Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis.

BACKGROUND: Cardiovascular (CV) risk factors have been associated with changes in clinical outcomes in patients with multiple sclerosis (MS). OBJECTIVES: To investigate the frequency of CV risks in patients with MS and their association with MRI outcomes. METHODS: In a prospective study, 326 patients with relapsing-remitting MS and 163 patients with progressive MS, 61 patients with clinically isolated syndrome (CIS) and 175 healthy controls (HCs) were screened for CV risks and scanned on a 3T MRI scanner. Examined CV risks included hypertension, heart disease, smoking, overweight/obesity and type 1 diabetes. MRI measures assessed lesion volumes (LVs) and brain atrophy. Association between individual or multiple CV risks and MRI outcomes was examined adjusting for age, sex, race, disease duration and treatment status. RESULTS: Patients with MS showed increased frequency of smoking (51.7% vs 36.5%, p = 0.001) and hypertension (33.9% vs 24.7%, p=0.035) compared with HCs. In total, 49.9% of patients with MS and 36% of HCs showed ≥ 2 CV risks (p = 0.003), while the frequency of ≥ 3 CV risks was 18.8% in the MS group and 8.6% in the HCs group (p = 0.002). In patients with MS, hypertension and heart disease were associated with decreased grey matter (GM) and cortical volumes (p < 0.05), while overweight/obesity was associated with increased T1-LV (p < 0.39) and smoking with decreased whole brain volume (p = 0.049). Increased lateral ventricle volume was associated with heart disease (p = 0.029) in CIS. CONCLUSIONS: Patients with MS with one or more CV risks showed increased lesion burden and more advanced brain atrophy.

Application of hidden Markov random field approach for quantification of perfusion/diffusion mismatch in acute ischemic stroke.

The perfusion/diffusion 'mismatch model' in acute ischemic stroke provides the potential to more accurately understand the consequences of thrombolytic therapy on an individual patient basis. Few methods exist to quantify mismatch extent (ischemic penumbra) and none have shown a robust ability to predict infarcted tissue outcome. Hidden Markov random field (HMRF) approaches have been used successfully in many other applications. The aim of the study was to develop a method for rapid and reliable identification and quantification of perfusion/diffusion mismatch using an HMRF approach. An HMRF model was used in combination with automated contralateral identification to segment normal tissue from non-infarcted tissue with perfusion abnormality. The infarct was used as a seed point to initialize segmentation, along with the contralateral mirror tissue. The two seeds were then allowed to compete for ownership of all unclassified tissue. In addition, a novel method was presented for quantifying tissue salvageability by weighting the volume with the degree of hypoperfusion, allowing the penumbra voxels to contribute unequal potential damage estimates. Simulated and in vivo datasets were processed and compared with results from a conventional thresholding approach. Both simulated and in vivo experiments demonstrated a dramatic improvement in accuracy with the proposed technique. For the simulated dataset, the mean absolute error decreased from 171.9% with conventional thresholding to 2.9% for the delay-weighted HMRF approach. For the in vivo dataset, the mean absolute error decreased from 564.6% for thresholding to 34.2% for the delay-weighted HMRF approach. The described method represents a significant improvement over thresholding techniques.