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Multi-parametric representation of voxel-based quantitative magnetic resonance imaging.

Engström M, Warntjes JB, Tisell A, Landtblom AM, Lundberg P - PLoS ONE (2014)

Bottom Line: The resulting parameter images were normalized to a standard template.The results were visualized by conventional geometric representations and also by multi-parametric representations.In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes.

View Article: PubMed Central - PubMed

Affiliation: Division of Radiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.

ABSTRACT
The aim of the study was to explore the possibilities of multi-parametric representations of voxel-wise quantitative MRI data to objectively discriminate pathological cerebral tissue in patients with brain disorders. For this purpose, we recruited 19 patients with Multiple Sclerosis (MS) as benchmark samples and 19 age and gender matched healthy subjects as a reference group. The subjects were examined using quantitative Magnetic Resonance Imaging (MRI) measuring the tissue structure parameters: relaxation rates, R(1) and R(2), and proton density. The resulting parameter images were normalized to a standard template. Tissue structure in MS patients was assessed by voxel-wise comparisons with the reference group and with correlation to a clinical measure, the Expanded Disability Status Scale (EDSS). The results were visualized by conventional geometric representations and also by multi-parametric representations. Data showed that MS patients had lower R(1) and R(2), and higher proton density in periventricular white matter and in wide-spread areas encompassing central and sub-cortical white matter structures. MS-related tissue abnormality was highlighted in posterior white matter whereas EDSS correlation appeared especially in the frontal cortex. The multi-parameter representation highlighted disease-specific features. In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes. Results from voxel-based statistical analysis, as exemplified in the present work, may guide radiologists where in the image to inspect for signs of disease. Future clinical studies must validate the usability of the method in clinical practice.

No MeSH data available.


Related in: MedlinePlus

Geometrical representation of the correlation of tissue parameters and clinical measures.The figure shows the geometrical representation of the correlation of R–R–PD values with a clinical measure for the group of Multiple Sclerosis (MS) patients. Shown as the color overlay is the slope of the voxel-based correlation with Expanded Disability Status Scale (EDSS). A) longitudinal relaxation rate (R) on a scale r = 0.0 to -0.15; B) transversal relaxation rate (R) on a scale r = 0.0 to -1; C) proton density (PD) on a scale r = 0.0–4.0. The correlation images are shown in the same slices as in Figure 2. The figure shows uncorrected statistics, thresholded at T = 2. In the background synthetic T2-weighted images of the MS group in the same slices are displayed for visual guidance.
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pone-0111688-g003: Geometrical representation of the correlation of tissue parameters and clinical measures.The figure shows the geometrical representation of the correlation of R–R–PD values with a clinical measure for the group of Multiple Sclerosis (MS) patients. Shown as the color overlay is the slope of the voxel-based correlation with Expanded Disability Status Scale (EDSS). A) longitudinal relaxation rate (R) on a scale r = 0.0 to -0.15; B) transversal relaxation rate (R) on a scale r = 0.0 to -1; C) proton density (PD) on a scale r = 0.0–4.0. The correlation images are shown in the same slices as in Figure 2. The figure shows uncorrected statistics, thresholded at T = 2. In the background synthetic T2-weighted images of the MS group in the same slices are displayed for visual guidance.

Mentions: In Figure 3, the geometrical representations of the correlation between tissue parameters and a clinical measure, the Expanded Disability Status Scale (EDSS), are shown. When comparing the EDSS correlation maps with the results in Figure 2, which shows voxel-based differences between MS patients and the reference group, some overlap was observed, especially in periventricular WM and the corona radiata. However, there were also features with distinct differences between the two representations. Notably, MS-related tissue pathology in frontal WM in, and adjacent to, the corpus callosum was highly correlated with EDSS, while WM changes in the posterior corpus callosum were highlighted in the group difference representations. These features are most clearly visualized in the sagittal images in Figures 2 and 3, which show uncorrected results. In this study, the differences between MS patients and the reference group were significant when correcting for multiple comparisons, whereas the EDSS correlations were not significant at the corrected level.


Multi-parametric representation of voxel-based quantitative magnetic resonance imaging.

