Neuroimage. 2003 Oct;20(2):1193-209
Meier DS, Guttmann CR.
Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue, RFB 396, 02115, Boston, MA, USA
In progressive neurological disorders, such as multiple sclerosis (MS), magnetic resonance imaging (MRI) follow-up is used to monitor disease activity and progression and to understand the underlying pathogenic mechanisms.
This article presents image postprocessing methods and validation for integrating multiple serial MRI scans into a spatiotemporal volume for direct quantitative evaluation of the temporal intensity profiles.
This temporal intensity signal and its dynamics have thus far not been exploited in the study of MS pathogenesis and the search for MRI surrogates of disease activity and progression.
The integration into a four-dimensional data set comprises stages of tissue classification, followed by spatial and intensity normalization and partial volume filtering.
Spatial normalization corrects for variations in head positioning and distortion artifacts via fully automated intensity-based registration algorithms, both rigid and nonrigid.
Intensity normalization includes separate stages of correcting intra- and interscan variations based on the prior tissue class segmentation.
Different approaches to image registration, partial volume correction, and intensity normalization were validated and compared.
Validation included a scan-rescan experiment as well as a natural-history study on MS patients, imaged in weekly to monthly intervals over a 1-year follow-up.
Significant error reduction was observed by applying tissue-specific intensity normalization and partial volume filtering.
Example temporal profiles within evolving multiple sclerosis lesions are presented.
An overall residual signal variance of 1.4% +/- 0.5% was observed across multiple subjects and time points, indicating an overall sensitivity of 3% (for axial dual echo images with 3-mm slice thickness) for longitudinal study of signal dynamics from serial brain MRI.