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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

Multi-parametrical representation of tissue parameters in groups.The figure shows whole-brain differences in R-R-PD values between the group of Multiple Sclerosis (MS) patients and the reference group. A) R-R values; B) R-PD values; C) R-PD values. The color scales indicate number of voxels. Blue color indicates a larger number of voxels in the reference group. Red color indicates a larger number of voxels in the MS group. The markers indicate the positions of the mean values in cortical grey matter (CGM), thalamus (Tha), putamen (Put), white matter (WM), and corpus callosum (CC). The mean values are taken from the decreased ROIs presented in Table 2. A square is used for the reference group and a circle for the MS group. The arrows point in the direction from the reference group to the MS group.
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pone-0111688-g004: Multi-parametrical representation of tissue parameters in groups.The figure shows whole-brain differences in R-R-PD values between the group of Multiple Sclerosis (MS) patients and the reference group. A) R-R values; B) R-PD values; C) R-PD values. The color scales indicate number of voxels. Blue color indicates a larger number of voxels in the reference group. Red color indicates a larger number of voxels in the MS group. The markers indicate the positions of the mean values in cortical grey matter (CGM), thalamus (Tha), putamen (Put), white matter (WM), and corpus callosum (CC). The mean values are taken from the decreased ROIs presented in Table 2. A square is used for the reference group and a circle for the MS group. The arrows point in the direction from the reference group to the MS group.

Mentions: In Figure 4 the multi-parametric representations of tissue parameters of the whole brain are projected as 2-dimensional graphs for each R-R, R-PD, and R-PD pair. The differences between the reference group and the MS group are visualized in two colors in the multi-parametric representations. The blue color scale indicates a larger number of voxels with a specific parametric location in the reference group compared to the MS group. Correspondingly, the red scale indicates a larger number of voxels in the MS group. Only voxels with signal intensity higher than 10 of the maximum signal intensity (e.g. 395451 out of 510340 voxels) were taken into account. In this way, differences between the groups are visualized in the multi-parametric space and indications of the direction of disease-specific tissue changes are provided. In Figure 4A, it can be seen that the direction of change due to MS is towards lower R and R values. In Figures 4B and 4C the MS-specific changes point towards higher PD values.


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

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

Multi-parametrical representation of tissue parameters in groups.The figure shows whole-brain differences in R-R-PD values between the group of Multiple Sclerosis (MS) patients and the reference group. A) R-R values; B) R-PD values; C) R-PD values. The color scales indicate number of voxels. Blue color indicates a larger number of voxels in the reference group. Red color indicates a larger number of voxels in the MS group. The markers indicate the positions of the mean values in cortical grey matter (CGM), thalamus (Tha), putamen (Put), white matter (WM), and corpus callosum (CC). The mean values are taken from the decreased ROIs presented in Table 2. A square is used for the reference group and a circle for the MS group. The arrows point in the direction from the reference group to the MS group.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0111688-g004: Multi-parametrical representation of tissue parameters in groups.The figure shows whole-brain differences in R-R-PD values between the group of Multiple Sclerosis (MS) patients and the reference group. A) R-R values; B) R-PD values; C) R-PD values. The color scales indicate number of voxels. Blue color indicates a larger number of voxels in the reference group. Red color indicates a larger number of voxels in the MS group. The markers indicate the positions of the mean values in cortical grey matter (CGM), thalamus (Tha), putamen (Put), white matter (WM), and corpus callosum (CC). The mean values are taken from the decreased ROIs presented in Table 2. A square is used for the reference group and a circle for the MS group. The arrows point in the direction from the reference group to the MS group.
Mentions: In Figure 4 the multi-parametric representations of tissue parameters of the whole brain are projected as 2-dimensional graphs for each R-R, R-PD, and R-PD pair. The differences between the reference group and the MS group are visualized in two colors in the multi-parametric representations. The blue color scale indicates a larger number of voxels with a specific parametric location in the reference group compared to the MS group. Correspondingly, the red scale indicates a larger number of voxels in the MS group. Only voxels with signal intensity higher than 10 of the maximum signal intensity (e.g. 395451 out of 510340 voxels) were taken into account. In this way, differences between the groups are visualized in the multi-parametric space and indications of the direction of disease-specific tissue changes are provided. In Figure 4A, it can be seen that the direction of change due to MS is towards lower R and R values. In Figures 4B and 4C the MS-specific changes point towards higher PD values.

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