Utility of shape evolution and displacement in the classification of chronic multiple sclerosis lesions.

The accurate recognition of multiple sclerosis (MS) lesions is challenged by the high sensitivity and imperfect specificity of MRI. To examine whether longitudinal changes in volume, surface area, 3-dimensional (3D) displacement (i.e. change in lesion position), and 3D deformation (i.e. change in lesion shape) could inform on the origin of supratentorial brain lesions, we prospectively enrolled 23 patients with MS and 11 patients with small vessel disease (SVD) and performed standardized 3-T 3D brain MRI studies. Bayesian linear mixed effects regression models were constructed to evaluate associations between changes in lesion morphology and disease state. A total of 248 MS and 157 SVD lesions were studied. Individual MS lesions demonstrated significant decreases in volume < 3.75mm3 (p = 0.04), greater shifts in 3D displacement by 23.4% with increasing duration between MRI time points (p = 0.007), and greater transitions to a more non-spherical shape (p < 0.0001). If 62.2% of lesions within a given MRI study had a calculated theoretical radius > 2.49 based on deviation from a perfect 3D sphere, a 92.7% in-sample and 91.2% out-of-sample accuracy was identified for the diagnosis of MS. Longitudinal 3D shape evolution and displacement characteristics may improve lesion classification, adding to MRI techniques aimed at improving lesion specificity.

BOLD signal within and around white matter lesions distinguishes multiple sclerosis and non-specific white matter disease: a three-dimensional approach.

Multiple sclerosis (MS) diagnostic criteria are based upon clinical presentation and presence of white matter hyperintensities on two-dimensional magnetic resonance imaging (MRI) views. Such criteria, however, are prone to false-positive interpretations due to the presence of similar MRI findings in non-specific white matter disease (NSWMD) states such as migraine and microvascular disease. The coexistence of age-related changes has also been recognized in MS patients, and this comorbidity further poses a diagnostic challenge. In this study, we investigated the physiologic profiles within and around MS and NSWMD lesions and their ability to distinguish the two disease states. MS and NSWMD lesions were identified using three-dimensional (3D) T2-FLAIR images and segmented using geodesic active contouring. A dual-echo functional MRI sequence permitted near-simultaneous measurement of blood-oxygen-level-dependent signal (BOLD) and cerebral blood flow (CBF). BOLD and CBF were calculated within lesions and in 3D concentric layers surrounding each lesion. BOLD slope, an indicator of lesion metabolic capacity, was calculated as the change in BOLD from a lesion through its surrounding perimeters. We observed sequential BOLD signal reductions from the lesion towards the perimeters for MS, while no such decreases were observed for NSWMD lesions. BOLD slope was significantly lower in MS compared to NSWM lesions, suggesting decreased metabolic activity in MS lesions. Furthermore, BOLD signal within and around lesions significantly distinguished MS and NSWMD lesions. These results suggest that this technique shows promise for clinical utility in distinguishing NSWMD or MS disease states and identifying NSWMD lesions occurring in MS patients.