CN109741411B - Low-dose PET image reconstruction method, device, equipment and medium based on gradient domain - Google Patents
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Abstract
Description
技术领域technical field
本发明属于医学PET成像技术领域,尤其涉及一种基于梯度域的低剂量PET图像重建方法、装置、设备及介质。The invention belongs to the technical field of medical PET imaging, and in particular relates to a gradient-domain-based low-dose PET image reconstruction method, device, equipment and medium.
背景技术Background technique
正电子发射断层成像(Positron Emission Tomography,简称PET)是一种发射型成像技术(Emission Tomography,简称ET),它通过把放射性药物注入体内的方法来显示不同组织的新陈代谢情况。PET技术是继计算机断层成像(Computed Tomography,简称CT)和磁共振成像(Magnetic Resonance Imaging,简称MRI)之后应用于临床的一种新型影像技术,PET技术在肿瘤学、心血管疾病学、神经系统疾病研究、以及新药开发研究等领域中显示出卓越的性能。Positron Emission Tomography (PET for short) is an emission imaging technique (Emission Tomography, ET for short), which displays the metabolism of different tissues by injecting radiopharmaceuticals into the body. PET technology is a new type of imaging technology applied clinically after Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). It has shown excellent performance in the fields of disease research and new drug development research.
在PET成像中,放射性药物实际上是个分子载体,它依附于特定的生理组织或病理过程。放射性物质在药物的带领下在人体内有目的的分布。PET成像的目的实际上就是得到放射性物质在人体内部的分布图,它的工作原理是:将一些放射性核元素,如O-15、C-11、N-13和F-18等标记在人体代谢所需的化合物上,然后通过手臂静脉血管注射等方式输入受检者体内。标记化合物在参与体内代谢的过程中,放射性核元素发生衰变,释放出正电子(带一个正电荷的电子),正电子与其周围的(带负电)电子发生湮灭,产生两个能量为511keV的伽马光子。这对光子在一条直线上朝相反的方向射出,利用体外的伽马照相机可以探测到特定区域放射的所有光子,然后设计一定的算法,就可以近似得到放射性物质在人体内部的分布情况。In PET imaging, radiopharmaceuticals are actually molecular carriers that attach to specific physiological tissues or pathological processes. Radioactive substances are purposefully distributed in the human body under the leadership of drugs. The purpose of PET imaging is actually to obtain the distribution map of radioactive substances in the human body. Its working principle is: to mark some radioactive nuclear elements, such as O-15, C-11, N-13 and F-18, etc. in the human body. The desired compound is then injected into the subject's body through intravenous injection in the arm. When the labeled compound participates in the metabolism of the body, the radioactive nuclear element decays and releases a positron (an electron with a positive charge), which annihilates with its surrounding (negatively charged) electrons to produce two Gamma atoms with an energy of 511keV. Ma Photon. The pair of photons shoot out in opposite directions on a straight line, and the gamma camera outside the body can detect all the photons emitted in a specific area, and then design a certain algorithm to approximate the distribution of radioactive substances in the human body.
由于在PET检查中使用的放射性药物会对近距离接触该药物的人员产生辐射,而受到辐射的人员患癌的几率会远高于正常人,同时放射性药物的消耗在PET检查的成本中占有一定比重。因此,根据国际放射防护委员会(International Commission onRadiological Protection,简称ICRP)提出的合理使用低剂量(As Low As ReasonablyAchievable,简称ALARA)原则,在PET临床诊断时,以期用最小的剂量获得满足临床需求的图像,尽量降低对患者的辐射剂量。Because the radiopharmaceuticals used in PET examinations will produce radiation to those who are in close contact with the medicines, and the chances of people who are exposed to the radiation will be much higher than normal people, and the consumption of radiopharmaceuticals occupies a certain part of the cost of PET examinations. proportion. Therefore, according to the principle of As Low As Reasonably Achievable (ALARA) proposed by the International Commission on Radiological Protection (ICRP), in the clinical diagnosis of PET, it is hoped that the minimum dose can be used to obtain images that meet clinical needs. , to minimize the radiation dose to the patient.
