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Automatic segmentation of gadolinium-enhanced multiple sclerosis lesions
1Department of Radiology, University of Texas Medical School at Houston, 77030, USA.
Abstract:
Automatic detection and quantitation of contrast-enhanced lesions on MRI is expected to be useful in characterizing the disease state in multiple sclerosis (MS). The enhancing structures such as cerebral vasculature and regions with no blood-brain barrier complicate automated analysis of lesion enhancement. A pulse sequence that incorporates both stationary and marching saturation bands and gradient dephasing is described for suppressing the enhancements within the cerebral vasculature. A postprocessing technique based on automatic image segmentation is implemented for eliminating enhancing structures such as choroid plexus. All MS lesions larger than 5 mm3 have been successfully identified. The automated analysis produced no false-positives or false-negative lesions above this volume in 13 patients with MS who were evaluated using the acquisition and evaluation techniques described.
Insights
This study presents an automated method for detecting multiple sclerosis (MS) lesions on MRI scans. The technique successfully identifies MS lesions larger than 5 mm3 without false positives or negatives.
Area of Science:
- Medical Imaging
- Neurology
- Biomedical Engineering
Background:
- Accurate characterization of disease state in multiple sclerosis (MS) relies on detecting contrast-enhanced lesions on MRI.
- Automated analysis of lesion enhancement is complicated by enhancing structures like cerebral vasculature and blood-brain barrier disruptions.
Purpose of the Study:
- To develop and evaluate an automated method for detecting and quantifying contrast-enhanced lesions in multiple sclerosis (MS) patients using MRI.
- To overcome challenges in automated analysis caused by non-lesion enhancing structures.
Main Methods:
- A novel MRI pulse sequence incorporating stationary and marching saturation bands with gradient dephasing was used to suppress vascular enhancement.
- Automatic image segmentation was employed as a postprocessing technique to eliminate non-lesion enhancing structures, such as the choroid plexus.
- The developed technique was evaluated on 13 patients with MS.
Main Results:
- The automated method successfully identified all multiple sclerosis (MS) lesions larger than 5 mm3.
- No false-positive or false-negative lesions exceeding 5 mm3 were detected in the evaluated patients.
- The technique effectively suppressed enhancements from cerebral vasculature and eliminated structures like the choroid plexus.
Conclusions:
- The described automated MRI acquisition and postprocessing techniques are effective for accurate detection and quantitation of contrast-enhanced MS lesions.
- This method shows promise for reliable disease state characterization in multiple sclerosis (MS) by overcoming common image analysis artifacts.