Signal abnormalities on 1.5 and 3 Tesla brain MRI in multiple sclerosis patients and healthy controls. A morphological and spatial quantitative comparison study.

Previous studies in patients with multiple sclerosis (MS) revealed increased lesion count and volume on 3 T compared to 1.5 T. Morphological and spatial lesion characteristics between 1.5 T and 3 T have not been examined. The aim of this study was to investigate the effect of changing from a 1.5 T to a 3 T MRI scanner on the number, volume and spatial distribution of signal abnormalities (SA) on brain MRI in a sample of MS patients and normal controls (NC), using pair- and voxel-wise comparison procedures. Forty-one (41) MS patients (32 relapsing-remitting and 9 secondary-progressive) and 38 NC were examined on both 1.5 T and 3 T within one week in random order. T2-weighted hyperintensities (T2H) and T1-weighted hypointensities (T1H) were outlined semiautomatically by two operators in a blinded fashion on 1.5 T and 3 T images. Spatial lesion distribution was assessed using T2 and T1 voxel-wise SA probability maps (SAPM). Pair-wise analysis examined the proportion of SA not simultaneously outlined on 1.5 T and 3 T. A posteriori unblinded analysis was conducted to examine the non-overlapping identifications of SA between the 1.5 T and 3 T. For pair-wise T2- and T1-analyses, a higher number and individual volume of SA were detected on 3 T compared to 1.5 T (p<0.0001) in both MS and NC. Logistic regression analysis showed that the likelihood of missing SA on 1.5 T was significantly higher for smaller SA in both MS and NC groups. SA probability map (SAPM) analysis revealed significantly more regionally distinct spatial SA differences on 3 T compared to 1.5 T in both groups (p<0.05); these were most pronounced in the occipital, periventricular and cortical regions for T2H. This study provides important information regarding morphological and spatial differences between data acquired using 1.5 T and 3 T protocols at the two scanner field strengths.

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.