3D MPRAGE improves classification of cortical lesions in multiple sclerosis.

BACKGROUND: Gray matter lesions are known to be common in multiple sclerosis (MS) and are suspected to play an important role in disease progression and clinical disability. A combination of magnetic resonance imaging (MRI) techniques, double-inversion recovery (DIR), and phase-sensitive inversion recovery (PSIR), has been used for detection and classification of cortical lesions. This study shows that high-resolution three-dimensional (3D) magnetization-prepared rapid acquisition with gradient echo (MPRAGE) improves the classification of cortical lesions by allowing more accurate anatomic localization of lesion morphology. METHODS: 11 patients with MS with previously identified cortical lesions were scanned using DIR, PSIR, and 3D MPRAGE. Lesions were identified on DIR and PSIR and classified as purely intracortical or mixed. MPRAGE images were then examined, and lesions were re-classified based on the new information. RESULTS: The high signal-to-noise ratio, fine anatomic detail, and clear gray-white matter tissue contrast seen in the MPRAGE images provided superior delineation of lesion borders and surrounding gray-white matter junction, improving classification accuracy. 119 lesions were identified as either intracortical or mixed on DIR/PSIR. In 89 cases, MPRAGE confirmed the classification by DIR/PSIR. In 30 cases, MPRAGE overturned the original classification. CONCLUSION: Improved classification of cortical lesions was realized by inclusion of high-spatial resolution 3D MPRAGE. This sequence provides unique detail on lesion morphology that is necessary for accurate classification.

Performance tiers: Implementing comparative effectiveness analysis in the health care setting through operations research and spatial methods

This dissertation develops and tests a comparative effectiveness methodology utilizing a novel approach to the application of Data Envelopment Analysis (DEA) in health studies. The concept of performance tiers (PerT) is introduced as terminology to express a relative risk class for individuals within a peer group and the PerT calculation is implemented with operations research (DEA) and spatial algorithms. The analysis results in the discrimination of the individual data observations into a relative risk classification by the DEA-PerT methodology. The performance of two distance measures, kNN (k-nearest neighbor) and Mahalanobis, was subsequently tested to classify new entrants into the appropriate tier. The methods were applied to subject data for the 14 year old cohort in the Project HeartBeat! study.^ The concepts presented herein represent a paradigm shift in the potential for public health applications to identify and respond to individual health status. The resultant classification scheme provides descriptive, and potentially prescriptive, guidance to assess and implement treatments and strategies to improve the delivery and performance of health systems. ^