Title
标题
A denoising diffusion probabilistic model for metal artifact reduction in CT
CT金属伪影去除的去噪扩散概率模子
01
文献速递先容
CT图像中的金属伪影是在CT扫描视野内存在金属物体（如牙科添补物、骨科假体、支架、手术东西等）时出现的视觉失真。这些失真大概会影响临床查抄的诊断信心以及放射治疗操持的准确性。
金属伪影在CT图像中体现为从金属物体发出的明暗带和更细的条纹。这些伪影的重要缘故原由是光子饥饿，即由于X射线通过金属物体时的高衰减，导致只有不敷数量的光子到达探测器，从而产生严峻的噪声条纹。别的，当X射线被金属物体剧烈衰减时，束硬化和散射辐射的影响会加剧，导致金属物体之间出现暗带。部分容积效应和混叠也被以为是金属伪影形成的机制。
为进步CT扫描的图像质量，已经提出了几种金属伪影去除（MAR）方法。之前的研究重要会合在改正引起金属伪影的物理效应，包罗噪声、散射和束硬化。然而，当存在严峻伪影时，这些方法大概效果不佳。迭代重修MAR方法也被提出：其上风在于可以统计性地低沉或完全忽略受损丈量数据，而且可以连合非理想物理效应的前向模子，从而制止伪影的产生。别的，基于插值的技能被广泛用于MAR，这些技能将正弦图中受金属污染的部分视为缺失信息，并利用插值方案举行更换。
Abstract
择要
金属物体的存在会导致CT投影丈量数据粉碎，从而在重修的CT图像中产生金属伪影。人工智能（AI）有望提供更好的办理方案来估计缺失的正弦图数据，以淘汰金属伪影（MAR），如之前在卷积神经网络（CNNs）和天生对抗网络（GANs）中所示。近来，去噪扩散概率模子（DDPM）在图像天生任务中体现出极大的潜力，大概优于GANs。在本研究中，提出了一种基于DDPM的方法，用于弥补缺失的正弦图数据，以改进MAR。该模子在练习过程中不必要金属物体的信息，这大概增强其在差别范例金属植入物上的泛化本领，相较于依靠条件练习的方法。对所提出的技能举行了性能评估，并与开始进的归一化MAR（NMAR）方法以及基于CNN和GAN的MAR方法举行了比力。基于DDPM的方法提供了显着更高的布局相似性指数（SSIM）和峰值信噪比（PSNR），与NMAR（SSIM：p < 10-26；PSNR：p < 10-21），CNN（SSIM：p < 10-25；PSNR：p < 10-9）和GAN（SSIM：p < 10-6；PSNR：p < 0.05）方法相比。基于DDPM的MAR技能进一步基于临床干系图像质量指标对临床CT图像中假造引入的金属物体和金属伪影举行了评估，体现出相对于其他三种模子的精良质量。总体而言，基于AI的技能相较于非AI的NMAR方法体现出改进的MAR性能。所提出的方法在进步MAR效果方面体现出远景，从而进步了CT的诊断准确性。
Method
方法
A. Data generation
Training data are generated by performing highly realistic CT simulations of real patient images with and without metal objects as well as simulations of metal objects only. The metalonly sinograms are used to mask the data in the shadow of the metal objects (i.e., the metal trace) as an ideal input to the MAR methods, as illustrated in Figure 1. We previously validated that our recently developed realistic CT physics models in the CatSim CT simulator [23], [24] result in highly realistic CT simulations [25] as well as realistic metal artifacts. We here use a nominal CT geometry with source-to-iso-distance 540 mm, source-to-detector distance 950 mm, 1.0 mm x 1.1 mm detector cells, detector quarter offset, 120 kVp tube voltage, 200 mA tube current, 2 detector rows, 888 detector columns, 984 views, a large bowtie filter, and realistic quantum noise, electronic noise and beam hardening.
A. 数据天生
练习数据通过对真实患者图像举行高逼真度的CT模拟天生，这些图像包罗有金属物体和没有金属物体的情况，以及仅有金属物体的模拟。金属物体的正弦图用于在金属物体的阴影中掩藏数据（即金属陈迹），作为MAR方法的理想输入，如图1所示。我们之前已履历证，我们近来在CatSim CT模拟器中开发的逼真CT物理模子可以产生高度逼真的CT模拟，以及逼真的金属伪影。在此，我们利用了名义上的CT多少参数，源到等距隔断为540毫米，源到探测器隔断为950毫米，探测器单位为1.0毫米x 1.1毫米，探测器四分之一偏移，管电压为120 kVp，管电流为200 mA，2排探测器，888列探测器，984视图，大楔形滤波器，以及逼真的量子噪声、电子噪声和束硬化。
Conclusion
结论
In this study, a denoising diffusion probabilistic model (DDPM) was developed to estimate missing sinogram data for CT MAR. The model is unconditionally trained without using information on the metal corrupted data, which can potentially provideenhanced generalization capabilities across various types of metal objects. The DDPM-MAR was compared to a state-ofthe-art analytical method (NMAR) and two other AI methods (PUnet and GAN). The results were evaluated using standard computer vision metrics in a test dataset of 200 images. In addition, the methods were evaluated using a series of clinically relevant performance metrics in four clinical CT scans with realistic metal objects and artifacts. In general, all AI methods outperformed the analytical NMAR method. Furthermore, the DDPM approach outperformed the PUnet and GAN approaches. The DDPM model was capable of effectively reducing metal artifacts in all test cases and following all different performance metrics. The proposed methodology enhances the state of the art in MAR, showing promise in improving the diagnostic accuracy of CT scans.