然而,在对低剂量采样得到的测量数据进行PET图像重建时,现有传统的PET图像重建算法重建图像的速度慢,进而使得重建图像产生运动伪影,这些伪影将会直接影响医生的诊断行为。However, when performing PET image reconstruction on the measurement data obtained by low-dose sampling, the existing traditional PET image reconstruction algorithm reconstructs the image slowly, which in turn causes motion artifacts in the reconstructed image, which will directly affect the doctor's diagnosis Behavior.
发明内容Contents of the invention
本发明的目的在于提供一种基于梯度域的低剂量PET图像重建方法、装置、设备及介质,旨在解决由于现有技术无法提供一种有效的低剂量PET图像重建方法,导致低剂量PET图像重建速度慢、且重建图像质量差的问题。The purpose of the present invention is to provide a low-dose PET image reconstruction method, device, equipment and medium based on the gradient domain, aiming to solve the problem of low-dose PET image reconstruction due to the inability of the prior art to provide an effective low-dose PET image reconstruction method. The reconstruction speed is slow and the reconstruction image quality is poor.
一方面,本发明提供了一种基于梯度域的低剂量PET图像重建方法,所述方法包括下述步骤:On the one hand, the present invention provides a kind of low-dose PET image reconstruction method based on gradient field, and described method comprises the following steps:
当接收到低剂量PET图像的重建请求时,获取通过PET设备采集到的投影数据,并获取所述PET设备的系统矩阵;When a reconstruction request of a low-dose PET image is received, the projection data collected by the PET device is obtained, and the system matrix of the PET device is obtained;
根据所述投影数据以及所述系统矩阵,通过预设的PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像;performing image reconstruction on a pre-initialized PET image to be reconstructed through a preset PET image reconstruction algorithm according to the projection data and the system matrix, to obtain an initial reconstructed PET image;
根据所述初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到所述初始重建PET图像对应的目标重建PET图像。According to the initial reconstructed PET image, Lagrangian multiplication is used to jointly optimize and solve the pre-built image reconstruction equation and the pre-built gradient domain image feature selection equation to obtain the target reconstructed PET image corresponding to the initial reconstructed PET image.
另一方面,本发明提供了一种基于梯度域的低剂量PET图像重建装置,所述装置包括:In another aspect, the present invention provides a gradient domain-based low-dose PET image reconstruction device, the device comprising:
参数获取单元,用于当接收到低剂量PET图像的重建请求时,获取通过PET设备采集到的投影数据,并获取所述PET设备的系统矩阵;A parameter acquisition unit, configured to acquire the projection data collected by the PET device and obtain the system matrix of the PET device when a reconstruction request of the low-dose PET image is received;
初始重建单元,用于根据所述投影数据以及所述系统矩阵,通过预设的PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像;以及An initial reconstruction unit, configured to perform image reconstruction on a pre-initialized PET image to be reconstructed through a preset PET image reconstruction algorithm according to the projection data and the system matrix, to obtain an initial reconstructed PET image; and
重建图像获得单元,用于根据所述初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到所述初始重建PET图像对应的目标重建PET图像。The reconstructed image obtaining unit is used to jointly optimize and solve the pre-built image reconstruction equation and the pre-built gradient domain image feature selection equation according to the initial reconstructed PET image by using Lagrangian multiplication to obtain the initial reconstructed PET image Corresponding target reconstructed PET images.
另一方面,本发明还提供了一种计算设备,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序,所述处理器执行所述计算机程序时实现如上述基于梯度域的低剂量PET图像重建方法所述的步骤。On the other hand, the present invention also provides a computing device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program, the Steps as described above for gradient-domain-based low-dose PET image reconstruction method.
另一方面,本发明还提供了一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,所述计算机程序被处理器执行时实现如上述基于梯度域的低剂量PET图像重建方法所述的步骤。On the other hand, the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned low-dose PET image reconstruction based on the gradient domain is realized. steps described in the method.
