摘要翻译：
高空间-角分辨率扩散MRI(dMRI)已经被证明可以准确地识别复杂的光纤结构，尽管代价是获取时间长。我们提出了一种在高空间-角分辨率下恢复体素内光纤结构的方法，该方法依赖于KQ空间欠采样方案，以实现加速捕获。利用一种结构化的稀疏先验规则化重构光纤方向分布(FOD)的反问题，同时提高了光纤方向的体素稀疏性和空间光滑性。还假定了白质、灰质和脑脊液的空间分布的先验知识。通过一种前向-后向凸优化算法结构，构造并求解了一个最小化问题。模拟和实际数据分析表明，在严重的KQ空间欠采样情况下，可以实现精确的FOD标测，从而有可能在临床环境中实现高空间角dMRI。
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英文标题：
《Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space
  Sampling and Anatomical Priors》
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作者：
Marica Pesce, Audrey Repetti, Anna Aur\'ia, Alessandro Daducci,
  Jean-Philippe Thiran, Yves Wiaux
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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：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：
  High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex fiber configurations, albeit at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter and cerebrospinal fluid is also assumed. A minimization problem is formulated and solved via a forward-backward convex optimization algorithmic structure. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes, potentially enabling high spatio-angular dMRI in the clinical setting.
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PDF链接：
https://arxiv.org/pdf/1802.02912