Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study
Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study
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DOI：
10.1371/journal.pone.0222406
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发表时间：
2019-09-13
期刊：
影响因子：
3.7
通讯作者：
Mizukami, Shinya
中科院分区：
文献类型：
--
作者：
Gomi, Tsutomu;Sakai, Rina;Mizukami, Shinya
本研究旨在开发一种去噪卷积神经网络金属伪影减少混合重建（DnCNN-MARHR）算法，用于通过使用投影数据减少关节成形术数字断层合成（DT）中的金属物体。对于金属伪影减少（MAR），我们实现了一个DnCNN-MARHR算法的基础上的训练网络（小批量随机梯度下降算法与动量），以估计残留参考（140 keV虚拟单色[VM]）和对象（70 kV与金属伪影）的图像。为此，我们使用投影数据并从目标图像中减去估计的残差图像，涉及混合和主观重建图像的使用（反投影和最大似然期望最大化[MLEM]）。将DnCNN-MARHR算法与双能量物质分解重建算法（DEMECHANISM）、VM、MLEM、已建立和常用的滤波反投影（FBP）以及一种结合MAR处理的同步代数重建技术-全变分（SART-TV）进行了比较。使用伪影指数（AI）和纹理分析比较MAR。使用假体体模评价平面外和焦点内图像的扩散函数（ASF）。DnCNN-MARHR算法的整体性能对于ASF是足够的，并且所得到的图像显示出更好的结果，而不受金属类型的影响（AI几乎等于DEMPERATURE的最佳值）。在ASF分析中，DnCNN-MARHR算法产生更好的MAR相比，采用通常的算法重建使用MAR处理。此外，DnCNN-MARHR和传统算法之间的差异（均方误差）的比较导致最小的VM。DnCNN-MARHR算法在纹理分析中的图像均匀性方面表现出最好的性能。所提出的算法是特别有用的减少伪影在纵向方向上，它是不受组织误分类。
The present study aimed to develop a denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm for decreasing metal objects in digital tomosynthesis (DT) for arthroplasty by using projection data. For metal artifact reduction (MAR), we implemented a DnCNN-MARHR algorithm based on a training network (mini-batch stochastic gradient descent algorithm with momentum) to estimate the residual reference (140 keV virtual monochromatic [VM]) and object (70 kV with metal artifacts) images. For this, we used projection data and subtracted the estimated residual images from the object images, involving hybrid and subjectively reconstructed image usage (back projection and maximum likelihood expectation maximization [MLEM]). The DnCNN-MARHR algorithm was compared with the dual-energy material decomposition reconstruction algorithm (DEMDRA), VM, MLEM, established and commonly used filtered back projection (FBP), and a simultaneous algebraic reconstruction technique-total variation (SART-TV) with MAR processing. MAR was compared using artifact index (AI) and texture analysis. Artifact spread functions (ASFs) for images that were out-of-plane and in-focus were evaluated using a prosthesis phantom. The overall performance of the DnCNN-MARHR algorithm was adequate with regard to the ASF, and the derived images showed better results, without being influenced by the metal type (AI was almost equal to the best value for the DEMDRA). In the ASF analysis, the DnCNN-MARHR algorithm generated better MAR compared with that obtained employing usual algorithms for reconstruction using MAR processing. In addition, comparison of the difference (mean square error) between DnCNN-MARHR and the conventional algorithm resulted in the smallest VM. The DnCNN-MARHR algorithm showed the best performance with regard to image homogeneity in the texture analysis. The proposed algorithm is particularly useful for reducing artifacts in the longitudinal direction, and it is not affected by tissue misclassification.