本发明根据PET设备采集到的投影数据以及该PET设备的系统矩阵,通过PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像,根据初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到初始重建PET图像对应的目标重建PET图像,从而提高了低剂量PET图像的重建速度,且降低重建图像的伪影程度,进而提高了低剂量PET图像重建的图像质量。According to the projection data collected by the PET equipment and the system matrix of the PET equipment, the present invention reconstructs the pre-initialized PET image to be reconstructed through the PET image reconstruction algorithm to obtain the initial reconstructed PET image. According to the initial reconstructed PET image, the Lager Langer multiplication jointly optimizes the pre-built image reconstruction equation and the pre-built gradient domain image feature selection equation to obtain the target reconstructed PET image corresponding to the initial reconstructed PET image, thereby improving the reconstruction speed of low-dose PET images and reducing The degree of artifacts in the reconstructed images, thereby improving the image quality of low-dose PET image reconstruction.
附图说明Description of drawings
图1是本发明实施例一提供的基于梯度域的低剂量PET图像重建方法的实现流程图;Fig. 1 is a flow chart of the realization of the low-dose PET image reconstruction method based on the gradient domain provided by Embodiment 1 of the present invention;
图2是本发明实施例一中采用Bregman迭代方法对拉格朗日方程进行迭代求解的实现流程图;Fig. 2 is the realization flow chart that adopts Bregman iterative method to iteratively solve Lagrangian equation in embodiment one of the present invention;
图3是本发明实施例二提供的基于梯度域的低剂量PET图像重建装置的结构示意图;Fig. 3 is a schematic structural diagram of a low-dose PET image reconstruction device based on a gradient domain provided by Embodiment 2 of the present invention;
图4是本发明实施例二提供的基于梯度域的低剂量PET图像重建装置的优选结构示意图;Fig. 4 is a schematic diagram of an optimal structure of a low-dose PET image reconstruction device based on a gradient domain provided by Embodiment 2 of the present invention;
图5是本发明实施例二提供的基于梯度域的低剂量PET图像重建装置的又一优选结构示意图;以及Fig. 5 is a schematic diagram of another preferred structure of the gradient domain-based low-dose PET image reconstruction device provided by Embodiment 2 of the present invention; and
图6是本发明实施例三提供的计算设备的结构示意图。FIG. 6 is a schematic structural diagram of a computing device provided by Embodiment 3 of the present invention.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
以下结合具体实施例对本发明的具体实现进行详细描述:The specific realization of the present invention is described in detail below in conjunction with specific embodiment:
实施例一:Embodiment one:
图1示出了本发明实施例一提供的基于梯度域的低剂量PET图像重建方法的实现流程,为了便于说明,仅示出了与本发明实施例相关的部分,详述如下:Figure 1 shows the implementation process of the gradient domain-based low-dose PET image reconstruction method provided by Embodiment 1 of the present invention. For the convenience of illustration, only the parts related to the embodiment of the present invention are shown, and the details are as follows:
在步骤S101中,当接收到低剂量PET图像的重建请求时,获取通过PET设备采集到的投影数据,并获取PET设备的系统矩阵。In step S101, when a reconstruction request of a low-dose PET image is received, the projection data collected by the PET device is obtained, and the system matrix of the PET device is obtained.
本发明实施例适用于医学图像处理平台、系统或设备,例如个人计算机、服务器等。当接收到对低剂量PET图像进行重建的请求时,获取通过PET设备在低剂量条件下采集到的欠采样投影数据,并获取PET设备的系统矩阵,该系统矩阵是根据PET设备的几何结构信息计算得到的。Embodiments of the present invention are applicable to medical image processing platforms, systems or devices, such as personal computers, servers, and the like. When a request for reconstruction of a low-dose PET image is received, the under-sampled projection data collected by the PET device under low-dose conditions is obtained, and the system matrix of the PET device is obtained, which is based on the geometric structure information of the PET device calculated.
在步骤S102中,根据投影数据以及系统矩阵,通过预设的PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像。In step S102, according to the projection data and the system matrix, the pre-initialized PET image to be reconstructed is reconstructed through a preset PET image reconstruction algorithm to obtain an initial reconstructed PET image.
