CN111709897B - Domain transformation-based positron emission tomography image reconstruction method - Google Patents

Abstract

The invention discloses a reconstruction method of positron emission tomography images based on domain transformation. The method includes: reconstructing the positron emission tomography image to obtain a first reconstructed image, which is an image without denoising processing; performing domain transformation on the positron emission tomography image and then filtering the processed image. The filtered image is reconstructed to obtain a second reconstructed image; using the second reconstructed image as prior information, guided filtering is performed on the first reconstructed image to obtain a fused reconstructed image. The present invention can effectively eliminate noise and well retain image details through guided filtering, thereby improving the quality of low-dose image reconstruction.

Description

A reconstruction method of positron emission tomography images based on domain transformation

Technical field

The present invention relates to the technical field of medical image processing, and more specifically, to a reconstruction method of positron emission tomography images based on domain transformation.

Background technique

Positron emission tomography (PET) is a functional imaging model and a relatively advanced clinical imaging technology in the field of nuclear medicine. PET can detect activity at the molecular level within tissues through specific injection of radioactive tracers, and is widely used in oncology, neurology, cardiology and other fields.

However, injecting normal tracer doses poses a potential radiation risk to the patient, as gamma rays can cause ionization of organic molecules, causing damage to the patient's body. However, limitations in injection dose and acquisition time will result in relatively poor spatial resolution and high noise levels in positron emission imaging, making quantitative interpretation of PET images difficult. High noise levels in PET images can mask small but important lesions and blur organ edges, further leading to diagnostic and quantitative errors. In addition, image reconstruction is slow due to the large amount of data collected, and artifacts caused by possible patient movement occur due to long scanning times.

Therefore, research and development of new low-dose PET imaging methods can not only ensure the quality of PET imaging but also reduce harmful radiation doses, which has important scientific significance and application prospects in the field of medical diagnosis.

Contents of the invention

The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide a reconstruction method of positron emission tomography based on domain transformation, which can complete image reconstruction based on injection of low-dose tracer sampling and obtain a clearer reconstructed image.

The present invention provides a method for reconstructing positron emission tomography images based on domain transformation, which includes the following steps:

Reconstruct the positron emission tomography image to obtain a first reconstructed image, where the first reconstructed image is an image without denoising processing;

The positron emission tomography image is subjected to domain transformation and filtering processing, and the filtered image is reconstructed to obtain a second reconstructed image;

Using the second reconstructed image as prior information, guided filtering is performed on the first reconstructed image to obtain a fused reconstructed image.

In one embodiment, the first reconstructed image is obtained according to the following steps:

Perform attenuation correction processing on the positron emission tomography image to obtain corrected projection data;

The corrected projection data is reconstructed by the expectation maximum method to obtain the first reconstructed image.

In one embodiment, the second reconstructed image is obtained according to the following steps:

The positron emission tomography image is attenuated and corrected to obtain corrected projection data;

Perform Anscombe transformation on the corrected projection data, and filter the transformed projection data to eliminate Gaussian distribution noise;

Perform Anscombe inverse transform on the filtered data to obtain the second reconstructed image.

In one embodiment, a non-local mean method is used to filter the transformed projection data.

In one embodiment, the Anscombe transformation process is expressed as:

where y represents the corrected projection data.

In one embodiment, using the second reconstructed image as prior information, guided filtering is performed on the first reconstructed image to obtain a fused reconstructed image, including:

For each pixel of the second reconstructed image, find multiple similar neighbors and normalize the matrix to obtain a normalized kernel matrix;

The normalized kernel matrix is used to guide the first reconstructed image filtering to obtain the fused reconstructed image.

In one embodiment, a K nearest neighbor method is used to find k similar neighbors for each pixel of the second reconstructed image.

In one embodiment, the fused reconstructed image is expressed as:

x＝K· _xEM

x _EM represents the first reconstructed image, and K represents the kernel matrix.

