two terms, L (y, ax) represents the matching degree of the measured projection data and the initial estimated image x based on the current, and a conditional probability function or likelihood probability function aiming at the y statistical characteristic is generally adopted; f (f) p (x) Representing an a priori probability or penalty term for the initial estimate image x, typically used to constrain noise and artifacts in the image, -L (y, ax) +f p (x) Collectively known as posterior probability functions.
Still further, the maximum a posteriori probability method satisfies the following relationship:
wherein ,representing the initial estimated image, and +.>y j Representing the vector corresponding to the initial image data, A j A system transmission matrix representing the image reconstruction process at said point in time j,/and a method for reconstructing an image at said point in time j>Regarding the iteration parameter +.f representing the organ tissue image i at the time point j differently>Is satisfied, the constraint satisfying:
or:
in another possible embodiment of the present application, the iteration parameters are solved using a maximum likelihood estimation methodPlease refer to fig. 7 and 8, which are the iteration parameters corresponding to the organ tissues of fig. 3 and 4, respectively>The comparison diagram of the solving result and the actual activity curve, wherein the preset iteration times are set to be 16 times, the solid line is the result curve, and the dotted line is the actual curve.
S7, multiplying each pixel value in the initial image data by the iteration parameterAnd carrying out image reconstruction to obtain a final dynamic reconstruction image.
Compared with the related art, the application has the following beneficial technical effects: the method for reconstructing the dynamic image based on SPECT dynamic acquisition data solves the problem of solving all voxel values of the conventional tomographic image reconstruction, simplifies the problem of solving the weighting coefficients of the influences of different organs and tissues, reduces the sampling angle and the noise level of projection data required by tomographic data reconstruction at each time point by reducing the number of unknown variables in the image reconstruction process, and further can reconstruct the dynamic tomographic image based on the image distribution of different organs more accurately, thereby facilitating the subsequent quantitative analysis and clinical diagnosis of the image.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM) or the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", "front", "back", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
While the embodiments of the present application have been illustrated and described in connection with the drawings, what is presently considered to be the most practical and preferred embodiments of the application, it is to be understood that the application is not limited to the disclosed embodiments, but on the contrary, is intended to cover various equivalent modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (8)
1. A method for reconstructing a dynamic image based on SPECT dynamic acquisition data, the method comprising the steps of:
s1, acquiring J initial image data acquired at J time points, and dividing each initial image data according to a preset image dividing method to obtain i organ tissue images;
s2, dividing each organ tissue image according to the preset image dividing method to obtain I initial estimated images corresponding to the organ tissue images;
s3, carrying out normalization processing on pixel values of each initial estimated image;
s4, setting iteration parameters for the initial estimated image I obtained by dividing the organ tissue image I when the time point is j
S5, multiplying each pixel value in the initial estimated image by the iteration parameterPerforming image reconstruction to obtain a current dynamic reconstruction estimation image;
s6, updating the iteration parameters according to a preset iteration method according to the initial image data and the current dynamic reconstruction estimation imageThe preset iteration method is a maximum likelihood estimation method or a maximum posterior probability method, and meanwhile, whether the preset iteration method reaches the preset iteration times or not is judged, if yes, the step S7 is executed; if not, iterating to the step S5;
s7, multiplying each pixel value in the initial image data by the iteration parameterAnd carrying out image reconstruction to obtain a final dynamic reconstruction image.
2. The method for reconstructing a dynamic image based on SPECT dynamic acquisition data of claim 1 wherein the initial image data is a CT image or a fused image of the CT image fused with a SPECT image acquired at the same point in time.
3. The method for reconstructing a dynamic image based on SPECT dynamic acquisition data as recited in claim 1, wherein the preset image segmentation method is implemented based on a deep learning network model or on an atlas template registration method.
4. The method for reconstructing a dynamic image based on SPECT dynamic acquisition data of claim 1, wherein step S1 further comprises the steps of:
before each piece of initial image data is segmented according to the preset image segmentation method, registration evaluation is carried out on the initial image data, and optimization is carried out according to a preset image registration algorithm.
5. The method for reconstructing a dynamic image based on SPECT dynamic acquisition data of claim 2, wherein the SPECT image is a SPECT static tomographic image obtained by a static tomographic acquisition and reconstruction method at a relatively stationary phase after a drug dynamic change process.
6. The method for reconstructing a dynamic image based on SPECT dynamic acquisition data as recited in claim 1, wherein in step S2, further comprising the steps of: and carrying out pixel value processing on each initial estimated image according to a preset assignment method, wherein the preset assignment method is as follows:
and carrying out assignment processing of 1 or 0 on the pixel value according to whether the pixel point corresponds to human organ tissue or whether the medicine content corresponding to the pixel point reaches/does not reach an average value.
7. The method for reconstructing a dynamic image based on SPECT dynamic acquisition data of claim 1 wherein the maximum a posteriori probability method satisfies the relationship:
wherein ,representing the initial estimated image, and +.>y j Representing the vector corresponding to the initial image data, A j A system transmission matrix representing the image reconstruction process at said point in time j,/and a method for reconstructing an image at said point in time j>Regarding the iteration parameter +.f representing the organ tissue image i at the time point j differently>Is satisfied, the constraint satisfying:
or:
8. the method for reconstructing a dynamic image based on SPECT dynamic acquisition data of claim 3 wherein the deep learning network model is a unet++ deep learning network model.
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