在本发明实施例中,根据投影数据以及系统矩阵,通过预设的PET图像重建算法对预先初始化的待重建PET图像进行预设次数的迭代操作,以对待重建PET图像进行图像重建,获得初始重建PET图像,其中,待重建PET图像是二维图像,预设的PET图像重建算法为最大似然期望最大算法(Maximum Likelihood Expectation Maximized,简称MLEM)或者有序子集期望值最大算法(Ordered Subset Expectation Maximization,简称OSEM)或者最大后验概率算法(Maximum A Posterior,MAP)。In the embodiment of the present invention, according to the projection data and the system matrix, the pre-initialized PET image to be reconstructed is iteratively operated for a preset number of times through the preset PET image reconstruction algorithm, so as to perform image reconstruction on the PET image to be reconstructed, and obtain the initial reconstruction PET image, wherein the PET image to be reconstructed is a two-dimensional image, and the preset PET image reconstruction algorithm is Maximum Likelihood Expectation Maximized (MLEM for short) or Ordered Subset Expectation Maximization algorithm (Ordered Subset Expectation Maximization) , referred to as OSEM) or Maximum A Posterior probability algorithm (Maximum A Posterior, MAP).
在初始化待重建PET图像时,作为示例地,将待重建PET图像的像素值都初始化为零。When initializing the PET image to be reconstructed, as an example, all pixel values of the PET image to be reconstructed are initialized to zero.
在步骤S103中,根据初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到初始重建PET图像对应的目标重建PET图像。In step S103, according to the initial reconstructed PET image, the pre-constructed image reconstruction equation and the pre-constructed gradient domain image feature selection equation are jointly optimized and solved by Lagrangian multiplication, and the target reconstructed PET image corresponding to the initial reconstructed PET image is obtained .
在本发明实施例中,采用拉格朗日乘法将预先构建的图像重建方程yu=Gum和预先构建的梯度域图像特征选取方程进行联立,得到对应的拉格朗日方程 再对该拉格朗日方程进行优化求解,最终得到初始重建PET图像对应的目标重建PET图像,其中,yu为投影数据,Gu为系统矩阵,m为待重建PET图像(也即目标重建PET图像),v1为预设的权重参数,Rl为图像块提取矩阵,即根据该Rl从梯度图像ω中提取l个图像块,D为梯度图像ω的特征矩阵,αl为从梯度图像ω中提取出的第l个图像块对应的特征向量,ω(i)表示初始重建PET图像对应的水平/垂直梯度图像,i∈{1,2}表示所述梯度图像ω的方向(水平/垂直),L表示对特征向量的稀疏度的控制系数。In the embodiment of the present invention, the pre-built image reconstruction equation y u =G u m and the pre-built gradient domain image feature selection equation are combined by Lagrangian multiplication Simultaneously, get the corresponding Lagrangian equation Then optimize and solve the Lagrangian equation, and finally obtain the target reconstructed PET image corresponding to the initial reconstructed PET image, where y u is the projection data, G u is the system matrix, and m is the PET image to be reconstructed (that is, the target reconstruction PET image), v 1 is the preset weight parameter, R l is the image block extraction matrix, that is, extract l image blocks from the gradient image ω according to the R l , D is the feature matrix of the gradient image ω, and α l is from The feature vector corresponding to the lth image block extracted from the gradient image ω, ω (i) represents the horizontal/vertical gradient image corresponding to the initial reconstructed PET image, and i∈{1,2} represents the direction of the gradient image ω ( Horizontal/vertical), L represents the control coefficient for the sparsity of the feature vector.
在本发明实施例中,优选地,将特征向量稀疏度的控制系数L设置为5,从而更好的降低通过学习到的特征矩阵和特征向量稀疏表示的PET图像的噪声。In the embodiment of the present invention, preferably, the control coefficient L of the feature vector sparsity is set to 5, so as to better reduce the noise of the PET image sparsely represented by the learned feature matrix and feature vector.
在采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解时,优选地,采用Bregman迭代方法对由图像重建方程和梯度域图像特征选取方程联立的拉格朗日方程进行迭代求解,从而提高了PET图像的重建速度。When Lagrange multiplication is used to jointly optimize and solve the pre-built image reconstruction equation and the pre-built gradient domain image feature selection equation, preferably, the Bregman iterative method is used to combine the image reconstruction equation and the gradient domain image feature selection equation. Iteratively solves the established Lagrangian equation, thus improving the reconstruction speed of PET images.