在本研究中，开发了一种去噪扩散概率模子（DDPM）来估计CT金属伪影去除（MAR）中的缺失正弦图数据。该模子在练习过程中不利用金属污染数据的信息，从而大概在各种范例的金属物体中提供增强的泛化本领。DDPM-MAR与一种开始进的分析方法（NMAR）和其他两种AI方法（PUnet和GAN）举行了比力。利用200张图像的测试数据集，通过尺度盘算机视觉指标对效果举行了评估。别的，这些方法在四个带有逼真金属物体和伪影的临床CT扫描中，通过一系列临床干系的性能指标举行了评估。总体而言，全部AI方法都优于分析方法NMAR。别的，DDPM方法优于PUnet和GAN方法。DDPM模子可以或许在全部测试案例中以及差别的性能指标下有用地淘汰金属伪影。所提出的方法改进了MAR的开始进技能，在进步CT扫描的诊断准确性方面体现出远景。
Results
效果
A. Qualitative performance assessment
Figure 4 shows the performance of the different MAR techniques in an abdominal CT scan of the test dataset with a single simulated metal object. Figure 4-A illustrates an example of a CT image resulting from a sinogram without metal object. The respective simulated metal object, indicated by a yellow circle, is overlaid with the maximum grayscale value. Figure 4-B shows the respective metal corrupted image. A realistic metal simulation was carried out to visualize the metal artifacts. Figures 4-C-F illustrate the MAR-corrected images resulting from the NMAR, PUnet, GAN and DDPM correction methods, respectively. Figures 4-G-L demonstrate magnified versions of Figures 4-C-F, in a region around the virtual metal trace. The location of the magnified region corresponds to the greendashed rectangle, overlaid on Figure 4-A. The red, orange and green colored arrows correspond to high, intermediate and lowdegree of metal artifact presence. Difference images illustratingthe intensity of residual metal artifacts after applying each MAR technique are provided in the supplemental Figure S1. It)can be observed that the AI methods generally outperform NMAR, while the DDPM outperforms PUnet and GAN, demonstrating relatively lower intensity of residual metal artifacts.
A. 定性性能评估
图4体现了在测试数据会合单个模拟金属物体的腹部CT扫描中，差别MAR技能的体现。图4-A展示了一个没有金属物体的正弦图天生的CT图像示例。相应的模拟金属物体（用黄色圆圈标示）被叠加在最大灰度值上。图4-B体现了相应的金属污染图像，举行了一次逼真的金属模拟以可视化金属伪影。图4-C至4-F分别展示了通过NMAR、PUnet、GAN和DDPM修正方法天生的MAR修正图像。图4-G至4-L展示了图4-C至4-F中假造金属陈迹附近地区的放大版本。放大地区的位置对应于图4-A中绿色虚线矩形。赤色、橙色和绿色箭头分别对应高、中、低水平的金属伪影。增补图S1中提供了应用每种MAR技能后残余金属伪影强度的差别图像。
可以观察到，AI方法总体上优于NMAR，而DDPM优于PUnet和GAN，体现出相对较低的残余金属伪影强度。
Figure
图
Figure 1: Methodology for dataset generation and sinogram corruption using traces of generated metal objects.
图1：数据集天生方法和利用天生的金属物体陈迹举行正弦图粉碎的方法。
Figure 2: Training process of a denoising diffusion probabilistic model. A ground truth sinogram, SGT,0, is subjected to noising at multiple noising steps, t, through the forward diffusion process, q(), until it is converted to white noise, SGT,T. The reverse diffusion process, pθ, starts from SGT,Τ, and removes noise at multiple time steps, until it is restores the original sinogram. The reverse diffusion is modelled by a U-net which predicts the noise distribution at each step.
