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The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

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Magnetic Resonance-guided Focused UltraSound (MRgFUS) offers an incisionless approach to treat essential tremor (ET). Due to lack of evident internal anatomy on traditional structural imaging, indirect targeting must still be used to localize the lesion. Here, we investigate the potential predictive value of probabilistic tractography guided thalamic targeting by defining how tractography-defined targets, lesion size and location, and clinical outcomes interrelate.MR imaging and clinical outcomes from 12 ET patients that underwent MRgFUS thalamotomy in a pilot study at the University of Virginia were evaluated in this analysis. FSL was used to evaluate each patient's voxel-wise thalamic connectivity with FreeSurfer generated pre- and post-central gyrus targets, to generate thalamic target maps. Using Receiver Operating Characteristic curves, the overlap between these thalamic target maps and the MRgFUS lesion was systematically evaluated relative to clinical outcome. To further define the connectivity characteristics of effective MRgFUS thalamotomy lesions, we evaluated whole brain probabilistic tractography of lesions (using post-treatment imaging to define the lesion pre-treatment diffusion tensor MRI). The structural connectivity difference was explored between subjects with the best clinical outcome relative to all others.Ten of twelve patients presented high percentage of overlapping between connectivity-based thalamic segmentation maps and lesion area. The improvement of clinical score was predicted (AUC: 0.80) using the volume of intersection between the thalamic target (precentral gyrus) map and MRgFUS induced lesion as feature. The main structural differences between those with different magnitudes of response were observed in connectivity to the pre- and post-central gyri and brainstem/cerebellum.MRgFUS thalamotomy lesions characterized by strong structural connectivity to precentral gyrus demonstrated better responses in a cohort of patients treated with MRgFUS for ET. The intersection between lesion and thalamic-connectivity maps to motor - sensory targets proved to be effective in predicting the response to the therapy. These imaging techniques can be used to increase the efficacy and consistency of outcomes with MRgFUS and potentially shorten treatment times by identifying optimal targets in advance of treatment.

Purpose. To evaluate the diagnostic value of 3T 1H-MRS in grading cerebral gliomas using short and long echo times. Methods. 1H-MRS was performed on 71 patients with untreated cerebral gliomas. Metabolite ratios of NAA/Cr, Cho/Cr, Cho/NAA, and mI/Cr were calculated for short and long TE and compared between low and high grade gliomas. Lipids were qualitatively evaluated. ROC analysis was performed to obtain the cut-off values for the metabolic ratios presenting statistical difference between the two glioma grades. Results. Intratumoral Cho/Cr at both TEs and long TE Cho/NAA were significantly different between low and high grade gliomas. Peritumoral NAA/Cr of both TEs, as well as long TE Cho/Cr and Cho/NAA ratios, significantly differentiated the two tumor grades. Diagnostic sensitivity of peritumoral short TE NAA/Cr proved to be superior over the other metabolic ratios, whereas intratumoral short TE Cho/Cr reached the highest levels of specificity and accuracy. Overall, short TE 1H-MRS reached higher total sensitivity in predicting glioma grade, over long TE. Conclusion. An advantage was found in using short TE over long TE 1H-MRS in the discrimination of low versus high grade gliomas. Moreover, the results suggested that the peritumoral area of gliomas may be more valuable in predicting glioma grade than using only the intratumoral area.

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FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.