OBJECTIVE: The aim of this study was to develop a method for evaluating the spatial distribution of human brain gliomas in individual subjects by evaluating the correlation between the Choline (Cho) signal intensity and the diffusion trace (Tr(ADC)) values. MATERIALS AND METHODS: Eleven patients with different histopathologic diagnoses and five healthy subjects were examined with diffusion-weighted EPI-trace sequence and (1)H MR spectroscopic imaging. The calculation of the correlation between choline and Tr(ADC) values on a pixel-by-pixel basis and simulations estimating the influence of partial volume effects on the result were performed. RESULTS: Statistical evaluation of the data in the patients with a glioblastoma showed that pixels corresponding to different tissue states are situated in different areas in the Cho-Tr(ADC) correlation graph. Namely, points forming an inverse linear dependence interpreted as an area of an active tumor were observed. Different types of correlations were found in grade II and III gliomas. No statistically significant correlation was found in healthy subjects. Simulations proved that the observed linear dependence cannot be attributed solely to partial volume effects. CONCLUSION: The analysis of the correlation between Cho concentrations and Tr(ADC) values on a pixel-by-pixel basis should help the regional identification of the pathological state of a tissue in patients with a glioblastoma.
OBJECTIVE: The aim of this study was to develop a method for evaluating the spatial distribution of humanbrain gliomas in individual subjects by evaluating the correlation between the Choline (Cho) signal intensity and the diffusion trace (Tr(ADC)) values. MATERIALS AND METHODS: Eleven patients with different histopathologic diagnoses and five healthy subjects were examined with diffusion-weighted EPI-trace sequence and (1)H MR spectroscopic imaging. The calculation of the correlation between choline and Tr(ADC) values on a pixel-by-pixel basis and simulations estimating the influence of partial volume effects on the result were performed. RESULTS: Statistical evaluation of the data in the patients with a glioblastoma showed that pixels corresponding to different tissue states are situated in different areas in the Cho-Tr(ADC) correlation graph. Namely, points forming an inverse linear dependence interpreted as an area of an active tumor were observed. Different types of correlations were found in grade II and III gliomas. No statistically significant correlation was found in healthy subjects. Simulations proved that the observed linear dependence cannot be attributed solely to partial volume effects. CONCLUSION: The analysis of the correlation between Cho concentrations and Tr(ADC) values on a pixel-by-pixel basis should help the regional identification of the pathological state of a tissue in patients with a glioblastoma.
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