图2：去噪扩散概率模子的练习过程。一个真实的正弦图 SGT,0S{\text{GT},0}SGT,0 在多个加噪步调 ttt 中通过前向扩散过程 q()q()q() 徐徐被加噪，直到其酿成白噪声 SGT,TS{\text{GT},T}SGT,T。反向扩散过程 pθp{\theta}pθ 从 SGT,TS{\text{GT},T}SGT,T 开始，在多个时间步长中徐徐去噪，直到规复原始正弦图。反向扩散过程通过一个U-net模子来模拟，该模子在每一步猜测噪声分布。
Figure 3: Inference scheme for sinogram inpainting based on a trained DDPM. At each diffusion step, t, a generated sinogram, SG,t is formed. The uncorrupted pixels of, SG,t, are sampled directly from the input, by adding noise through the forward diffusion process. The pixels corresponding to corrupted regions, are sampled by running the reverse diffusion process.
图3：基于已练习的DDPM的正弦图修复推理方案。在每个扩散步调 ttt，天生的正弦图 SG,tS{G,t}SG,t 被形成。SG,tS{G,t}SG,t 的未粉碎像素直接从输入中采样，通过前向扩散过程添加噪声。对应于粉碎地区的像素通过运行反向扩散过程来采样。
Figure 4: Performance of the different MAR techniques in an abdominal CT scan in the test dataset, with a single simulated metal object. A) CT image resulting from a ground truth sinogram. The respective simulated metal object is overlaid (inside yellow circle). B) Metal corrupted image. C-F) MAR corrected images resulting from the NMAR, PUnet, GAN and DDPM correction methods, respectively. G-L) Magnified regions of the ground truth, uncorrected, NMAR, PUnet, GAN and DDPM corrected images. The location of the magnified regions corresponds to the green rectangle, overlaid on Figure 4-A, with the virtual metal trace in the field of view. The red, orange and green colored arrows correspond to high, intermediate and low degree of metal artifact presence. All images are displayed with a window level of 0 and width of 400 HU
图4：在测试数据会合单个模拟金属物体的腹部CT扫描中，差别MAR技能的体现。A) 由真实正弦图天生的CT图像。相应的模拟金属物体被叠加（黄色圆圈内）。B) 金属污染图像。C-F) 分别由NMAR、PUnet、GAN和DDPM修正方法天生的MAR修正图像。G-L) 真实图像、未修正图像、NMAR、PUnet、GAN和DDPM修正图像的放大地区。放大地区的位置对应于图4-A上叠加的绿色矩形，视野内有假造金属陈迹。赤色、橙色和绿色箭头分别对应高、中、低水平的金属伪影。全部图像均以窗口水平0和宽度400 HU体现。
Figure 5: Performance of MAR techniques in a clinical CT of the pelvis with two virtually placed gold marker implants in the prostate (Patient 1). The blue and orange rings overlaid in section A) denote the ROIs in which the CTN performance metric was evaluated. For arrow description see Figure 4.
图5：在患者1的临床骨盆CT中，带有两个假造放置在火线腺中的金属标志植入物的MAR技能性能。A部分叠加的蓝色和橙色环体现评估CTN性能指标的感爱好地区（ROI）。箭头阐明见图4。
Figure 6: Performance of MAR techniques in a clinical CT of the thorax with virtually placed implants (Patient 2)
图6：在患者2的临床胸部CT中，带有假造放置的植入物的MAR技能性能。
Figure 7: Performance of MAR techniques in a clinical CT of the head with virtually placed dental implants (Patient 3).
图7：在患者3的临床头部CT中，带有假造放置的牙科植入物的MAR技能性能。
Figure 8: Performance of MAR techniques in a clinical CT of the hip with a virtual total hip replacement (Patient 4).
图8：在患者4的临床髋部CT中，带有假造全髋关节置换的MAR技能性能。
Figure 9: A) Clinical CT of the pelvis with two virtually placed gold marker implants in the prostate. B-E) Metal segmentation masks with ratios of segmented area to actual metal trace area equal to 1.4, 1.8, 0.7 and 0.5, respectively. The segmentation masks are overlaid in green color on top of a binary mask depicting the ground truth metal trace geometry.
图9：A) 临床骨盆CT，此中火线腺中有两个假造放置的金属标志植入物。B-E) 金属分割掩膜，其分割地区与现实金属陈迹地区的比值分别为1.4、1.8、0.7和0.5。分割掩膜以绿色叠加在体现真实金属陈迹多少外形的二值掩膜上。
Table
表
TABLE I – Mean and standard deviation of performance metrics
表 I – 性能指标的均值和尺度差
TABLE II – Summary of clinically relevant performance metrics in the clinical dataset with realistic metal object simulation. The best performing results for each metric are bolded. The worst performing result for each metric are shaded in gray.
表 II – 在临床数据会合利用逼真金属物体模拟的临床干系性能指标总结。每个指标中体现最好的效果用粗体体现。每个指标中体现最差的效果用灰色阴影体现。
Table III – MAR performance metrics for different metal mask segmentations
表 III – 差别金属掩膜分割的MAR性能指标
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发表于 2026-4-24 08:49:30