Compared with the prior art, the advantage of the present invention is that the noise projection data with stable variance is transformed and NLM (non-local mean filtering) filtered. Then, use the ML-EM (Maximum likelihood-expectation maximization) algorithm to reconstruct it. The image obtained in this step is used as prior information, and the image after ordinary electromagnetic reconstruction is guided through GK filtering. The resulting restored image not only suppresses noise but also preserves image details. The framework includes complete reconstruction and restoration of PET images. The invention solves the technical problem of improving low-dose image quality.

Other features of the invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention with reference to the accompanying drawings.

Description of the 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.

Figure 1 is a flow chart of a reconstruction method for positron emission tomography based on domain transformation according to an embodiment of the present invention;

Figure 2 is an example process of a reconstruction method for domain transformation-based positron emission tomography according to one embodiment of the present invention.

Detailed ways

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 components and steps, numerical expressions and numerical values set forth in these examples do not limit the scope of the invention unless otherwise specifically stated.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application or uses.

Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered a part of the specification.

In all examples shown and discussed herein, any specific values are to be construed as illustrative only and not as limiting. Accordingly, other examples of the exemplary embodiments may have different values.

It should be noted that similar reference numerals and letters refer to similar items in the following figures, so that once an item is defined in one figure, it does not need further discussion in subsequent figures.

Embodiments of the present invention provide a PET image reconstruction method based on domain transformation, which can be used for low-dose PET image reconstruction. As shown in Figure 1 and Figure 2, the method specifically includes the following steps:

Step S110: Reconstruct the PET image to obtain a first reconstructed image containing noise.

For example, the projection data that has only been attenuated corrected (ai represents the attenuation coefficient in Figure 2) is reconstructed using the ML-EM (expectation maximization) algorithm to obtain x _EM , and the reconstruction process is expressed as:

Among them, y represents the measured PET data (also called the obtained sinogram data), which can be considered as a collection 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 labeled x0 in Figure 2. The reconstructed image obtained in this way contains considerable noise, but it also contains most of the structural information of the PET image.

Step S120: Filter the PET image in the transform domain and then reconstruct it to obtain a denoised second reconstructed image.

Specifically, for the PET detector projection data y that obeys an independent Poisson distribution, the more difficult to process Poisson noise is first converted into Gaussian noise through Anscombe transformation. The converted signal can be expressed as:

Then, NLM filtering is performed on the transformed projection data to remove Gaussian noise.

Next, perform Anscombe inverse transformation on the filtered data to obtain y', which is expressed as:

Finally, the traditional ML-EM algorithm is used to reconstruct the processed projection data, and x _TD is obtained, expressed as:

The reconstructed image obtained in this step is marked as x1 in Figure 2, which provides guidance for the next step of guided filtering, which can achieve the purpose of suppressing noise.

It should be understood that other types of filtering methods other than NLM filtering can also be used, or other transformation methods can be used to implement domain transformation.

Step S130: Using the second reconstructed image as prior information, perform guided filtering on the first reconstructed image to obtain a fused reconstructed image.

This step implements guided kernel filtering. For example, the k-nearest neighbor algorithm (KNN) can be used to construct a sparse matrix. KNN finds k similar neighbors for each pixel and normalizes the matrix to obtain the normalized kernel matrix K. Then GK filtering is performed under the guidance of the second reconstructed image to obtain the final fused reconstructed image x, expressed as:

x＝K· _xEM (5)

It should be noted that other clustering algorithms can also be used to identify similar neighbors for each pixel, such as the k-means algorithm. In addition, the image reconstruction method is not limited to the ML-EM method.

In summary, the present invention solves the technical problem of improving low-dose image quality by transforming the noise projection data with stable variance and reconstructing it after filtering, and then uses the obtained reconstructed image as prior information, through GK filter guides the image after ordinary electromagnetic reconstruction. The resulting restored image not only suppresses noise but also preserves image details.

Compared with the existing technology, the image reconstruction method proposed by the present invention contains two key elements. The first element is a transform domain based on projection data denoising. This method takes into account the statistical characteristics of the projection data and transforms the data that obeys the Poisson distribution into the data that obeys the Gaussian distribution, so that the original noise that obeys the Poisson distribution can be easily removed. Another element is reconstructed pilot filtering. Although projection domain filtering can remove noise more effectively, it will also lose some details, resulting in the loss of details in the reconstructed image. Therefore, the lost structural information is restored through the guided filtering method, so that the denoised image retains the image details well and the structure is clearer.

