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摘要翻译：
基于欠采样测量的磁共振图像重建算法利用先验信息来补偿丢失的K空间数据。深度学习(DL)提供了一个强大的框架，可以从现有的图像数据集中提取这些信息，通过学习，然后利用这些信息进行重建。利用这一点，最近的方法使用DL来学习从欠采样图像到完全采样图像的映射，使用配对数据集，包括欠采样图像和相应的完全采样图像，隐式集成先验知识。在这篇文章中，我们提出了一种替代的方法，利用无监督的DL学习全采样MR图像的概率分布，特别是变分自动编码器(VAE)，并将其作为重建的显式先验项，使编码操作与先验完全解耦。所得到的重建算法在补偿丢失的k空间数据之前具有强大的图像，而不需要成对的数据集用于训练，也不容易产生相关的灵敏度，例如在训练和测试时间或线圈设置中使用的欠采样模式中的偏差。我们用来自公开数据集的T1加权图像、来自健康志愿者(n=8)的多线圈复杂图像和白质病变图像来评估所提出的方法。该算法利用VAE先验信息，生成了高质量的可视化重建，并获得了较低的RMSE值，在相同的数据集上优于大多数替代方法。在多线圈复杂数据上，该算法得到了准确的幅值和相位重建结果。在白质病变图像的实验中，该方法能较好地重建病变。关键词：重建，MRI，先验概率，机器学习，深度学习，无监督学习，密度估计
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英文标题：
《MR image reconstruction using deep density priors》
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作者：
Kerem C. Tezcan, Christian F. Baumgartner, Roger Luechinger, Klaas P.
Pruessmann, Ender Konukoglu
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最新提交年份：
2018
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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：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically Variational Autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (N=8) and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. Keywords: Reconstruction, MRI, prior probability, machine learning, deep learning, unsupervised learning, density estimation
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PDF链接：
https://arxiv.org/pdf/1711.11386
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