Engström M, Warntjes JB, Tisell A, Landtblom AM, Lundberg P - PLoS ONE (2014)

Geometrical representation of the correlation of tissue parameters and clinical measures.The figure shows the geometrical representation of the correlation of R–R–PD values with a clinical measure for the group of Multiple Sclerosis (MS) patients. Shown as the color overlay is the slope of the voxel-based correlation with Expanded Disability Status Scale (EDSS). A) longitudinal relaxation rate (R) on a scale r = 0.0 to -0.15; B) transversal relaxation rate (R) on a scale r = 0.0 to -1; C) proton density (PD) on a scale r = 0.0–4.0. The correlation images are shown in the same slices as in Figure 2. The figure shows uncorrected statistics, thresholded at T = 2. In the background synthetic T2-weighted images of the MS group in the same slices are displayed for visual guidance.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230947&req=5

pone-0111688-g003: Geometrical representation of the correlation of tissue parameters and clinical measures.The figure shows the geometrical representation of the correlation of R–R–PD values with a clinical measure for the group of Multiple Sclerosis (MS) patients. Shown as the color overlay is the slope of the voxel-based correlation with Expanded Disability Status Scale (EDSS). A) longitudinal relaxation rate (R) on a scale r = 0.0 to -0.15; B) transversal relaxation rate (R) on a scale r = 0.0 to -1; C) proton density (PD) on a scale r = 0.0–4.0. The correlation images are shown in the same slices as in Figure 2. The figure shows uncorrected statistics, thresholded at T = 2. In the background synthetic T2-weighted images of the MS group in the same slices are displayed for visual guidance.
Mentions: In Figure 3, the geometrical representations of the correlation between tissue parameters and a clinical measure, the Expanded Disability Status Scale (EDSS), are shown. When comparing the EDSS correlation maps with the results in Figure 2, which shows voxel-based differences between MS patients and the reference group, some overlap was observed, especially in periventricular WM and the corona radiata. However, there were also features with distinct differences between the two representations. Notably, MS-related tissue pathology in frontal WM in, and adjacent to, the corpus callosum was highly correlated with EDSS, while WM changes in the posterior corpus callosum were highlighted in the group difference representations. These features are most clearly visualized in the sagittal images in Figures 2 and 3, which show uncorrected results. In this study, the differences between MS patients and the reference group were significant when correcting for multiple comparisons, whereas the EDSS correlations were not significant at the corrected level.

Bottom Line: The resulting parameter images were normalized to a standard template.The results were visualized by conventional geometric representations and also by multi-parametric representations.In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes.

View Article: PubMed Central - PubMed

Affiliation: Division of Radiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.

ABSTRACT
The aim of the study was to explore the possibilities of multi-parametric representations of voxel-wise quantitative MRI data to objectively discriminate pathological cerebral tissue in patients with brain disorders. For this purpose, we recruited 19 patients with Multiple Sclerosis (MS) as benchmark samples and 19 age and gender matched healthy subjects as a reference group. The subjects were examined using quantitative Magnetic Resonance Imaging (MRI) measuring the tissue structure parameters: relaxation rates, R(1) and R(2), and proton density. The resulting parameter images were normalized to a standard template. Tissue structure in MS patients was assessed by voxel-wise comparisons with the reference group and with correlation to a clinical measure, the Expanded Disability Status Scale (EDSS). The results were visualized by conventional geometric representations and also by multi-parametric representations. Data showed that MS patients had lower R(1) and R(2), and higher proton density in periventricular white matter and in wide-spread areas encompassing central and sub-cortical white matter structures. MS-related tissue abnormality was highlighted in posterior white matter whereas EDSS correlation appeared especially in the frontal cortex. The multi-parameter representation highlighted disease-specific features. In conclusion, the proposed method has the potential to visualize both high-probability focal anomalies and diffuse tissue changes. Results from voxel-based statistical analysis, as exemplified in the present work, may guide radiologists where in the image to inspect for signs of disease. Future clinical studies must validate the usability of the method in clinical practice.

No MeSH data available.


Related in: MedlinePlus