CN111709897B - Domain transformation-based positron emission tomography image reconstruction method - Google Patents
Domain transformation-based positron emission tomography image reconstruction method Download PDFInfo
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- 238000002600 positron emission tomography Methods 0.000 title claims abstract description 32
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Abstract
The invention discloses a reconstruction method of a positron emission tomography image based on domain transformation. The method comprises the following steps: reconstructing a positron emission tomography image to obtain a first reconstructed image, wherein the first reconstructed image is an image which is not subjected to denoising treatment; performing domain transformation on the positron emission tomography image, performing filtering treatment, and reconstructing the filtered image to obtain a second reconstructed image; and taking the second reconstructed image as prior information, and conducting guided filtering on the first reconstructed image so as to obtain a fused reconstructed image. The invention can effectively eliminate noise and well preserve image details through guiding filtering, thereby improving the quality of low-dose image reconstruction.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to a reconstruction method of a positron emission tomography image based on domain transformation.
Background
Positron Emission Tomography (PET) is a functional imaging model and is an advanced clinical imaging technology in the field of nuclear medicine. PET can detect the activity of molecular level in tissues by specific injection of radioactive tracers and is widely applied to the fields of tumors, neurology, cardiology and the like.
However, injection of normal tracer doses poses a potential radiation risk to the patient, as gamma rays can cause ionization of the organic molecules, causing damage to the patient's body. However, the limitation of injection dosage and the limitation of acquisition time will cause the spatial resolution of positron emission imaging to be relatively poor and the noise level to be high, so that the quantitative interpretation of PET images is difficult. The high noise level of PET images can mask small but important lesions, blurring the edges of organs, further leading to diagnostic and quantitative errors. Furthermore, the image reconstruction speed is slow due to the large amount of data acquired and artifacts caused by possible movements of the patient due to the long scan time.
Therefore, the research and development of the novel low-dose PET imaging method can ensure the PET imaging quality and reduce the harmful radiation dose, and has important scientific significance and application prospect in the field of medical diagnosis.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a reconstruction method of positron emission tomography based on domain transformation, which is used for completing image reconstruction based on injection of low-dose tracer sample and obtaining a clearer reconstructed image.
The invention provides a reconstruction method of a positron emission tomography image based on domain transformation, which comprises the following steps:
reconstructing a positron emission tomography image to obtain a first reconstructed image, wherein the first reconstructed image is an image which is not subjected to denoising treatment;
performing domain transformation on the positron emission tomography image, performing filtering treatment, and reconstructing the filtered image to obtain a second reconstructed image;
and taking the second reconstructed image as prior information, and conducting guided filtering on the first reconstructed image so as to obtain a fused reconstructed image.
In one embodiment, the first reconstructed image is obtained according to the steps of:
performing attenuation correction processing on the positron emission tomography image to obtain corrected projection data;
reconstructing the corrected projection data by a desired maximum method to obtain the first reconstructed image.
In one embodiment, the second reconstructed image is obtained according to the steps of:
performing attenuation correction on the positron emission tomography image to obtain corrected projection data;
performing Anscombe transformation on the corrected projection data, and filtering the transformed projection data to eliminate Gaussian distribution noise;
and performing Anscombe inverse transformation on the filtered data to obtain the second reconstructed image.
In one embodiment, the transformed projection data is filtered using a non-local mean method.
In one embodiment, the Anscombe transform process is expressed as:
where y represents corrected projection data.
In one embodiment, using the second reconstructed image as a priori information, performing guided filtering on the first reconstructed image to obtain a fused reconstructed image, including:
for each pixel of the second reconstructed image, finding a plurality of similar neighbors, and normalizing the matrix to obtain a normalized kernel matrix;
and guiding the first reconstructed image to filter by using the normalized kernel matrix to obtain the fused reconstructed image.
In one embodiment, K similar neighbors are found for each pixel of the second reconstructed image using a K-nearest neighbor method.
In one embodiment, the fused reconstructed image is represented as:
x＝K·x EM
x EM representing the first reconstructed image, K represents the kernel matrix.
Compared with the prior art, the invention has the advantages that the noise projection data with stable variance is transformed and NLM (non local mean filtering) is carried out. Then, it was reconstructed using the ML-EM (Maximum likelihood-expectation maximization) algorithm. And (3) taking the image obtained in the step as priori information, and guiding the image after common electromagnetic reconstruction through GK filtering. The resulting restored image not only suppresses noise, but also retains image details. The framework includes a complete reconstruction and restoration of PET images. The invention solves the technical problem of low-dose image quality improvement.
Other features of the present invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart of a method of reconstruction of positron emission tomography based on domain transformation in accordance with an embodiment of the invention;
fig. 2 is an exemplary process of a reconstruction method for domain transformation-based positron emission tomography in accordance with an embodiment of the invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
The embodiment of the invention provides a domain transformation-based PET image reconstruction method which can be used for reconstructing a low-dose PET image, and specifically comprises the following steps of:
step S110, reconstructing the PET image to obtain a first reconstructed image including noise.
