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.
To investigate the physiologic profiles within and around MS and NSWMD lesions and their ability to distinguish the two disease states.
MS and NSWMD patients were scanned on a Philips 3T MRI scanner. A total of 143 MS from 23 patients and 105 NSWMD lesions from 13 patients 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 (p<0.0005). BOLD slope was significantly lower in MS- compared to NSWM-lesions (p=0.0006), suggesting decreased metabolic activity in MS lesions. Furthermore, BOLD signal within and around lesions significantly distinguished MS and NSWMD lesions (p=0.0007).
Alternative approaches beyond the evaluation of structural characteristics are needed to improve the specificity of lesion origin. Our 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. In addition, this technique effectively adds to other methods that aim to improve the specificity in identifying the etiology of central nervous system lesions to optimize the quality of medical management provided to the patients we serve.