CN108230277B - Dual-energy CT image decomposition method based on convolutional neural network - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及医学双能图像分解和图像处理技术领域,尤其涉及双能CT图像分解方法,特别涉及一种基于卷积神经网络的双能CT图像分解方法。The invention relates to the technical field of medical dual-energy image decomposition and image processing, in particular to a dual-energy CT image decomposition method, in particular to a dual-energy CT image decomposition method based on a convolutional neural network.
背景技术Background technique
双能CT图像重建已经越来越多地应用于医学成像、安全检查、无损检测等领域,相比于传统的单能谱CT成像技术,双能CT能够利用不同能谱下图像衰减信息实现对不同物质材料的识别。双能CT技术打破了传统单能谱CT的物理局限,成为CT成像领域研究的热点和难点问题。Dual-energy CT image reconstruction has been increasingly used in medical imaging, security inspection, non-destructive testing and other fields. Compared with the traditional single-energy spectrum CT imaging technology, dual-energy CT can use image attenuation information under different energy spectra to realize Identification of different physical materials. Dual-energy CT technology breaks the physical limitations of traditional single-energy spectral CT, and has become a hot and difficult issue in the field of CT imaging.
双能CT成像技术的核心理论是双能CT图像重建算法,其中材料和能量交叉信息的解耦合是其中的一个关键问题。与传统CT图像重建理论相比,双能CT图像重建算法的困难在于问题的非线性、多解性与不适定性、高维数等特点。由于双能CT成像应用需求不同,所采用的成像模式不同,以及采集得到的数据信息不同,因此相应的双能CT图像重建算法也各不相同。目前,双能CT图像重建算法主要可以分为三类:迭代类直接重建算法、基于投影域预处理的图像重建算法和基于图像域后处理的图像重建算法。The core theory of dual-energy CT imaging technology is dual-energy CT image reconstruction algorithm, in which the decoupling of material and energy cross information is one of the key issues. Compared with the traditional CT image reconstruction theory, the difficulty of the dual-energy CT image reconstruction algorithm lies in the nonlinearity, multi-solution and ill-posedness of the problem, and high dimensionality. Due to the different application requirements of dual-energy CT imaging, different imaging modes used, and different acquired data information, the corresponding dual-energy CT image reconstruction algorithms are also different. At present, dual-energy CT image reconstruction algorithms can be mainly divided into three categories: iterative direct reconstruction algorithms, image reconstruction algorithms based on projection domain preprocessing, and image reconstruction algorithms based on image domain postprocessing.
迭代双能CT图像重建算法对于几何上一致或不一致的高、低能投影集都适合,重建图像信噪比较高。但是这些方法通常需要巨大的计算开销,计算速度慢,严重降低了算法的实用性。基于投影域预处理的图像重建算法充分利用高、低能谱信息和多色投影生成模型,将非线性求解问题转化为线性求解问题,从而使用常规CT图像重建算法实现双能CT图像重建,能够从理论上有效消除硬化伪影的影响,得到准确的物理参数分布信息,计算简便,效率高,是目前双能CT图像重建技术的主流重建方法。但是这类方法高度依赖于校准过程,而且要求高、低能投影数据集在空间几何上是一致的,即每一对高、低能投影测量值都需要沿着相同的射线路径。The iterative dual-energy CT image reconstruction algorithm is suitable for both high and low energy projection sets that are geometrically consistent or inconsistent, and the reconstructed image has a high signal-to-noise ratio. But these methods usually require huge computational overhead and slow computational speed, which seriously reduces the practicability of the algorithm. The image reconstruction algorithm based on projection domain preprocessing makes full use of the high and low energy spectral information and the multi-color projection generation model to convert the nonlinear solution problem into a linear solution problem, so that the conventional CT image reconstruction algorithm can be used to achieve dual-energy CT image reconstruction. In theory, the influence of hardening artifacts can be effectively eliminated, and accurate physical parameter distribution information can be obtained. The calculation is simple and the efficiency is high. It is the mainstream reconstruction method of dual-energy CT image reconstruction technology at present. However, such methods are highly dependent on the calibration process and require the high and low energy projection datasets to be spatially geometrically consistent, i.e. each pair of high and low energy projection measurements needs to follow the same ray path.
