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相控阵磁共振波谱数据在相关噪声存在下的自适应优化组合(AOC):与去相关噪声或白化方法的比较。

Adaptively Optimized Combination (AOC) of Phased-Array MR Spectroscopy Data in the Presence of Correlated Noise: Compared with Noise-Decorrelated or Whitened Methods.

机构信息

Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA.

Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA.

出版信息

Magn Reson Med. 2017 Sep;78(3):848-859. doi: 10.1002/mrm.26504. Epub 2016 Nov 21.

DOI:10.1002/mrm.26504
PMID:27873353
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7200078/
Abstract

PURPOSE

A method for adaptively optimized combination (AOC) of MR spectroscopic data from a coil array was recently introduced. The superior performance of the AOC method is evident when compared with the methods that assume uncorrelated noise between coil elements. However, it is unclear whether the AOC method represents the most optimal combination in the presence of correlated noise, when compared with the noise-decorrelated or whitened methods that specifically tackle the correlated noise between coil elements.

METHODS

A new, unified theoretical framework was developed to illustrate the relationship between the AOC method and three noise-decorrelated or whitened methods, namely, noise-decorrelated combination (nd-comb), whitened singular value decomposition (WSVD), and improved WSVD (WSVD+Apod). Simulation-based comparisons and in vivo human brain experiments on a 3 Tesla (T) MRI scanner were performed using an 8-channel phased-array head coil.

RESULTS

Compared with the noise-decorrelated or whitened methods, the AOC method consistently yielded the best combination in terms of the robustness against noise and maintaining the combined spectrum from distortion, and the superior performance was most evident at a low signal-to-noise ratio (SNR).

CONCLUSION

The AOC method represents the theoretical optimal combination in the presence of correlated noise between coil elements, whereas the three noise-decorrelated or whitened methods are asymptotically optimal. Magn Reson Med 78:848-859, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

摘要

目的

最近引入了一种用于线圈阵列磁共振波谱数据自适应优化组合(AOC)的方法。与假设线圈元件之间无相关噪声的方法相比,AOC 方法的性能明显更好。然而,当与专门针对线圈元件之间相关噪声的去噪或白化方法(即去噪组合(nd-comb)、白化奇异值分解(WSVD)和改进的 WSVD(WSVD+Apod))相比,在存在相关噪声的情况下,AOC 方法是否代表最优化组合尚不清楚。

方法

开发了一个新的统一理论框架,以说明 AOC 方法与三种去噪或白化方法(即去噪组合(nd-comb)、白化奇异值分解(WSVD)和改进的 WSVD(WSVD+Apod))之间的关系。在 3T MRI 扫描仪上使用 8 通道相控阵头部线圈进行了基于模拟的比较和体内人脑实验。

结果

与去噪或白化方法相比,AOC 方法在抗噪声和保持组合谱失真方面始终具有最佳的组合性能,在低信噪比(SNR)下性能更为明显。

结论

在存在线圈元件之间相关噪声的情况下,AOC 方法代表理论上的最优组合,而三种去噪或白化方法则是渐近最优的。磁共振医学 78:848-859,2017。©2016 年国际磁共振学会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/e4a6624508b6/nihms-1577670-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/047619783ee1/nihms-1577670-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/a549615100a5/nihms-1577670-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/0c1fb5eda384/nihms-1577670-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/35511df98e91/nihms-1577670-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/e4a6624508b6/nihms-1577670-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/047619783ee1/nihms-1577670-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/a549615100a5/nihms-1577670-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/0c1fb5eda384/nihms-1577670-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/35511df98e91/nihms-1577670-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26b4/7200078/e4a6624508b6/nihms-1577670-f0005.jpg

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