Multi-parametric representation of voxel-based quantitative magnetic resonance imaging.
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.
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.
Maria
M
Engström
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; SyntheticMR AB, Linköping, Sweden.
Jan B M
JB
Warntjes
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Division of Radiation Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Anders
A
Tisell
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Division of Neurology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
Anne-Marie
AM
Landtblom
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; Division of Radiation Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Peter
P
Lundberg
D001931:Brain Mapping / Q000379:methods*; D018450:Disease Progression; D005260:Female; D005625:Frontal Lobe / Q000000981:diagnostic imaging / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000000981:diagnostic imaging / Q000473:pathology*; D011859:Radiography; D066127:White Matter / Q000000981:diagnostic imaging / Q000473:pathology*