摘要目的 针对磁共振波谱成像(MRSI)数据,研究基于Hankel矩阵的量化方法 、抑水方法 及代谢物信息图像成像方法 .方法 分析不同Hankel矩阵结构对几种MRSI数据方法 的影响,得到量化效率较高的Hankel矩阵结构.应用水成分信号强度最大的特点,提出基于最大幅值抑水处理MRSI方法 .从MRSI数据中提取感兴趣代谢物信息,再通过双线性插值进行代谢物信息成像.结果 Hankel矩阵列数为量化信号点数3/4时MRSI数据幅值及频率参数误差达到最小,应用3/4信号长度构成Hankel矩阵的基于Hankel矩阵及部分重正交Lanczos算法的奇异值分解(HLSVDPRO)方法 ,仿真数据的幅值、频率、衰减系数准确度分别为96.94％、99.72％、95.55％.数据量化速度随采样点数的增加而减小,采样点数为512点时,量化参数误差达到最小.基于最大幅值抑水的方法 在仿真数据的抑水程度为99.55％.结论 将优化的Hankel矩阵结构应用于基于Hankel矩阵的MRSI量化方法 中对参数准确度和速度均有提升,最佳采样点数为512,基于最大幅值抑水方法 对MRSI数据抑水彻底,通过对代谢物信息成像,可在(超)早期对疾病进行诊断.

abstractsObjective To study the quantification method based on Hankel matrix,the water suppression method and the metabolite imaging method for magnetic resonance spectroscopic imaging (MRSI) data.Methods Impact of Hankel data matrix on quantification MRSI methods were analyzed to obtain the most efficient Hankel matrix structure.The maximum amplitude method of the water signal peak was proposed for MRSI data water suppression.The interested metabolites information was extracted from MRSI data,and then metabolite image was obtained through bilinear interpolation algorithm.Results The minimum amplitude error and the minimum frequency error were acquired when columns number was 3/4 sampling points.The amplitude,frequency and the damping factor of the simulation data accuracy was 96.94％,99.72％ and 95.55％ respectively.Hankel lanczos with partial reorthogonalization singular value decomposition (HLSVDPRO) method with 3/4 sampling points was used to form Hankel matrices.The speed of quantification decreased with the increase of sampling points.The error of quantification parameter reached minimum when the number of sampling points was 512.The water suppression degree of simulation data was 99.55％ with the maximum amplitude water suppression method.Conclusions The accuracy and the speed of the quantification are promoted with an optimized Hankel matrix structure for the MRSI quantization method.The optimal length of sampling points is 512.The maximum amplitude method can suppress water perfectly.Doctors can detect the presence of tumor regions in human body at the (super) early stage by metabolite information images.