Organ-specific context-sensitive CT image reconstruction and display
Organ-specific context-sensitive CT image reconstruction and display
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DOI：
10.1117/12.2291897
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发表时间：
2018-03
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影响因子：
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通讯作者：
Sabrina Dorn;Shuqing Chen;S. Sawall;D. Simons;M. May;J. Maier;M. Knaup;H. Schlemmer;A. Maier;M. Lell;M. Kachelriess
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作者：
Sabrina Dorn;Shuqing Chen;S. Sawall;D. Simons;M. May;J. Maier;M. Knaup;H. Schlemmer;A. Maier;M. Lell;M. Kachelriess
在这项工作中，我们提出了一种新的方法，联合收割机互斥CT图像的属性，出现从不同的重建内核和显示设置到一个单一的器官特定的图像重建和显示。我们提出了一个上下文敏感的重建，局部强调所需的图像属性，利用先验解剖知识。此外，我们介绍了一个器官特定的窗口和显示方法，旨在提供一个上级图像可视化。使用从粗到细的分层3D全卷积网络（3D U-Net），CT数据集被分割并分类为不同的器官，例如心脏、脉管系统、肝脏、肾脏、脾脏和肺，以及组织类型骨、脂肪、软组织和血管。最适合于器官、组织类型和临床适应症的重建和显示参数是基于每个体素从预定义的重建参数集合中自动选择的。使用双源CT系统采集的患者数据对该方法进行评估。最终的上下文敏感图像同时链接不同参数设置的适应症特定优势，并导致图像结合组织相关的期望图像属性。与常规重建和显示的图像的比较揭示了在高衰减对象和空气中的改进的空间分辨率，同时在复合图像中的软组织中保持低噪声水平。这些图像同时向读者提供了更多的信息，并且可能不再需要处理多个卷。所提出的方法对于临床工作流程是有用的，并且具有增加偶然发现率的潜力。
In this work, we present a novel method to combine mutually exclusive CT image properties that emerge from different reconstruction kernels and display settings into a single organ-specific image reconstruction and display. We propose a context-sensitive reconstruction that locally emphasizes desired image properties by exploiting prior anatomical knowledge. Furthermore, we introduce an organ-specific windowing and display method that aims at providing a superior image visualization. Using a coarse-to-fine hierarchical 3D fully convolutional network (3D U-Net), the CT data set is segmented and classified into different organs, e.g. the heart, vasculature, liver, kidney, spleen and lung, as well as into the tissue types bone, fat, soft tissue and vessels. Reconstruction and display parameters most suitable for the organ, tissue type, and clinical indication are chosen automatically from a predefined set of reconstruction parameters on a per-voxel basis. The approach is evaluated using patient data acquired with a dual source CT system. The final context-sensitive images simultaneously link the indication-specific advantages of different parameter settings and result in images joining tissue-related desired image properties. A comparison with conventionally reconstructed and displayed images reveals an improved spatial resolution in highly attenuating objects and air while maintaining a low noise level in soft tissue in the compound image. The images present significantly more information to the reader simultaneously and dealing with multiple volumes may no longer be necessary. The presented method is useful for the clinical workflow and bears the potential to increase the rate of incidental findings.