Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction.
Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction.
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DOI：
10.1002/mrm.22841
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发表时间：
2011-09
影响因子：
3.3
通讯作者：
Nezafat, Reza
中科院分区：
文献类型：
--
作者：
Akcakaya, Mehmet;Basha, Tamer A.;Goddu, Beth;Goepfert, Lois A.;Kissinger, Kraig V.;Tarokh, Vahid;Manning, Warren J.;Nezafat, Reza
关键词：
提出了一种改进的基于欠采样k-空间数据的图像重建方法--“低维结构自学习阈值(Lost)”方法，该方法利用了底层图像的结构。重建来自全采样k空间中心的低分辨率图像，以学习具有相似解剖特征的图像块。利用底层的低维属性，将这些补丁排列成“相似性簇”，随后对这些簇进行去混叠和伪影去除处理。在一项初步的冠状动脉MRI研究中，评估了所提出的方法在缩短扫描时间方面的有效性。首先，在对10名健康成人受试者的回溯性研究中，我们评估了使用基于小波的L1范数最小化和全变差压缩传感(CS)的回溯性欠采样和重建。血管清晰度和均方误差的定量测量以及定性图像评分被用来比较2、3和4的重建速率。随后，在一项前瞻性研究中，使用这些速率采集冠状动脉MRI数据，并将丢失重建图像与使用均匀欠采样和敏感性编码(SENSE)重建的加速数据采集进行比较。主观图像质量和清晰度数据表明，Lost在所有速率下都优于替代技术。与SENSE或L1最小化CS相比，预期丢失产生的图像具有更高的质量。所提出的Lost技术极大地改善了用于加速冠状动脉MRI采集的图像重建。
An improved image reconstruction method from undersampled k-space data, “LOw-dimensional-structure Self-learning and Thresholding (LOST),” which utilizes the structure from the underlying image is presented. A low resolution image from the fully-sampled k-space center is reconstructed to learn image patches of similar anatomical characteristics. These patches are arranged into “similarity clusters,” which are subsequently processed for de-aliasing and artifact removal, using underlying low-dimensional properties. The efficacy of the proposed method in scan time reduction was assessed in a pilot coronary MRI study. Initially, in a retrospective study on 10 healthy adult subjects, we evaluated retrospective undersampling and reconstruction using LOST, wavelet-based l1-norm minimization and total variation compressed-sensing (CS). Quantitative measures of vessel sharpness and mean square error, and qualitative image scores were used to compare reconstruction for rates of 2, 3 and 4. Subsequently, in a prospective study, coronary MRI data were acquired using these rates, and LOST-reconstructed images were compared with an accelerated data acquisition using uniform undersampling and sensitivity-encoding (SENSE) reconstruction. Subjective image quality and sharpness data indicate that LOST outperforms the alternative techniques for all rates. The prospective LOST yields images with superior quality compared to SENSE or l1-minimization CS. The proposed LOST technique greatly improves image reconstruction for accelerated coronary MRI acquisitions.
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