基于图像域后处理的图像重建算法首先对高、低能投影数据分别利用传统CT图像重建算法重建出高、低能CT图像,对高、低能CT重建图像在图像域进行材料分解,获取物体断层的物理参数分布图像。实现高低能图像下的不同材料的分解是基于图像后处理的图像重建算法的关键。基于图像域后处理的双能CT图像重建算法对于高、低能投影数据的空间几何一致性要求不高,计算简便,可在一定程度上抑制硬化伪影。并且,该方法能够直接应用于现有成像系统中,不需要额外增加硬件设备,节省了成本。因此,该方法被广泛应用于现有双能CT成像系统中。基于此,本专利设计了一种基于卷积神经网络的双能CT图像分解方法。The image reconstruction algorithm based on image domain post-processing first reconstructs high- and low-energy CT images from high- and low-energy projection data using traditional CT image reconstruction algorithms, and then decomposes high- and low-energy CT reconstructed images in the image domain to obtain the physical properties of the object tomography. Parametric distribution image. Realizing the decomposition of different materials under high and low energy images is the key to image reconstruction algorithms based on image post-processing. The dual-energy CT image reconstruction algorithm based on image domain post-processing does not require high spatial geometric consistency of high-energy and low-energy projection data, and the calculation is simple, which can suppress hardening artifacts to a certain extent. Moreover, the method can be directly applied to the existing imaging system without additional hardware equipment, thus saving cost. Therefore, this method is widely used in existing dual-energy CT imaging systems. Based on this, this patent designs a dual-energy CT image decomposition method based on convolutional neural network.
发明内容SUMMARY OF THE INVENTION
针对现有双能CT图像分解方法得到的基材料图像含有大量噪声、信噪比较低的问题,本发明提供了一种基于卷积神经网络的双能CT图像分解方法,通过双输入、双输出的卷积神经网络模型和交叉卷积的建立,实现高能CT图像、低能CT图像中不同基材料的合理分流,从而有效提升双能CT图像基材料分解的质量。Aiming at the problem that the base material image obtained by the existing dual-energy CT image decomposition method contains a lot of noise and has a low signal-to-noise ratio, the present invention provides a dual-energy CT image decomposition method based on a convolutional neural network. The output of the convolutional neural network model and the establishment of cross-convolution can realize the reasonable shunting of different base materials in high-energy CT images and low-energy CT images, thereby effectively improving the quality of dual-energy CT image base material decomposition.
为了实现上述目的,本发明采用以下技术方案:In order to achieve the above object, the present invention adopts the following technical solutions:
一种基于卷积神经网络的双能CT图像分解方法,包括以下步骤:A dual-energy CT image decomposition method based on convolutional neural network, comprising the following steps:
步骤1:设计卷积神经网络模型作为双能分解模型中的映射函数D(μH,L;Θ);Step 1: design the convolutional neural network model as the mapping function D (μ H, L ; Θ) in the dual-energy decomposition model;
步骤2:通过卷积神经网络模型和训练数据集对卷积神经网络进行训练,对卷积神经网络参数Θ进行有效估计;Step 2: Train the convolutional neural network through the convolutional neural network model and the training data set, and effectively estimate the parameter Θ of the convolutional neural network;
步骤3:利用训练后的卷积神经网络和步骤2得到的卷积神经网络参数Θ对双能CT图像进行基材料高效分解。Step 3: Use the trained convolutional neural network and the convolutional neural network parameters Θ obtained in
优选地,所述卷积神经网络模型设计为双输入、双输出的网络结构模型以实现高能CT图像、低能CT图像的直接输入和不同材料图像的直接输出。Preferably, the convolutional neural network model is designed as a dual-input and dual-output network structure model to realize the direct input of high-energy CT images and low-energy CT images and the direct output of images of different materials.
优选地,所述双输入、双输出的网络结构模型建立了交叉卷积以实现高能CT图像、低能CT图像中不同基材料信息的合理分流。Preferably, the dual-input, dual-output network structure model establishes a cross convolution to achieve reasonable shunting of information of different base materials in high-energy CT images and low-energy CT images.
优选地,所述卷积神经网络模型中建立了短链接。Preferably, short links are established in the convolutional neural network model.