进一步优选地,采用Bregman迭代方法将拉格朗日方程分解为梯度图像更新函数、迭代误差校正函数、PET图像重建函数、以及特征提取函数,以对梯度图像更新函数、迭代误差校正函数、PET图像重建函数、以及特征提取函数进行迭代求解,得到初始重建PET图像对应的目标重建PET图像,从而进一步提高了PET图像的重建速度,且提高重建得到的目标重建PET图像的图像质量。Further preferably, the Lagrangian equation is decomposed into a gradient image update function, an iterative error correction function, a PET image reconstruction function, and a feature extraction function using the Bregman iterative method, so that the gradient image update function, the iterative error correction function, and the PET image The reconstruction function and the feature extraction function are iteratively solved to obtain the target reconstructed PET image corresponding to the initial reconstructed PET image, thereby further improving the reconstruction speed of the PET image and improving the image quality of the reconstructed target reconstructed PET image.
优选地,对拉格朗日方程分解得到的特征提取函数为获得的特征矩阵D和特征向量αl用来作为下一轮迭代中对梯度图像ω进行更新的初始值,从而将初始重建PET图像从图像域转换到梯度域,对梯度域图像进行特征学习,以通过学习到的特征矩阵和特征向量稀疏表示初始重建PET图像,从而降低初始重建PET图像中的噪声,进而提高后续PET图像的重建效果。其中,k为当前迭代次数。Preferably, the feature extraction function obtained by decomposing the Lagrangian equation is The obtained feature matrix D and feature vector α l are used as the initial value for updating the gradient image ω in the next iteration, so as to convert the initial reconstructed PET image from the image domain to the gradient domain, and perform feature learning on the gradient domain image. The initial reconstructed PET image is sparsely represented by the learned feature matrix and feature vector, so as to reduce the noise in the initial reconstructed PET image, and then improve the reconstruction effect of the subsequent PET image. Among them, k is the current iteration number.
优选地,对拉格朗日方程分解得到的梯度图像更新函数为获得的梯度图像ω用来作为下一轮迭代中对目标重建PET图像m进行重建的初始值,从而降低后续重建的目标重建PET图像的伪影程度。其中,v2为预设的用来在Bregman迭代中控制迭代误差的权重,b为Bregman迭代的误差校正值。Preferably, the gradient image update function obtained by decomposing the Lagrangian equation is The obtained gradient image ω is used as the initial value for reconstructing the target reconstructed PET image m in the next iteration, thereby reducing the degree of artifacts of the target reconstructed PET image for subsequent reconstruction. Among them, v 2 is the preset weight used to control the iteration error in the Bregman iteration, and b is the error correction value of the Bregman iteration.
在本发明实施例中,进一步优选地,将v2设置为1,从而进一步降低后续重建的目标重建PET图像的伪影程度。In the embodiment of the present invention, further preferably, v 2 is set to 1, so as to further reduce the degree of artifacts of the subsequent reconstructed target reconstructed PET image.
优选地,对拉格朗日方程分解得到的迭代误差校正函数获得的误差校正值b用来作为下一轮迭代中对目标重建PET图像m进行重建的初始值,从而提高重建得到的PET图像的图像质量。Preferably, the iterative error correction function obtained by decomposing the Lagrangian equation The obtained error correction value b is used as an initial value for reconstructing the target reconstructed PET image m in the next iteration, so as to improve the image quality of the reconstructed PET image.
优选地,对拉格朗日方程分解得到的PET图像重建函数ωk为第k次迭代的梯度图像,bk为第k次迭代的误差校正值,从而提高重建得到的PET图像的图像质量。Preferably, the PET image reconstruction function obtained by decomposing the Lagrangian equation ω k is the gradient image of the k-th iteration, and b k is the error correction value of the k-th iteration, so as to improve the image quality of the reconstructed PET image.
如图2所示,优选地,通过下述步骤实现采用Bregman迭代方法对拉格朗日方程进行迭代求解:As shown in Figure 2, preferably, the Bregman iterative method is used to iteratively solve the Lagrangian equation through the following steps:
在步骤S201中,根据预设的初始特征矩阵和预设的初始特征向量,使用梯度图像更新函数对初始重建PET图像对应的梯度图像进行更新。In step S201, according to the preset initial feature matrix and the preset initial feature vector, a gradient image update function is used to update the gradient image corresponding to the initially reconstructed PET image.
