A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms
A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms
复制标题
DOI：
10.1002/mp.13640
复制
发表时间：
2019-12-01
期刊：
影响因子：
3.8
通讯作者：
Wang, Ge
中科院分区：
文献类型：
--
作者：
De Man, Quinten;Haneda, Eri;Wang, Ge
机器学习，特别是深度学习，已经被应用于典型的X射线计算机层析成像(CT)应用，包括图像重建、图像增强、图像域特征检测和图像域特征表征。据我们所知，这是第一个直接基于CT投影数据进行特征检测和分析的机器学习研究。具体地说，我们提出了神经网络方法在正弦图域中检测和表征血管，避免了重建过程中引入的任何部分体积、波束硬化或运动伪影。首先，我们使用残差编解码卷积神经网络(REDCNN)来估计正弦图域血管映射。接下来，我们估计血管中心线，并从原始的正弦图中提取仅限于血管的正弦图，去除任何背景信息。最后，我们使用完全连接的神经网络从仅有血管的正弦图中估计血管管腔横截面积。我们使用CatSim模拟、血管模体的真实CT测量和NIH CT图像数据库中的临床数据来训练和测试所提出的方法。我们取得了令人鼓舞的初步结果，表明了CT分析在正弦图域的可行性。原则上，正弦图域分析应该可以用于许多其他更复杂的临床CT分析任务。这种正弦图域分析方法需要进一步的研究才能成为临床应用的实用方法。(C)2019年美国医学物理学家协会。
Machine Learning, especially deep learning, has been used in typical x-ray computed tomography (CT) applications, including image reconstruction, image enhancement, image domain feature detection and image domain feature characterization. To our knowledge, this is the first study on machine learning for feature detection and analysis directly based on CT projection data. Specifically, we present neural network methods for blood vessel detection and characterization in the sinogram domain avoiding any partial volume, beam hardening, or motion artifacts introduced during reconstruction. First, we estimate sinogram domain vessel maps using a residual encoder-decoder convolutional neural network (REDCNN). Next, we estimate the vessel centerline and we extract the vessel-only sinogram from the original sinogram, eliminating any background information. Finally, we use a fully connected neural network to estimate the vessel lumen cross-sectional area from the vessel-only sinogram. We trained and tested the proposed methods using CatSim simulations, real CT measurements of vessel phantoms, and clinical data from the NIH CT image database. We achieved encouraging initial results showing the feasibility of CT analysis in the sinogram domain. In principle, sinogram domain analysis should be possible for many other and more complicated clinical CT analysis tasks. Further studies are needed for this sinogram domain analysis approach to become practical for clinical applications. (C) 2019 American Association of Physicists in Medicine.