Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, John Radcliffe Hospital, Oxford, UK.
Magn Reson Med. 2010 Apr;63(4):881-91. doi: 10.1002/mrm.22230.
Receive array coils play a pivotal role in modern MRI. MR spectroscopy can also benefit from the enhanced signal-to-noise ratio and field of view provided by a receive array. In any experiment using an n-element array, n different complex spectra will be recorded and each spectrum unavoidably contains an undesired noise contribution. Previous algorithms for combining spectra have ignored the fact that the noise detected by different array elements is correlated. We introduce here an algorithm for efficiently, robustly, and automatically combining these n spectra using noise whitening and the singular value decomposition to provide the single combined spectrum that has maximum likelihood in the presence of this correlated noise. Simulations are performed that demonstrate the superiority of this approach to previous methods. Experiments in phantoms and in vivo on the brain, heart, and liver of normal volunteers, at 1.5 T and 3 T, using array coils from eight to 32 elements and with (1)H and (31)P nuclei, validate our approach, which provides signal-to-noise ratio improvements of up to 60% in our tests. The whitening and the singular value decomposition algorithm become most advantageous for large arrays, when the noise is markedly correlated, and when the signal-to-noise ratio is low.
接收阵列线圈在现代 MRI 中起着至关重要的作用。磁共振波谱也可以受益于接收阵列提供的增强的信噪比和视野。在任何使用 n 个元素阵列的实验中，将记录 n 个不同的复数谱，并且每个谱不可避免地包含不希望的噪声贡献。以前用于组合谱的算法忽略了这样一个事实，即不同阵列元件检测到的噪声是相关的。我们在这里引入了一种算法，用于使用噪声白化和奇异值分解有效地、稳健地和自动地组合这些 n 个谱，以提供在存在这种相关噪声的情况下具有最大似然的单个组合谱。进行了模拟，证明了该方法优于以前的方法。在正常志愿者的大脑、心脏和肝脏的体模和体内实验中，在 1.5T 和 3T 下，使用 8 到 32 个元素的阵列线圈，以及 (1)H 和 (31)P 核，验证了我们的方法，该方法在我们的测试中提供了高达 60%的信噪比提高。当噪声明显相关且信噪比低时，白化和奇异值分解算法对大阵列最为有利。