在步骤S202中,根据更新后的梯度图像和迭代误差校正函数得到的误差校正值,使用PET图像重建函数将梯度图像从梯度域中恢复到图像域,得到目标重建PET图像。In step S202, according to the updated gradient image and the error correction value obtained by the iterative error correction function, the PET image reconstruction function is used to restore the gradient image from the gradient domain to the image domain to obtain the target reconstructed PET image.
在步骤S203中,判断当前迭代次数是否达到预设的迭代阈值。In step S203, it is judged whether the current number of iterations reaches a preset iteration threshold.
在本发明实施例中,当当前迭代次数达到预设的迭代阈值(例如,50次)时,执行步骤S204,否则,跳转至步骤S205。In the embodiment of the present invention, when the current number of iterations reaches a preset iteration threshold (for example, 50 times), step S204 is executed; otherwise, step S205 is skipped.
在步骤S204中,输出目标重建PET图像。In step S204, the reconstructed PET image of the target is output.
在步骤S205中,将目标重建PET图像设置为初始重建PET图像,并根据预设的图像块提取矩阵,从初始重建PET图像对应的梯度图像中提取对应数量的图像块,梯度图像包括水平梯度图像和垂直梯度图像。In step S205, set the target reconstructed PET image as the initial reconstructed PET image, and extract a corresponding number of image blocks from the gradient image corresponding to the initial reconstructed PET image according to the preset image block extraction matrix, the gradient image includes the horizontal gradient image and a vertical gradient image.
在本发明实施例中,首先将初始重建PET图像从图像域转换到梯度域,得到水平梯度图像和垂直梯度图像,根据预设的图像块提取矩阵,分别从水平梯度图像和垂直梯度图像中提取对应数量的水平图像块和垂直图像块。In the embodiment of the present invention, firstly, the initial reconstructed PET image is converted from the image domain to the gradient domain to obtain the horizontal gradient image and the vertical gradient image. A corresponding number of horizontal image blocks and vertical image blocks.
在步骤S206中,对图像块进行特征学习,直至学习得到的梯度图像对应的特征矩阵和图像块对应的特征向量满足特征提取函数。In step S206, feature learning is performed on the image block until the feature matrix corresponding to the learned gradient image and the feature vector corresponding to the image block satisfy the feature extraction function.
在本发明实施例中,分别对提取到水平图像块和垂直图像块进行特征学习,直至学习得到的水平/垂直梯度图像对应的水平/垂直特征矩阵和水平/垂直图像块对应的水平/垂直特征向量满足特征提取函数,其中,特征矩阵的每一列与每个图像块对应的特征向量一一对应。In the embodiment of the present invention, feature learning is performed on the extracted horizontal image block and vertical image block, until the horizontal/vertical feature matrix corresponding to the learned horizontal/vertical gradient image and the horizontal/vertical feature matrix corresponding to the horizontal/vertical image block The vector satisfies the feature extraction function, where each column of the feature matrix is in one-to-one correspondence with the feature vector corresponding to each image block.
在步骤S207中,将特征矩阵和特征向量分别设置为初始特征矩阵和初始特征向量,将当前迭代次数增加1次,并跳转至步骤S201,继续下一轮迭代,以重建PET图像。In step S207, set the eigenmatrix and eigenvector as the initial eigenmatrix and initial eigenvector respectively, increase the current iteration number by 1, and jump to step S201 to continue the next iteration to reconstruct the PET image.
通过上述步骤S201-步骤S207实现对拉格朗日方程进行迭代求解,以得到初始重建PET图像对应的目标重建PET图像,从而降低重建图像的伪影程度,提高了低剂量PET图像重建的图像质量。Through the above step S201-step S207, the Lagrangian equation is iteratively solved to obtain the target reconstructed PET image corresponding to the initial reconstructed PET image, thereby reducing the artifact degree of the reconstructed image and improving the image quality of low-dose PET image reconstruction .
