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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 regions of interest (ROIs).The figure shows differences in R and R values between Multiple Sclerosis (MS) patients and the reference group for three separate cerebral ROIs. A) Caudate nucleus; B) Thalamus; C) Total white matter. The ROIs were all reduced with 2 mm to avoid partial volume effects at the edges. Only the R-R projections of the R-R-PD space are shown. 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.
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pone-0111688-g005: Multi-parametrical representation of tissue parameters in regions of interest (ROIs).The figure shows differences in R and R values between Multiple Sclerosis (MS) patients and the reference group for three separate cerebral ROIs. A) Caudate nucleus; B) Thalamus; C) Total white matter. The ROIs were all reduced with 2 mm to avoid partial volume effects at the edges. Only the R-R projections of the R-R-PD space are shown. 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.

Mentions: In Figure 5, the parametric representation in –R space is visualized for three selected ROIs: the caudate nucleus, the thalamus, and the total WM. Each ROI was reduced with 2 mm in comparison to the ROIs in the standard atlas to avoid large influence of partial volume at the ROI edges. Since the multi-parametric representations in Figure 5 contained a far lower number of included voxels compared to the representations in Figure 4, the number of bins was chosen to 50 rather than 200 as in Figure 4.


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 regions of interest (ROIs).The figure shows differences in R and R values between Multiple Sclerosis (MS) patients and the reference group for three separate cerebral ROIs. A) Caudate nucleus; B) Thalamus; C) Total white matter. The ROIs were all reduced with 2 mm to avoid partial volume effects at the edges. Only the R-R projections of the R-R-PD space are shown. 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.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0111688-g005: Multi-parametrical representation of tissue parameters in regions of interest (ROIs).The figure shows differences in R and R values between Multiple Sclerosis (MS) patients and the reference group for three separate cerebral ROIs. A) Caudate nucleus; B) Thalamus; C) Total white matter. The ROIs were all reduced with 2 mm to avoid partial volume effects at the edges. Only the R-R projections of the R-R-PD space are shown. 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.
Mentions: In Figure 5, the parametric representation in –R space is visualized for three selected ROIs: the caudate nucleus, the thalamus, and the total WM. Each ROI was reduced with 2 mm in comparison to the ROIs in the standard atlas to avoid large influence of partial volume at the ROI edges. Since the multi-parametric representations in Figure 5 contained a far lower number of included voxels compared to the representations in Figure 4, the number of bins was chosen to 50 rather than 200 as in Figure 4.

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