The invention may be a system, method and/or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to implement various aspects of the invention.

Computer-readable storage media may be tangible devices that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: 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), Compact Disk Read Only Memory (CD-ROM), Digital Versatile Disk (DVD), Memory Stick, Floppy Disk, Mechanical Coding Device, such as a printer with instructions stored on it. Protruding structures in hole cards or grooves, and any suitable combination of the above. As used herein, computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or through electrical wires. transmitted electrical signals.

Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to various computing/processing devices, 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 transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter 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 on a computer-readable storage medium in the respective computing/processing device .

Computer program instructions for performing operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages. Source code or object code written in any combination of object-oriented programming languages - such as Smalltalk, C++, etc., and conventional procedural programming languages - such as the "C" language or similar programming languages. The computer-readable program instructions may execute 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 implement. In situations involving remote computers, the remote computer can 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 it can be connected to an external computer (such as an Internet service provider through the Internet). connect). In some embodiments, by utilizing state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can Computer readable program instructions are executed to implement various aspects of the invention.

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, a special-purpose computer, or other programmable data processing apparatus, thereby producing a machine that, when executed by the processor of the computer or other programmable data processing apparatus, , resulting in an apparatus that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium. These instructions cause the computer, programmable data processing device and/or other equipment to work in a specific manner. Therefore, the computer-readable medium storing the instructions includes An article of manufacture that includes instructions that implement aspects of the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.

Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other equipment, causing a series of operating steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executed on a computer, other programmable data processing apparatus, or other equipment to implement the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

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 that embody one or more elements for implementing the specified logical function(s). Executable instructions. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two consecutive blocks may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts. , or can be implemented using a combination of specialized hardware and computer instructions. It is well known to those skilled in the art that implementation through hardware, implementation through software, and implementation through a combination of software and hardware are all equivalent.

The embodiments of the present invention have been described above. The above description is illustrative, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements in the market of the embodiments, or to enable other persons 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 reconstruction method of positron emission tomography images based on domain transformation, including the following steps: Reconstruct the positron emission tomography image to obtain a first reconstructed image, where the first reconstructed image is an image without denoising processing; The positron emission tomography image is subjected to domain transformation and filtering processing, and the filtered image is reconstructed to obtain a second reconstructed image; Using the second reconstructed image as a priori information, perform guided filtering on the first reconstructed image to obtain a fused reconstructed image; Wherein, the second reconstructed image is obtained according to the following steps: The positron emission tomography image is attenuated and corrected to obtain corrected projection data; Perform Anscombe transformation on the corrected projection data, and filter the transformed projection data to eliminate Gaussian distribution noise; Perform an Anscombe inverse transform on the filtered data to obtain the second reconstructed image; Wherein, using the second reconstructed image as a priori information, guided filtering is performed on the first reconstructed image to obtain a fused reconstructed image, including: For each pixel of the second reconstructed image, find multiple similar neighbors and normalize the matrix to obtain a normalized kernel matrix; Use the normalized kernel matrix to guide the first reconstructed image filtering to obtain the fused reconstructed image; Wherein, the fused reconstructed image is expressed as: x＝K· _xEM x _EM represents the first reconstructed image, K represents the kernel matrix; Wherein, the first reconstructed image is obtained according to the following steps: Perform attenuation correction processing on the positron emission tomography image to obtain corrected projection data; The corrected projection data is reconstructed through the expectation maximum method to obtain the first reconstructed image. The reconstruction process is expressed as: Among them, y represents the obtained positron emission tomography imaging data, which is a collection of independent Poisson random variables; P is the system matrix; r represents the background event, used to identify 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 according to claim 1, wherein the Anscombe transformation process is expressed as: where y represents the corrected projection data. 4. A computer-readable storage medium having a computer program stored thereon, wherein the steps of the method according to claim 1 are implemented when the program is executed by a processor. 5. A computer device, comprising a memory and a processor, and a computer program capable of running on the processor is stored on the memory, characterized in that when the processor executes the program, the method of claim 1 is implemented Method steps.