在本发明实施例中,根据PET设备采集到的投影数据以及该PET设备的系统矩阵,通过PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像,根据初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到初始重建PET图像对应的目标重建PET图像,从而提高了低剂量PET图像的重建速度,且降低重建图像的伪影程度,进而提高了低剂量PET图像重建的图像质量。In the embodiment of the present invention, according to the projection data collected by the PET device and the system matrix of the PET device, the pre-initialized PET image to be reconstructed is reconstructed through the PET image reconstruction algorithm to obtain the initial reconstructed PET image, and according to the initial reconstructed PET image image, using Lagrangian multiplication to jointly optimize and solve the pre-built image reconstruction equation and the pre-built gradient domain image feature selection equation, and obtain the target reconstructed PET image corresponding to the initial reconstructed PET image, thereby improving the accuracy of low-dose PET images. The reconstruction speed is improved, and the artifact degree of the reconstructed image is reduced, thereby improving the image quality of low-dose PET image reconstruction.
实施例二:Embodiment two:
图3示出了本发明实施例二提供的基于梯度域的低剂量PET图像重建装置的结构,为了便于说明,仅示出了与本发明实施例相关的部分,其中包括:Fig. 3 shows the structure of the gradient domain-based low-dose PET image reconstruction device provided by Embodiment 2 of the present invention. For the convenience of illustration, only the parts related to the embodiment of the present invention are shown, including:
参数获取单元31,用于当接收到低剂量PET图像的重建请求时,获取通过PET设备采集到的投影数据,并获取PET设备的系统矩阵;The
初始重建单元32,用于根据投影数据以及系统矩阵,通过预设的PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像;以及The
重建图像获得单元33,用于根据初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到初始重建PET图像对应的目标重建PET图像。The reconstructed
如图4所示,优选地,重建图像获得单元33包括:As shown in Figure 4, preferably, the reconstructed
迭代求解单元331,用于采用Bregman迭代方法对由图像重建方程和梯度域图像特征选取方程联立的拉格朗日方程进行迭代求解。The
进一步优选地,迭代求解单元331包括:Further preferably, the
方程分解单元3311,用于采用Bregman迭代方法将拉格朗日方程分解为梯度图像更新函数、迭代误差校正函数、PET图像重建函数、以及特征提取函数,以对梯度图像更新函数、迭代误差校正函数、PET图像重建函数、以及特征提取函数进行迭代求解,得到初始重建PET图像对应的目标重建PET图像。The
进一步优选地,如图5所示,方程分解单元3311包括:Further preferably, as shown in Figure 5, the
梯度图像更新单元51,用于根据预设的初始特征矩阵和预设的初始特征向量,使用梯度图像更新函数对初始重建PET图像对应的梯度图像进行更新;The gradient
PET图像重建单元52,用于根据更新后的梯度图像和迭代误差校正函数得到的误差校正值,使用PET图像重建函数将梯度图像从梯度域中恢复到图像域,得到目标重建PET图像;The PET
迭代次数判断单元53,用于判断当前迭代次数是否达到预设的迭代阈值;The number of
PET图像输出单元54,用于是则,输出目标重建PET图像;The PET
图像块提取单元55,用于否则,将目标重建PET图像设置为初始重建PET图像,并根据预设的图像块提取矩阵,从初始重建PET图像对应的梯度图像中提取对应数量的图像块,梯度图像包括水平梯度图像和垂直梯度图像;The image
特征学习单元56,用于对图像块进行特征学习,直至学习得到的梯度图像对应的特征矩阵和图像块对应的特征向量满足特征提取函数;以及The
参数设置单元57,用于将特征矩阵和特征向量分别设置为初始特征矩阵和初始特征向量,并触发梯度图像更新单元51,继续下一轮迭代,以重建PET图像。The
在本发明实施例中,基于梯度域的低剂量PET图像重建装置的各单元可由相应的硬件或软件单元实现,各单元可以为独立的软、硬件单元,也可以集成为一个软、硬件单元,在此不用以限制本发明。具体地,各单元的实施方式可参考前述实施例一的描述,在此不再赘述。In the embodiment of the present invention, each unit of the low-dose PET image reconstruction device based on the gradient domain can be realized by corresponding hardware or software units, each unit can be an independent software and hardware unit, or can be integrated into a software and hardware unit, It is not intended to limit the present invention. Specifically, for the implementation manner of each unit, reference may be made to the description of the first embodiment above, and details are not repeated here.
