Zhu X P, Young K, Ebel A, Soher B J, Kaiser L, Matson G, Weiner W M, Schuff N
Department of Radiology, University of California San Francisco, San Francisco, CA 94121, USA.
Magn Reson Med. 2006 Mar;55(3):706-11. doi: 10.1002/mrm.20805.
Short echo time proton MR Spectroscopic Imaging (MRSI) suffers from low signal-to-noise ratio (SNR), limiting accuracy to estimate metabolite intensities. A method to coherently sum spectra in a region of interest of the human brain by appropriate peak alignment was developed to yield a mean spectrum with increased SNR. Furthermore, principal component (PC) spectra were calculated to estimate the variance of the mean spectrum. The mean or alternatively the first PC (PC(1)) spectrum from the same region can be used for quantitation of peak areas of metabolites in the human brain at increased SNR. Monte Carlo simulations showed that both mean and PC(1) spectra were more accurate in estimating regional metabolite concentrations than solutions that regress individual spectra against the tissue compositions of MRSI voxels. Back-to-back MRSI studies on 10 healthy volunteers showed that mean spectra markedly improved reliability of brain metabolite measurements, most notably for myo-inositol, as compared to regression methods.
短回波时间质子磁共振波谱成像(MRSI)存在信噪比(SNR)低的问题,限制了估计代谢物强度的准确性。开发了一种通过适当的峰对齐在人脑感兴趣区域相干求和谱的方法,以产生具有更高信噪比的平均谱。此外,计算主成分(PC)谱以估计平均谱的方差。来自同一区域的平均谱或第一主成分(PC(1))谱可用于在更高信噪比下定量人脑代谢物的峰面积。蒙特卡罗模拟表明,与将个体谱与MRSI体素的组织成分进行回归的方法相比,平均谱和PC(1)谱在估计区域代谢物浓度方面更准确。对10名健康志愿者进行的背对背MRSI研究表明,与回归方法相比,平均谱显著提高了脑代谢物测量的可靠性,尤其是对于肌醇。