优选地,所述训练数据集包括卷积神经网络模型的输入数据和输出数据,所述输出数据为基材料图像,所述输入数据为根据基材料图像和对应能量信息得到的双能CT图像,所述双能CT图像包括高能CT图像和低能CT图像;将输出数据作为标签数据。Preferably, the training data set includes input data and output data of a convolutional neural network model, the output data is a base material image, and the input data is a dual-energy CT image obtained according to the base material image and corresponding energy information, The dual-energy CT images include high-energy CT images and low-energy CT images; the output data is used as label data.
与现有技术相比,本发明具有的有益效果:Compared with the prior art, the present invention has the following beneficial effects:
1、本发明基于双输入、双输出的卷积神经网络模型对双能CT图像进行分解,可有效避免不同能量、不同材料信息在输入、输出端的信息串扰。1. The present invention decomposes the dual-energy CT image based on the dual-input and dual-output convolutional neural network model, which can effectively avoid the information crosstalk between the input and output ends of different energies and different material information.
2、本发明基于交叉卷积的卷积神经网络模型能使高能CT图像、低能CT图像中的不同材料信息合理分流,沿交叉网络结构到达不同输出端。2. The cross-convolution-based convolutional neural network model of the present invention can reasonably divide the information of different materials in high-energy CT images and low-energy CT images to reach different output ends along the cross network structure.
3、本发明基于网络残差设计思想,在卷积神经网络模型中建立短链接能有效提升卷积神经网络的训练效率,利于后续更加深层次网络的设计。3. The present invention is based on the network residual design idea, and establishing short links in the convolutional neural network model can effectively improve the training efficiency of the convolutional neural network, which is beneficial to the subsequent design of deeper networks.
附图说明Description of drawings
图1为本发明一种基于卷积神经网络的双能CT图像分解方法的基本流程示意图。FIG. 1 is a schematic diagram of the basic flow of a method for decomposing a dual-energy CT image based on a convolutional neural network according to the present invention.
图2是本发明一种基于卷积神经网络的双能CT图像分解方法的双输入、双输出网络结构模型。2 is a dual-input and dual-output network structure model of a dual-energy CT image decomposition method based on a convolutional neural network of the present invention.
图3是本发明一种基于卷积神经网络的双能CT图像分解方法的双输入、双输出交叉网络结构模型。3 is a dual-input and dual-output cross-network structure model of a dual-energy CT image decomposition method based on a convolutional neural network of the present invention.
图4是本发明一种基于卷积神经网络的双能CT图像分解方法的双能CT图像卷积神经网络模型;4 is a dual-energy CT image convolutional neural network model of a dual-energy CT image decomposition method based on a convolutional neural network of the present invention;
图5是本发明一种基于卷积神经网络的双能CT图像分解方法的由骨骼和组织材料填充的数字仿真测试体模。FIG. 5 is a digital simulation test phantom filled with bone and tissue material according to a dual-energy CT image decomposition method based on a convolutional neural network of the present invention.
图6是本发明一种基于卷积神经网络的双能CT图像分解方法的SpekCalc软件仿真的X射线高、低能能谱信息图。FIG. 6 is an X-ray high- and low-energy spectrum information diagram of a SpekCalc software simulation of a dual-energy CT image decomposition method based on a convolutional neural network of the present invention.
图7是本发明一种基于卷积神经网络的双能CT图像分解方法的利用图5测试体模和图6能谱信息生成的高能CT图像。FIG. 7 is a high-energy CT image generated by using the test phantom of FIG. 5 and the energy spectrum information of FIG. 6 according to a method for decomposing a dual-energy CT image based on a convolutional neural network of the present invention.
图8是本发明一种基于卷积神经网络的双能CT图像分解方法的利用图5测试体模和图6能谱信息生成的低能CT图像。FIG. 8 is a low-energy CT image generated by using the test phantom of FIG. 5 and the energy spectrum information of FIG. 6 according to a method for decomposing a dual-energy CT image based on a convolutional neural network of the present invention.
图9是本发明一种基于卷积神经网络的双能CT图像分解方法使用仿真数据得到的骨骼图像。FIG. 9 is a bone image obtained by using simulation data in a dual-energy CT image decomposition method based on a convolutional neural network of the present invention.