实施例三:Embodiment three:
图6示出了本发明实施例三提供的计算设备的结构,为了便于说明,仅示出了与本发明实施例相关的部分。FIG. 6 shows the structure of a computing device provided by Embodiment 3 of the present invention. For ease of description, only parts related to the embodiment of the present invention are shown.
本发明实施例的计算设备6包括处理器60、存储器61以及存储在存储器61中并可在处理器60上运行的计算机程序62。该处理器60执行计算机程序62时实现上述基于梯度域的低剂量PET图像重建方法实施例中的步骤,例如图1所示的步骤S101至S103。或者,处理器60执行计算机程序62时实现上述各装置实施例中各单元的功能,例如图3所示单元31至33的功能。The
在本发明实施例中,根据PET设备采集到的投影数据以及该PET设备的系统矩阵,通过PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像,根据初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到初始重建PET图像对应的目标重建PET图像,从而提高了低剂量PET图像的重建速度,且降低重建图像的伪影程度,进而提高了低剂量PET图像重建的图像质量。In the embodiment of the present invention, according to the projection data collected by the PET device and the system matrix of the PET device, the pre-initialized PET image to be reconstructed is reconstructed through the PET image reconstruction algorithm to obtain the initial reconstructed PET image, and according to the initial reconstructed PET image image, using Lagrangian multiplication to jointly optimize and solve the pre-built image reconstruction equation and the pre-built gradient domain image feature selection equation, and obtain the target reconstructed PET image corresponding to the initial reconstructed PET image, thereby improving the accuracy of low-dose PET images. The reconstruction speed is improved, and the artifact degree of the reconstructed image is reduced, thereby improving the image quality of low-dose PET image reconstruction.
本发明实施例的计算设备可以为个人计算机、服务器。该计算设备6中处理器60执行计算机程序62时实现基于梯度域的低剂量PET图像重建方法时实现的步骤可参考前述方法实施例的描述,在此不再赘述。The computing device in the embodiment of the present invention may be a personal computer or a server. For the steps to be implemented when the
实施例四:Embodiment four:
在本发明实施例中,提供了一种计算机可读存储介质,该计算机可读存储介质存储有计算机程序,该计算机程序被处理器执行时实现上述基于梯度域的低剂量PET图像重建方法实施例中的步骤,例如,图1所示的步骤S101至S103。或者,该计算机程序被处理器执行时实现上述各装置实施例中各单元的功能,例如图3所示单元31至33的功能。In an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned embodiment of the gradient-domain-based low-dose PET image reconstruction method is implemented The steps in, for example, steps S101 to S103 shown in FIG. 1 . Alternatively, when the computer program is executed by the processor, the functions of the units in the above-mentioned device embodiments are implemented, for example, the functions of the
在本发明实施例中,根据PET设备采集到的投影数据以及该PET设备的系统矩阵,通过PET图像重建算法对预先初始化的待重建PET图像进行图像重建,获得初始重建PET图像,根据初始重建PET图像,采用拉格朗日乘法将预先构建的图像重建方程和预先构建的梯度域图像特征选取方程进行联合优化求解,得到初始重建PET图像对应的目标重建PET图像,从而提高了低剂量PET图像的重建速度,且降低重建图像的伪影程度,进而提高了低剂量PET图像重建的图像质量。In the embodiment of the present invention, according to the projection data collected by the PET device and the system matrix of the PET device, the pre-initialized PET image to be reconstructed is reconstructed through the PET image reconstruction algorithm to obtain the initial reconstructed PET image, and according to the initial reconstructed PET image image, using Lagrangian multiplication to jointly optimize and solve the pre-built image reconstruction equation and the pre-built gradient domain image feature selection equation, and obtain the target reconstructed PET image corresponding to the initial reconstructed PET image, thereby improving the accuracy of low-dose PET images. The reconstruction speed is improved, and the artifact degree of the reconstructed image is reduced, thereby improving the image quality of low-dose PET image reconstruction.
本发明实施例的计算机可读存储介质可以包括能够携带计算机程序代码的任何实体或装置、记录介质,例如,ROM/RAM、磁盘、光盘、闪存等存储器。The computer-readable storage medium in the embodiments of the present invention may include any entity or device or recording medium capable of carrying computer program codes, such as ROM/RAM, magnetic disk, optical disk, flash memory and other memories.
以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. within range.
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