Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging

Abstract

A computerized system for processing spin-echo magnetic resonance (MR) imaging data was implemented to estimate whole brain (gray and white matter) and cerebrospinal fluid volumes and to display three-dimensional surface reconstructions of specified tissue classes. The techniques were evaluated by assessing the radiometric variability of MR volume data and by comparing automated and manual procedures for measuring tissue volumes. Results showed (a) the homogeneity of the MR data and (b) that automated techniques were consistently superior to manual techniques. Both techniques, however, were affected by the complexity of the structure, with simpler structures (eg, the intracranial cavity) showing less variability and better spatial correlation of segmentation results between raters. Moreover, the automated techniques were completed for whole brain in a fraction of the time required to complete the equivalent segmentation manually. Additional evaluations included interrater reliability and an evaluation that included longitudinal measurement, in which one subject was imaged sequentially 24 times, with reliability computed from data collected by three raters over 1 year. Results showed good reliability for the automated segmentation procedures.

A fully automated method for quantifying and localizing white matter hyperintensities on MR images

“…Unlike different visual grading systems, it is very easy to compare the WMH findings from this method across different centers. The method relies on the properties of subject's own FLAIR image such as the intensity distribution of WMHs, the connectivity and the diffusivity of the WMHs for the WMH segmentation, which does not rely on any training dataset as do some of the reviewed methods (Kikinis, et al 1992;Swartz, et al 2002;Anbeek, et al 2004b;Anbeek, et al 2004a).…”

“…For example, K-Nearest Neighbor (KNN) classification method was used to automatically or semi-automatically label the T2-weighted MR brain images as gray matter, CSF and white matter lesions (Kikinis, et al 1992;Swartz, et al 2002;Anbeek, et al 2004b;Anbeek, et al 2004a). In this method, the classification of an image voxel from a new patient relies on the voxel intensities and spatial information of a previously manually classified training set.…”

A Comparison of Brain and Serum Pharmacokinetics of R-Fluoxetine and Racemic Fluoxetine: A 19-F MRS Study

“…The volume of each subject's brain was estimated from analysis of the twodimensional axial slices of the structural scans using Cine software (Kikinis et al, 1992) running on a Sparc2 workstation (Sun Microsystems, Mountainview, CA). The area of brain calculated from each slice was then multiplied by the slice thickness and the thickness of the skipped region between slices.…”

Serial registration of intraoperative MR images of the brain

“…This means we can use segmentation approaches that are robust and accurate but are time consuming and hence impractical to use in the operating room. In our laboratory, preoperative data is segmented with a variety of manual (Gering et al, 1999), semi-automated (Kikinis et al, 1992) or automated (Warfield et al, 1995Kaus et al, 2000) approaches. We select the most robust and accurate approach available for a given clinical application.…”