Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data
Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data
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DOI：
10.1038/s41598-020-64733-7
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发表时间：
2020-05-06
影响因子：
4.6
通讯作者：
Zschech, Ehrenfried
中科院分区：
文献类型：
--
作者：
Topal, Emre;Loeffler, Markus;Zschech, Ehrenfried
随着X射线计算机层析成像(XCT)被进一步推向微米和纳米尺度，各种工具组件和对象运动的限制变得更加明显。对于高分辨率的XCT，将这些工具部件对准亚微米精度是必要的，但实际上是困难的。其目的是开发一种新的重建方法，该方法在数据采集过程中考虑不可避免的不对准和物体运动，以获得高质量的三维图像，并适用于从不完整数据集进行数据恢复。已经开发和应用了一个由复杂的校正模块支持的重建软件，该模块使用梯度下降和深度学习算法自主地估计和补偿伪影。对于运动估计，一种新的计算机视觉方法与深度卷积神经网络方法相结合，通过跟踪相邻投影中的特征来提供对对象运动的估计。该模型使用由球体、三角形和矩形等几个简单几何特征组成的模拟体模的正向投影来训练。由神经网络提取的特征映射被用于检测和分类由支持向量机完成的特征。对于缺失数据的恢复，使用了一种新型的深卷积神经网络来从不完整的投影集推断出高质量的重建数据。使用一定角度范围内的模拟几何形状的前投影和后投影来训练模型。该模型能够在有限的角度覆盖范围内学习角度相关性，并提出一组新的投影来抑制伪影。高质量的三维图像表明，可以有效地抑制由工具部件和物体的热机械不稳定导致的运动、旋转中心未对准和探测器位置不准确引起的伪影，而无需额外的计算工作。从不完整的投影集合中恢复数据导致直接校正的投影，而不是抑制最终重建图像中的伪影。所提出的方法已经得到验证，并以滚珠轴承为例进行了演示。将重建结果与先前的校正进行比较，并用商业上可用的重建软件作为基准。与XCT成像和数据分析的传统方法相比，所提出的生成高质量三维X射线图像的方法是完全自主的。本文提出的方法已经被证明适用于高分辨率的微-XCT和纳米-XCT，然而，该方法适用于所有长度尺度。
While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.