Dataset Information

Voxel-based texture analysis of the brain.

ABSTRACT: This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer's Disease (AD). The artificially generated effects included hyperintense and hypointense signals added to T1-weighted brain MRIs from 30 healthy subjects. The AD dataset included 30 patients with AD and 30 age/sex matched healthy control subjects. The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups. This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.

SUBMITTER: Maani R

PROVIDER: S-EPMC4355627 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Voxel-based texture analysis of the brain.

Maani Rouzbeh R Yang Yee Hong YH Kalra Sanjay S

PloS one 20150310 3

This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer's Disease (AD). T ...[more]

PMID: 25756621

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Project description:BackgroundMagnetic Resonance Spectroscopy (MRS) can measure in vivo brain tissue metabolism that exhibits unique biochemical characteristics in brain tumors. 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 tumor monitoring. The objective of our study is to propose an automated MRS quantitative approach and assess the feasibility of this approach for glioma grading, prognosis and boundary detection.MethodsAn 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 low-grade gliomas (LGG) and high-grade gliomas (HGG) and for detecting the tumor, peritumoral and contralateral areas, respectively. Seventy-eight cases of pre-operative brain gliomas with pathological reports are included in this study.ResultsCho/NAA, Cho/Cr and Lip-Lac/Cr (LL/Cr) calculated by AQoCE in the tumor area differ significantly between LGG and HGG, with p≤0.005. Using logistic regression combining Cho/NAA, Cho/Cr and LL/Cr 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/NAA and Cho/Cr 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 Siemens MRS processing software are also discussed in this paper.ConclusionsThe AQoCE approach is an automated method of residual water removal and metabolite quantification. It can be applied to multi-voxel 1H-MRS for evaluating brain glioma grading and demonstrating characteristics of brain glioma 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 Siemens.

Dataset Information

Voxel-based texture analysis of the brain.

Publications

Voxel-based texture analysis of the brain.

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