Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
Magn Reson Med. 2025 Jan;93(1):8-17. doi: 10.1002/mrm.30252. Epub 2024 Aug 18.
The objective of this study was to propose a novel preprocessing approach to simultaneously correct for the frequency and phase drifts in MRS data using cross-correlation technique.
The performance of the proposed method was first investigated at different SNR levels using simulation. Random frequency and phase offsets were added to a previously acquired STEAM human data at 7 T, simulating two different noise levels with and without baseline artifacts. Alongside the proposed spectral cross-correlation (SC) method, three other simultaneous alignment approaches were evaluated. Validation was performed on human brain data at 3 T and mouse brain data at 16.4 T.
The results showed that the SC technique effectively corrects for both small and large frequency and phase drifts, even at low SNR levels. Furthermore, the mean square measurement error of the SC algorithm was comparable to the other three methods used, with much faster processing time. The efficacy of the proposed technique was successfully demonstrated in both human brain MRS data and in a noisy MRS dataset acquired from a small volume-of-interest in the mouse brain.
The study demonstrated the availability of a fast and robust technique that accurately corrects for both small and large frequency and phase shifts in MRS.
本研究旨在提出一种新的预处理方法，利用互相关技术同时校正 MRS 数据的频率和相位漂移。
首先在不同的 SNR 水平下使用模拟来研究所提出方法的性能。在先前采集的 7T STEAM 人体数据上添加随机频率和相位偏移，模拟具有和不具有基线伪影的两种不同噪声水平。除了提出的光谱互相关 (SC) 方法外，还评估了另外三种同时对准方法。在 3T 的人脑数据和 16.4T 的鼠脑数据上进行验证。
结果表明，SC 技术可有效校正小和大频率和相位漂移，即使在低 SNR 水平下也是如此。此外，SC 算法的均方测量误差与使用的其他三种方法相当，但处理时间快得多。所提出技术的有效性在人脑 MRS 数据和从小鼠脑的小感兴趣体积中采集的嘈杂 MRS 数据集上得到了成功证明。
本研究证明了一种快速且稳健的技术的可用性，该技术可准确校正 MRS 中的小和大频率和相位偏移。
