摘要翻译：
背景和目的：我们的目的是开发一种深度学习血管造影(DLA)方法，从单一的对比增强采集中生成三维脑血管造影。材料和方法：根据IRB协议，从内部数据库中随机选择105例3D-DSA检查。所有患者均使用临床系统（Axiom Artis zee，Siemens Healthineers）结合标准注射方案获得。来自35个受试者的超过1.5亿个标记体素被用于训练。训练一个深度卷积神经网络，将每个图像体素分为三种组织类型（血管、骨骼和软组织）。然后将训练好的DLA模型应用于8名受试者的验证队列和其余62名受试者的最终测试队列中的组织分类。最后的血管组织类用于生成3D-DLA图像。为了量化训练模型的泛化误差，计算了相关解剖学血管分类的准确度、灵敏度、精密度和F1分值。对3D-DLA和临床3D-DSA图像进行定性评估，以确定扫描间运动伪影的存在。结果：血管病分类正确率为98.7%([98.3,99.1]%)。除耳囊和鼻腔的小区域外，所有3D-DLA检查病例未观察到来自骨结构的残余信号，而3D-DSAS检查病例的37%(23/62)。结论：DLA准确地再现了3D-DSA重建的血管解剖结构。DLA减少了由扫描间运动引起的误配准伪影。DLA减少了获得临床有用的3D-DSA所需的辐射照射
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英文标题：
《Deep Learning Angiography (DLA): Three-dimensional C-arm Cone Beam CT
  Angiography Using Deep Learning》
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作者：
Juan C. Montoya, Yinsheng Li, Charles Strother, and Guang-Hong Chen
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最新提交年份：
2018
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分类信息：

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：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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：
  Background and Purpose: Our purpose was to develop a deep learning angiography (DLA) method to generate 3D cerebral angiograms from a single contrast-enhanced acquisition.   Material and Methods: Under an approved IRB protocol 105 3D-DSA exams were randomly selected from an internal database. All were acquired using a clinical system (Axiom Artis zee, Siemens Healthineers) in conjunction with a standard injection protocol. More than 150 million labeled voxels from 35 subjects were used for training. A deep convolutional neural network was trained to classify each image voxel into three tissue types (vasculature, bone and soft tissue). The trained DLA model was then applied for tissue classification in a validation cohort of 8 subjects and a final testing cohort consisting of the remaining 62 subjects. The final vasculature tissue class was used to generate the 3D-DLA images. To quantify the generalization error of the trained model, accuracy, sensitivity, precision and F1-scores were calculated for vasculature classification in relevant anatomy. The 3D-DLA and clinical 3D-DSA images were subject to a qualitative assessment for the presence of inter-sweep motion artifacts.   Results: Vasculature classification accuracy and 95% CI in the testing dataset was 98.7% ([98.3, 99.1] %). No residual signal from osseous structures was observed for all 3D-DLA testing cases except for small regions in the otic capsule and nasal cavity compared to 37% (23/62) of the 3D-DSAs.   Conclusion: DLA accurately recreated the vascular anatomy of the 3D-DSA reconstructions without mask. DLA reduced mis-registration artifacts induced by inter-sweep motion. DLA reduces radiation exposure required to obtain clinically useful 3D-DSA
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PDF链接：
https://arxiv.org/pdf/1801.0952