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摘要翻译：
目的：本论文的目的是通过整合低阶和压缩感知来加速心脏弥散张量成像(CDTI)。方法：扩散加权图像具有变换稀疏性和低阶度。这些特性可以联合利用来加速CDTI，特别是当应用相位图来校正跨扩散方向的相位不一致时，从而增强低阶度。本文提出的方法在离体和体内进行了评估，并与单独使用低秩或稀疏约束的方法进行了比较。结果：与单独使用低秩或稀疏约束相比，该方法保留了更准确的螺旋角特征、跨壁连续体和高加速度下的平均扩散系数，同时产生明显的低偏差和高腔内相关系数。结论：低阶度和压缩感知共同促进了离体和体内CDTI的加速，与单独使用任何一种约束相比，都提高了重建的准确性。意义：与以往的加速CDTI的方法相比，该方法在保持肌纤维结构特征的同时具有更高的加速度，从而在未来允许更多的空间覆盖、更高的空间分辨率和更短的时间足迹。
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英文标题：
《Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and
Sparsity Constraints》
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作者：
Sen Ma, Christopher T. Nguyen, Anthony G. Christodoulou, Daniel
Luthringer, Jon Kobashigawa, Sang-Eun Lee, Hyuk-Jae Chang and Debiao Li
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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：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Objective: The purpose of this manuscript is to accelerate cardiac diffusion tensor imaging (CDTI) by integrating low-rankness and compressed sensing. Methods: Diffusion-weighted images exhibit both transform sparsity and low-rankness. These properties can jointly be exploited to accelerate CDTI, especially when a phase map is applied to correct for the phase inconsistency across diffusion directions, thereby enhancing low-rankness. The proposed method is evaluated both ex vivo and in vivo, and is compared to methods using either a low-rank or sparsity constraint alone. Results: Compared to using a low-rank or sparsity constraint alone, the proposed method preserves more accurate helix angle features, the transmural continuum across the myocardium wall, and mean diffusivity at higher acceleration, while yielding significantly lower bias and higher intraclass correlation coefficient. Conclusion: Low-rankness and compressed sensing together facilitate acceleration for both ex vivo and in vivo CDTI, improving reconstruction accuracy compared to employing either constraint alone. Significance: Compared to previous methods for accelerating CDTI, the proposed method has the potential to reach higher acceleration while preserving myofiber architecture features which may allow more spatial coverage, higher spatial resolution and shorter temporal footprint in the future.
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PDF链接：
https://arxiv.org/pdf/1801.03525
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