Magnetic resonance imagingMRI texture analysis on texture test objects, normal brain and intracranial tumors
Introduction
Several studies have addressed NMR tissue characterization for more than twenty years, both by ex-vivo relaxation times measurements and by quantitative MRI. Malignant intracranial tumors usually have longer relaxation times than benign tumors. However, the relaxation times overlap between these two kinds of tumors. Furthermore the different parts of the same tumor and often the surrounding edema are not clearly discriminated [1], [2], [3], [4], [5], [6], [7], [8], [9]. The average measurements performed in-vivo on full Region-of-Interest (ROI) or ex-vivo on surgery samples could explain these early disappointing results shading the tumor heterogeneity. Then a more sophisticated quantitative approach of MRI, taking into account the local pixel heterogeneity, was requested to improve the image segmentation and to contribute to a more clinically relevant tumor grading.
First used for satellite images, texture analysis (TA) medical applications first appeared in MRI at the beginning of the eighties [10]. TA covers several methods enabling quantification and statistical analysis of the distribution of the gray level values on an image ROI. A complete mathematical description of the TA parameters has already been published [11]. TA can offer information not visible by the human eye that can only discriminate complexity less than order two [12]. At this time, only very few studies have been performed to characterize brain tissue by TA. Lerski et al. [13], Schad et al. [14] and Kjaer et al. [15] have shown on preliminary clinical series that TA seems to be an interesting tool to characterize brain tissues and tumors such as glioblastoma and metastases in their general aspect (contrast, intensity, homogeneity) or their different constituents (micro or macro textures). They didn’t use raw data images (T1 and T2 weighted images) but T1 and T2 calculated images of metastases and heterogeneous brain tumors such as glioblastoma for their texture analysis.
In this study, a preliminary testing with TA test objects was first performed to evaluate the sensitivity and the effectiveness of the method in relation to variation of the MRI acquisition parameters. Comparatively to the previously published brain tumors TA studies performed on the same MRI unit with calculated T1 and T2 images, the goals of this study on a larger clinical sample size was to test the ability of TA to be performed: i) on more classic raw images (T1 or T2 weighted) and ii) on three different MRI units of three different university hospitals in their own routine acquisition conditions.
Section snippets
Test objects
Specific TA test objects were developed under the auspice of a European BIOMED Concerted Action (BMH-CT94-1274) [16]. They consisted of three tubes filled with gadolinium doped agarose gel (T1 = 360 ms and T2 = 80 ms at 1.5T and 296 K) and foams simulating coarse, medium or fine textures. The sizes of the foam porosities were respectively 1.69 to 3.70 mm for the first tube, 1.02 to 1.69 mm for the second one and 0.72 to 1.02 mm for the third one.
Patients
In agreement with the French ethical legislation
Texture analysis
Five circular ROI as large as possible (about 220 pixels) were chosen on three different slices and for each test object: 2 in the top gel and 3 in each foam to test the measurement reproducibility. On brain images, the ROI (were respectively 90 to 120 pixels and 40 to 180 pixels on the axial and sagittal slices according to the tissue studied) were visually defined as large as possible by a radiologist and chosen where the different parts (solid and liquid) of the tumor and the surrounding
Data analysis
Correspondence Factorial Analysis (CFA) was first performed with a specific software Bi@Loginserm [(1979–1987, INSERM, Paris (France)] using all the fifty texture parameters (2200 features for all subjects and 750 for all test tubes) to select the most statistically significant features (p < 0.05).
A Hierarchical Ascending Classification (HAC) was then used with the selected features to perform a four-class classification of test objects images: gel, large, intermediate and small porosities. For
Test objects
In the range of the SNR and resolutions tested for the gel and for the three foam sizes of the specific test objects used, no significant differences in TA classifications for both MPGR (T1w) and FSE (T2w) sequences have been evidenced.
The number of well-classified ROI (for the three foams sizes) was independent from the excitations number and slice thickness. The average number of well classified ROI was 6/10 for the gel, 8/10 for the fine foam, 8/10 for the medium foam and 9/10 for the coarse
Discussion and conclusion
TA method has been first presented as largely dependent from: i) the acquisition parameters, ii) the quality assessment of the MRI device, and iii) the methods of image reconstruction and processing [16], [17]. Then the application of texture analysis methods on three different MRI units using the same kind of MRI device but with slightly different sequence parameters, managed by different staffs for “maintenance” and for image acquisition was a challenge. The goal was to perform MR exams
Acknowledgements
We are grateful to J. Y. Bansard (Inst. Of public health, University of Rennes) for his helpful contribution in data analysis.
This work was supported by the Association pour la Recherche contre le Cancer (ARC) and by the Ligue Nationale Contre le Cancer.
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