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Updated: Aug 30, 2026
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Time-series analysis of MRI intensity patterns in multiple sclerosis
Dominik S Meier1, Charles R G Guttmann
1Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue, RFB 396,Boston, MA, 02115, USA. meier@bwh.harvard.edu
Abstract:
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.
Insights
This study introduces a novel method to analyze serial magnetic resonance imaging (MRI) scans for multiple sclerosis (MS) by creating spatiotemporal volumes. This approach quantizes temporal intensity profiles, offering new insights into MS progression and potential MRI biomarkers.
Area of Science:
- Neurology
- Medical Imaging
- Biophysics
Background:
- Serial magnetic resonance imaging (MRI) is crucial for monitoring progressive neurological disorders like multiple sclerosis (MS).
- Current MRI analysis often overlooks the temporal dynamics of intensity signals within lesions.
- Developing quantitative MRI biomarkers is essential for understanding MS pathogenesis and progression.
Purpose of the Study:
- To present and validate advanced image postprocessing methods for integrating serial MRI scans into spatiotemporal volumes.
- To enable quantitative evaluation of temporal intensity profiles for MS lesion analysis.
- To explore the utility of temporal intensity dynamics as potential MRI surrogates for MS disease activity.
Main Methods:
- Developed a four-dimensional (4D) data integration pipeline including tissue classification, spatial normalization (rigid and nonrigid registration), intensity normalization, and partial volume filtering.
- Validated methods using scan-rescan experiments and a 1-year natural-history study of MS patients with regular imaging intervals.
- Employed automated intensity-based registration algorithms for spatial correction and tissue-specific normalization for intensity correction.
Main Results:
- Significant error reduction was achieved through tissue-specific intensity normalization and partial volume filtering.
- Demonstrated the ability to extract example temporal intensity profiles from evolving MS lesions.
- Achieved an overall residual signal variance of 1.4% +/- 0.5%, indicating high sensitivity for longitudinal signal dynamics.
Conclusions:
- The proposed spatiotemporal MRI analysis method effectively quantifies temporal intensity profiles in serial scans.
- This technique offers enhanced sensitivity for detecting subtle changes in MS lesions over time.
- The validated methodology holds promise for advancing the search for reliable MRI biomarkers in multiple sclerosis research.
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01:24Magnetic Resonance Imaging
01:19Multiple Sclerosis l: Introduction