For example, projection data subjected to only attenuation correction (ai represents an attenuation coefficient in fig. 2) is reconstructed by using an ML-EM (expectation maximization) algorithm to obtain x EM The reconstruction process is expressed as:
where y represents measured PET data (also referred to as acquired scientific data) and can be considered as a set of independent Poisson random variables; p is the system matrix; r represents background events such as random noise and scattering; n is the total number of pixels in the image.
The reconstructed image obtained in this step is marked x0 in fig. 2. The reconstructed image obtained in this way contains considerable noise, but it also contains most of the structural information of the PET image.
And step S120, the PET image is reconstructed after being filtered in a transformation domain, and a denoised second reconstructed image is obtained.
Specifically, for PET detector projection data y that obeys an independent poisson distribution, the more intractable poisson noise is first transformed into gaussian noise by an ascombe transform. The converted signal can be expressed as:
then, the transformed projection data is NLM filtered to remove gaussian noise.
Next, the filtered data is inverse transformed by ascombe to obtain y', denoted as:
finally, reconstructing the processed projection data by adopting a traditional ML-EM algorithm to obtain x TD Expressed as:
the reconstructed image obtained in this step is denoted x1 in fig. 2, which provides guidance for the next step of guided filtering, which may achieve noise suppression.
It should be understood that other types of filtering than NLM filtering, or other transform modes may be used to implement the domain transform.
And step S130, the second reconstructed image is taken as prior information, and the first reconstructed image is guided and filtered, so that a fused reconstructed image is obtained.
This step implements guided kernel filtering, e.g., a k-nearest neighbor algorithm (KNN) may be employed to construct the sparse matrix. KNN finds K similar neighbors for each pixel and normalizes the matrix to obtain normalized kernel matrix K. And then GK filtering is carried out under the guidance of the second reconstructed image, so as to obtain a final fused reconstructed image x, which is expressed as:
x＝K·x EM (5)
it should be noted that other clustering algorithms may be used, such as k-means algorithm, for similar neighbors of each pixel. The image reconstruction method is not limited to the ML-EM method.
In summary, the invention solves the technical problem of improving the quality of low-dose images, and the invention transforms the noise projection data with stable variance, reconstructs the noise projection data after filtering, further uses the obtained reconstructed image as prior information, and guides the image after common electromagnetic reconstruction through GK filtering. The resulting restored image not only suppresses noise, but also retains image details.
Compared with the prior art, the image reconstruction method provided by the invention comprises two key elements. The first element is a transform domain that is denoised based on projection data. This way, taking into account the statistical properties of the projection data, the data following the poisson distribution is converted into data following the gaussian distribution, so that the original noise following the poisson distribution can be easily removed. Another element is the reconstructed pilot filtering. Although the projection domain filtering can remove noise more effectively, some details are lost, resulting in loss of details in the reconstructed image. Therefore, lost structural information is recovered through a guided filtering method, so that the image details of the denoised image are well reserved, and the structure is clearer.
The present invention may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (5)
1. A method of reconstructing a domain transformation-based positron emission tomography image, comprising the steps of:
reconstructing a positron emission tomography image to obtain a first reconstructed image, wherein the first reconstructed image is an image which is not subjected to denoising treatment;
performing domain transformation on the positron emission tomography image, performing filtering treatment, and reconstructing the filtered image to obtain a second reconstructed image;
taking the second reconstructed image as prior information, and conducting guided filtering on the first reconstructed image to obtain a fused reconstructed image;
wherein the second reconstructed image is obtained according to the steps of:
performing attenuation correction on the positron emission tomography image to obtain corrected projection data;
performing Anscombe transformation on the corrected projection data, and filtering the transformed projection data to eliminate Gaussian distribution noise;
performing Anscombe inverse transformation on the filtered data to obtain the second reconstructed image;
the step of performing guided filtering on the first reconstructed image by taking the second reconstructed image as prior information to obtain a fused reconstructed image comprises the following steps:
for each pixel of the second reconstructed image, finding a plurality of similar neighbors, and normalizing the matrix to obtain a normalized kernel matrix;
guiding the first reconstructed image to be filtered by the normalized kernel matrix to obtain the fused reconstructed image;
wherein the fused reconstructed image is represented as:
x＝K·x EM
x EM representing a first reconstructed image, K representing a kernel matrix;
wherein the first reconstructed image is obtained according to the steps of:
performing attenuation correction processing on the positron emission tomography image to obtain corrected projection data;
reconstructing the corrected projection data by a desired maximum method to obtain the first reconstructed image, the reconstruction process being expressed as:
wherein y represents the acquired positron emission tomography data and is a set of independent poisson random variables; p is the system matrix; r denotes a background event, for identifying random noise and scattering; n is the total number of pixels in the image.
2. The method of claim 1, wherein the transformed projection data is filtered using a non-local mean method.
3. The method of claim 1, wherein the ascombe transformation procedure is expressed as:
where y represents corrected projection data.
4. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor realizes the steps of the method according to claim 1.
5. A computer device comprising a memory and a processor, on which memory a computer program is stored which can be run on the processor, characterized in that the processor implements the steps of the method of claim 1 when executing the program.
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