Optimal sampling for "Noquist" reduced-data cine magnetic resonance imaging.
Optimal sampling for "Noquist" reduced-data cine magnetic resonance imaging.
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DOI：
10.1118/1.4770270
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发表时间：
2012-12
期刊：
影响因子：
3.8
通讯作者：
D. Moratal;W. Thomas Dixon;S. Ramamurthy;S. Lerakis;W. James Parks;M. Brummer
中科院分区：
文献类型：
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作者：
D. Moratal;W. Thomas Dixon;S. Ramamurthy;S. Lerakis;W. James Parks;M. Brummer
目的分析和优化部分静态视场下电影磁共振成像加速的“Noquist”方法的信噪比，设计实用的最佳或接近最佳样本集的选择方法，使该方法在可变图像维度时能够可靠地应用。方法为了研究Noquist方法及其实验参数对降维重建图像信噪比的影响，探索获得最高信噪比稳定性的优化方法，选择了3种不同的优化参数：定义噪声进入重建图像的前向矩阵(R(Cond))的条件，以及动态视场(FOV)区域的最大(Φ(Max))和平均(Φ(Meand))线性噪声放大因子。由于Noquist重建中的SNR在整个FOV中通常不是均匀的，并且由于动态区域可能包含图像中与临床更相关的部分，因此这些噪声水平主要是为了优化。使用这三个优化参数，进行了三个实验：作为重要图像尺寸参数的函数的Noquist SNR特性的表征；对于足够小的图像维度，使用词典编纂算法使用穷举搜索来访问在电影成像约束下的所有可能性，所述电影成像约束在序列的每个时间点获得相同数量的视图；以及，从假设的最优模式出发，生成和评估到该最优模式的一系列随机变化的SNR特性。结果分析了稀疏数据选择的影响，给出了信噪比特性与相关采集参数的关系。用穷举法研究了小尺寸图像的最优数据选择，并与算法选择模式进行了比较。通过对真实感图像维度数据选择的进一步研究，证实了这些实验的观察结果，并提出了一种最优选择算法。通过穷举搜索对64例小图像进行了分析，共调用了527 984 141次矩阵求逆运算，计算了每种情况下的几个信噪比参数。提出了一种名为“楼梯井”的算法，该算法允许设计具有最佳信噪比特性的图像尺寸，并与通过穷举搜索分析的实例进行了比较。在详尽研究的71.9%的案例中，楼梯井算法得到了最优解。无一例偏离最优值超过3.2%(R(Cond))、1.0%(Φ(Meand))和4.9%(Φ(Max))。结论我们已经证明了“楼梯井”选择算法在小图像维度下的信噪比最优性，并进行了额外的实验，这些实验都支持该算法对于满足一定对称性约束的任意图像维度的最优性。此外，对于实际的临床图像维度，我们通过该算法给出了与使用Noquist方法相关的总体信噪比特性。此外，从我们的优化实验中观察到的结果使我们能够为保证稳定反演的诺奎斯特图像采集参数的尺寸确定提出建议。此外，这些结果允许预测对于给定图像维度(S、D、T)的重建的预期信噪比特性，相对于传统全网格采集中的信噪比。
PURPOSE To analyze and optimize the signal-to-noise ratio (SNR) for the "Noquist" method for acceleration of cine magnetic resonance imaging in the presence of partially static field of view, designing practical methods for selection of optimal or near-optimal sample sets to allow reliable application of the method for variable image dimensions. METHODS To investigate the impact of the Noquist method and its experimental parameters on the SNR in the image reconstructed from reduced data, and to explore optimization of methods for highest SNR stability, three different optimization parameters have been selected: the condition of the forward matrix (R(cond)) as it defines the propagation of noise into the reconstructed image, and the maximum (Φ(maxD)) and the mean (Φ(meanD)) linear noise amplification factor of the dynamic field-of-view (FOV) region. As SNR in a Noquist reconstruction is often not uniform across the FOV and since dynamic regions may contain the part of the image more clinically relevant, primarily these noise levels are targeted for optimization. Using these three optimization parameters, three experiments were conducted: characterization of Noquist SNR properties as a function of important image size parameters; for sufficiently small image dimensions, employment of exhaustive search using lexicographical algorithms to visit all possibilities under the cine imaging constraint that equal numbers of views are acquired at each time point of the sequence; and, departing from an hypothetically optimal pattern, generation and evaluation of SNR characteristics of a series of random variations to that optimal pattern. RESULTS The impact of favorable sparse data selection is illustrated, and SNR properties are characterized as a function of relevant acquisition parameters. Optimal data selection is investigated by exhaustive methods for small image sizes, and compared with algorithmic selection patterns. Observations from these experiments are confirmed by further studies on data selection for realistic image dimensions and an optimal selection algorithm is proposed. Sixty-four cases of small image sizes were analyzed through exhaustive search with a total of 527 984 141 matrix inversions called in the process, evaluating several SNR parameters for each case. An algorithm, named "Stairwell," that permits to design image dimensions with optimal SNR characteristics is presented, evaluated and compared with cases analyzed through exhaustive search. In 71.9% of the cases exhaustively studied, the Stairwell algorithm yielded optimal solutions. For no case did the deviation from optimum exceed 3.2% (R(cond)), 1.0% (Φ(meanD)), and 4.9% (Φ(maxD)). CONCLUSIONS We have demonstrated SNR-optimality of the "Stairwell" selection algorithm for small image dimensions, and performed additional experiments which all support hypothesized optimality of the algorithm for any image dimensions that satisfy certain symmetry constraints for Noquist reduced-data cine MR imaging. Furthermore, we have presented overall SNR characteristics associated with use of the Noquist method by this algorithm for practical clinical image dimensions. Additionally, observations from our optimization experiments allow us to formulate recommendations for dimensioning Noquist image acquisition parameters which guarantee stable inversion. Moreover, these results allow prediction of the anticipated SNR properties of the reconstruction for given image dimensions (S,D,T), relative to SNR in a conventional full-grid acquisition.