Rodgers Christopher T, Robson Matthew D
Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom.
Magn Reson Med. 2016 Feb;75(2):473-87. doi: 10.1002/mrm.25618. Epub 2015 Mar 28.
Combining spectra from receive arrays, particularly X-nuclear spectra with low signal-to-noise ratios (SNRs), is challenging. We test whether data-driven combination methods are better than using computed coil sensitivities.
Several combination algorithms are recast into the notation of Roemer's classic formula, showing that they differ primarily in their estimation of coil receive sensitivities. This viewpoint reveals two extensions of the whitened singular-value decomposition (WSVD) algorithm, using temporal or temporal + spatial apodization to improve the coil sensitivities, and thus the combined spectral SNR.
Radiofrequency fields from an array were simulated and used to make synthetic spectra. These were combined with 10 algorithms. The combined spectra were then assessed in terms of their SNR. Validation used phantoms and cardiac (31) P spectra from five subjects at 3T.
Combined spectral SNRs from simulations, phantoms, and humans showed the same trends. In phantoms, the combined SNR using computed coil sensitivities was lower than with WSVD combination whenever the WSVD SNR was >14 (or >11 with temporal apodization, or >9 with temporal + spatial apodization). These new apodized WSVD methods gave higher SNRs than other data-driven methods.
In the human torso, at frequencies ≥49 MHz, data-driven combination is preferable to using computed coil sensitivities. Magn Reson, 2015. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Magn Reson Med 75:473-487, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.
将来自接收阵列的频谱进行合并，尤其是信噪比（SNR）较低的X核频谱，具有挑战性。我们测试数据驱动的合并方法是否优于使用计算出的线圈灵敏度。
几种合并算法被重新表示为勒默尔经典公式的形式，表明它们的主要区别在于对线圈接收灵敏度的估计。这种观点揭示了白化奇异值分解（WSVD）算法的两种扩展，即使用时间或时间+空间变迹来提高线圈灵敏度，从而提高合并频谱的SNR。
对阵列的射频场进行模拟并用于生成合成频谱。将这些频谱与10种算法进行合并。然后根据SNR对合并后的频谱进行评估。验证使用了体模以及来自5名3T受试者的心脏（31）P频谱。
模拟、体模和人体的合并频谱SNR呈现相同趋势。在体模中，当WSVD SNR>14（时间变迹时>11，或时间+空间变迹时>9）时，使用计算出的线圈灵敏度进行合并的SNR低于WSVD合并。这些新的变迹WSVD方法比其他数据驱动方法具有更高的SNR。
在人体躯干中，频率≥49 MHz时，数据驱动的合并优于使用计算出的线圈灵敏度。《磁共振》，2015年。©2015作者。《磁共振医学》由威利期刊公司代表国际磁共振医学学会出版。这是一篇根据知识共享署名许可协议条款的开放获取文章，允许在任何媒介中使用、分发和复制，前提是正确引用原始作品。《磁共振医学》75:473 - 487，2016。©2015作者。《磁共振医学》由威利期刊公司代表国际磁共振医学学会出版。
