BACKGROUND: Magnetic Resonance Spectroscopy (MRS) can measure in vivo brain tissue metabolism that exhibits unique biochemical characteristics in @entity1313 . For clinical application, an efficient and versatile quantification method of MRS would be an important tool for medical research, particularly for exploring the scientific problem of @entity5 monitoring. The objective of our study is to propose an automated MRS quantitative approach and assess the feasibility of this approach for @entity11 grading, prognosis and boundary detection. METHODS: An automated quantitative approach based on a convex envelope (AQoCE) is proposed in this paper, including preprocessing, convex-envelope based baseline fitting, bias correction, sectional baseline removal, and peak detection, in a total of 5 steps. Some metabolic ratios acquired by this quantification are selected for statistical analysis. An independent sample t-test and the Kruskal-Wallis test are used for distinguishing @entity11 ( @entity11 ) and high-grade @entity11 (HGG) and for detecting the @entity5 , peritumoral and contralateral areas, respectively. Seventy-eight cases of pre-operative @entity13 with pathological reports are included in this study. RESULTS: Cho/ @entity14728 , Cho/ @entity8818 and Lip-Lac/ @entity8818 (LL/ @entity8818 ) calculated by AQoCE in the @entity5 area differ significantly between @entity11 and HGG, with p <=0.005. Using logistic regression combining Cho/ @entity14728 , Cho/ @entity8818 and LL/ @entity8818 to generate a ROC curve, AQoCE achieves a sensitivity of 92.9%, a specificity of 72.2%, and an area under ROC curve (AUC) of 0.860. Moreover, both Cho/ @entity14728 and Cho/ @entity8818 in the AQoCE approach show a significant difference (p <=0.019) between tumoral, peritumoral, and contralateral areas. The comparison between the results of AQoCE and @entity5377 MRS processing software are also discussed in this paper. CONCLUSIONS: The AQoCE approach is an automated method of residual water removal and metabolite quantification. It can be applied to multi-voxel 1H-MRS for evaluating @entity13 grading and demonstrating characteristics of @entity13 metabolism. It can also detect infiltration in the peritumoral area. Under the limited clinical data used, AQoCE is significantly more versatile and efficient compared to the reference approach of @entity5377 .

Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to XXXX Analysis.

@entity8818 :: ('D002857', 'Chemical') :: ['Cr'] @entity13 :: ('MESH:C564230', 'Disease') :: ['brain glioma', 'brain gliomas'] @entity5377 :: ('MESH:D053560', 'Disease') :: ['Siemens'] @entity1313 :: ('MESH:D001932', 'Disease') :: ['brain tumors'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor'] @entity14728 :: ('CHEBI:32918', 'Chemical') :: ['NAA'] @entity11 :: ('MESH:D005910', 'Disease') :: ['gliomas', 'glioma', 'low-grade gliomas', 'LGG']
@entity1313 :: (MESH:D001932,Disease) :: ['Brain Tumor']