图10是本发明一种基于卷积神经网络的双能CT图像分解方法通过仿真数据得到的组织图像。FIG. 10 is a tissue image obtained by a method of decomposing a dual-energy CT image based on a convolutional neural network of the present invention through simulation data.
图11是本发明一种基于卷积神经网络的双能CT图像分解方法的实际高能CT图像。FIG. 11 is an actual high-energy CT image of a dual-energy CT image decomposition method based on a convolutional neural network of the present invention.
图12是本发明一种基于卷积神经网络的双能CT图像分解方法的实际低能CT图像。FIG. 12 is an actual low-energy CT image of a dual-energy CT image decomposition method based on a convolutional neural network of the present invention.
图13是本发明一种基于卷积神经网络的双能CT图像分解方法通过实际数据得到的骨骼图像。FIG. 13 is a skeleton image obtained by a method of decomposing a dual-energy CT image based on a convolutional neural network of the present invention through actual data.
图14是本发明一种基于卷积神经网络的双能CT图像分解方法通过实际数据得到的组织图像。FIG. 14 is a tissue image obtained by a method of decomposing a dual-energy CT image based on a convolutional neural network of the present invention through actual data.
具体实施方式Detailed ways
为了便于理解,对本发明的具体实施方式中出现的部分名词作以下解释说明:For ease of understanding, the following explanations are made to some terms that appear in the specific embodiments of the present invention:
BP算法:误差反向传播算法。BP algorithm: Error back propagation algorithm.
下面结合附图和具体的实施例对本发明做进一步的解释说明:The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments:
实施例一:Example 1:
如图1所示,本发明的一种基于卷积神经网络的双能CT图像分解方法,包括以下步骤:As shown in Figure 1, a method for decomposing a dual-energy CT image based on a convolutional neural network of the present invention includes the following steps:
步骤S101:设计双输入、双输出的卷积神经网络模型作为双能分解模型中的映射函数D(μH,L;Θ),其中μH,L为双能CT图像,所述卷积神经网络模型设计为双输入、双输出的网络结构模型,如图2所示,图中A、B为输入的高能CT图像、低能CT图像,M1、M2为输出的基材料1、基材料2,“→”代表两个不同网络结构;且在双输入、双输出的网络结构模型中建立了交叉卷积,如图3所示;所述卷积神经网络模型中建立了短链接。具体卷积神经网络模型如图4所示:其中,卷积神经网络模型包含十二个卷积层,除最后输出卷积(共两个,对应两个标签数据)外,每个卷积后跟随一个批量化(Batch Normalization,BN)和线性修正单元(rectified linear unit,ReLU),图4中每个方块表示卷积、BN和ReLU的组合,Data_A、Data_B、Label_A、Label_B依次代表输入低能CT图像、输入高能CT图像、输出基材料1图像、输出基材料2图像。整个卷积神经网络模型共分三个层次,其中第一层(Stage 1)为特征提取层,第二层(Stage 2)为分流层,第三层(Stage 3)为输出层。特征提取层由1×7×7×64的卷积组成,输出64维的特征图像;分流层由4个交叉卷积和2个残差块组成,卷积维度均为64×3×3×64;输出层由维度为64×5×5×1的卷积组成,将64维特征组合为输出图像。Step S101: design the convolutional neural network model of dual input and dual output as the mapping function D (μ H, L ; Θ) in the dual energy decomposition model, wherein μ H, L are dual energy CT images, and the convolutional neural network The network model is designed as a dual-input and dual-output network structure model, as shown in Figure 2. In the figure, A and B are the input high-energy CT images and low-energy CT images, and M1 and M2 are the
步骤S102:通过卷积神经网络模型和训练数据集对卷积神经网络进行训练,对卷积神经网络参数Θ进行有效估计;作为一种可实施方式,通过BP算法结合训练数据集和设计的卷积神经网络模型对卷积神经网络进行训练,从而对卷积神经网络参数Θ进行有效估计,得到训练后的卷积神经网络,且在监督训练过程中,初始学习率、步长、权重值分别设置为10e-6、0.95、0.0005。所述训练数据集包括卷积神经网络模型的输入数据和输出数据,所述输出数据为基材料图像,所述输入数据为根据分解基材料图像和对应能量信息得到的双能CT图像,所述双能CT图像包括高能CT图像和低能CT图像;将输出数据作为标签数据。作为一种可实施方式,利用双能CT设备得到人体高、低能CT图像,根据医生临床经验,对人体高、低能CT图像进行分割,获得不同能量下的骨骼图像(基材料1)和组织图像(基材料2),将其作为输出数据,即标签数据,利用SpekCalc软件获得X射线高、低能能谱信息,根据高、低能能谱信息生成投影并重建得到高、低能CT图像,以获得的高、低能CT图像作为输入数据,以此建立训练数据集。作为一种可实施方式,共获得1300对512×512像素的骨骼图像和组织图像,并生成了1300对512×512像素的高、低能CT图像。将1300对512×512像素的骨骼图像和组织图像进行分割,步幅为48,得到105300对大小为128×128像素的图像块作为卷积神经网络模型的输出数据,即标签数据;将1300对512×512像素的高、低能CT图像进行分割,步幅为48,得到105300对大小为128×128像素的图像块作为卷积神经网络模型的输入数据。