摘要翻译：
超声成像的成本效益和实际无害性使其成为最广泛的医疗诊断工具之一。不幸的是，基于波束形成的图像形成会产生颗粒斑点噪声、模糊、阴影和其他伪影。为了克服这些影响，最终的目标是通过求解全波传播反问题来重建组织的声学特性。在本论文中，我们利用多分辨率卷积神经网络(CNN)向这个目标迈进了一步。因此，我们能够从人体真实CT扫描中模拟获得的反射超声射频(RF)数据重建CT质量的图像。我们还证明了CNN能够模仿现有的计算量大的去啄方法，从而节省了数量级的计算量，并使其适合于实时应用。
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英文标题：
《Towards CT-quality Ultrasound Imaging using Deep Learning》
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作者：
Sanketh Vedula, Ortal Senouf, Alex M. Bronstein, Oleg V. Michailovich,
  Michael Zibulevsky
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Physics        物理学
二级分类：Medical Physics        医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling.  Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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英文摘要：
  The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflected ultrasound radio-frequency(RF) data obtained by simulation from real CT scans of a human body. We also show that CNN is able to imitate existing computationally heavy despeckling methods, thereby saving orders of magnitude in computations and making them amenable to real-time applications.
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PDF链接：
https://arxiv.org/pdf/1710.06304