Step S102: train the convolutional neural network through the convolutional neural network model and the training data set, and effectively estimate the convolutional neural network parameter Θ; The convolutional neural network is trained by the convolutional neural network model, so that the parameters Θ of the convolutional neural network can be effectively estimated, and the trained convolutional neural network is obtained. In the supervised training process, the initial learning rate, step size and weight value are Set to 10e-6, 0.95, 0.0005. The training data set includes input data and output data of the convolutional neural network model, the output data is a base material image, the input data is a dual-energy CT image obtained by decomposing the base material image and corresponding energy information, and the Dual-energy CT images include high-energy CT images and low-energy CT images; the output data is used as label data. As an embodiment, dual-energy CT equipment is used to obtain high-energy and low-energy CT images of the human body, and according to the clinical experience of doctors, the high- and low-energy CT images of the human body are segmented to obtain bone images (base material 1) and tissue images under different energies. (Base material 2), take it as output data, namely label data, use SpekCalc software to obtain X-ray high and low energy spectrum information, generate projections and reconstruct high and low energy CT images according to the high and low energy spectrum information, and obtain high and low energy CT images. High and low energy CT images are used as input data to build a training dataset. As an embodiment, a total of 1300 pairs of 512×512 pixel bone images and tissue images were obtained, and 1300 pairs of 512×512 pixel high-energy and low-energy CT images were generated. Segment 1300 pairs of 512 × 512 pixel bone images and tissue images with a stride of 48 to obtain 105300 pairs of image blocks with a size of 128 × 128 pixels as the output data of the convolutional neural network model, that is, label data; High and low-energy CT images of 512 × 512 pixels were segmented with a stride of 48, and 105,300 pairs of image patches of size 128 × 128 pixels were obtained as the input data of the convolutional neural network model.
步骤S103:利用训练后的卷积神经网络和步骤S102得到的卷积神经网络参数Θ对双能CT图像进行基材料高效分解。Step S103: Use the trained convolutional neural network and the convolutional neural network parameters Θ obtained in step S102 to efficiently decompose the base material of the dual-energy CT image.
作为一种可实施方式,利用图5、图6仿真生成图7、图8所示的高能CT图像和低能CT图像,利用训练后的卷积神经网络对图7、图8进行处理,得到的输出分解图像结果如图9、图10所示。As an embodiment, the high-energy CT images and low-energy CT images shown in FIGS. 7 and 8 are generated by simulation using FIG. 5 and FIG. 6 , and the trained convolutional neural network is used to process FIGS. 7 and 8 , and the obtained The output decomposition image results are shown in Figure 9 and Figure 10.
作为一种可实施方式,如图11、图12所示的高能CT图像和低能CT图像,利用本实施例方法得到的网络分解结果分别如图13、图14所示。As an embodiment, for the high-energy CT images and low-energy CT images shown in FIGS. 11 and 12 , the network decomposition results obtained by the method of this embodiment are shown in FIGS. 13 and 14 , respectively.
以上所示仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本发明的保护范围。The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the principles of the present invention, several improvements and modifications can be made. It should be regarded as the protection scope of the present invention.
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