It is concluded that DTI abnormalities in the regions of CC were more in patients with moderate TBI compared to mild TBI and this was associated with relatively poor neuropsychological outcome 6 months post-injury.

Purpose: To estimate precontrast tissue parameter (T 10 ) using fast spin echo (FSE) and to quantify physiological and hemodynamic parameters with leakage correction using T 1 -weighted dynamic contrast-enhanced (DCE) perfusion imaging. Materials and Methods:Voxel-wise T 10 computation was performed followed by the analysis of T 1 -weighted DCE perfusion data for the conversion of signal intensity time curve to concentration time curve, estimation of hemodynamic and physiological perfusion indices, and a method for leakage correction. Validations of accuracy of the computations have also been carried out. Results:The computed T 10 and hemodynamic perfusion indices in normal white and gray matter were in good agreement with the literature values. Physiological perfusion indices in these regions were found negligible, validating computations. Cerebral blood volume (CBV) values change negligibly over the length of concentration time curve in white matter, gray matter, and lesion (CBV corrected ), while CBV uncorrected (lesion) shows linear increase over time.Conclusion: T 1 -weighted DCE perfusion data along with FSE-based T 1 estimation can be used for an accurate estimation of hemodynamic and physiological perfusion indices.

We conclude that white matter tracts from both the somatosensory and the motor cortex play an important role in the pathophysiology of motor disability in patients with CP.

Background and Purpose Accurate grading of cerebral glioma using conventional structural imaging techniques remains challenging due to the relatively poor sensitivity and specificity of these methods. The purpose of this study was to evaluate the relative sensitivity and specificity of structural MRI and MR measurements of perfusion, diffusion, and spectroscopic parameters for glioma grading. A secondary objective was to evaluate a whole-brain MR spectroscopic imaging method for evaluation of brain tumors. Materials and Methods Fifty six patients with radiologically suspected untreated glioma were studied with T1- and T2-weighted MR imaging, DCE-MR imaging, DTI, and volumetric whole-brain MR spectroscopic imaging. ROC analysis was performed using the relative CBV, ADC, FA, and multiple spectroscopic parameters to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, PPV, and NPV for identifying high-grade gliomas. Logistic regression was performed to analyze all the parameters together. Results The relative CBV individually classified glioma as low and high grade with a sensitivity and specificity of 100% and 88% respectively based on a threshold value of 3.34. On combining all parameters under consideration, the classification was achieved with 2% error and sensitivity and specificity of 100% and 96% respectively. Conclusion Individually, CBV measurement provides the greatest diagnostic performance for predicting glioma grade; however, the most accurate classification can be achieved by combining all of the imaging parameters. The whole-brain MR spectroscopic imaging method provided data from of a large fraction of the tumor volumes.

Purpose: To develop a methodology for improved estimation of bolus arrival time (BAT) and arterial input function (AIF) which are prerequisites for tracer kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and to verify the applicability of the same in the case of intracranial lesions (brain tumor and tuberculoma). Materials and Methods:A continuous piecewise linear (PL) model (with BAT as one of the free parameters) is proposed for concentration time curve C(t) in T 1 -weighted DCE-MRI. The resulting improved procedure suggested for automatic extraction of AIF is compared with earlier methods. The accuracy of BAT and other estimated parameters is tested over simulated as well as experimental data. Results:The proposed PL model provides a good approximation of C(t) trends of interest and fit parameters show their significance in a better understanding and classification of different tissues. BAT was correctly estimated. The automatic and robust estimation of AIF obtained using the proposed methodology also corrects for partial volume effects. The accuracy of tracer kinetic analysis is improved and the proposed methodology also reduces the time complexity of the computations. Conclusion:The PL model parameters along with AIF measured by the proposed procedure can be used for an improved tracer kinetic analysis of DCE-MRI data. DYNAMIC CONTRAST-ENHANCED (DCE) magnetic resonance imaging (MRI) provides noninvasive methods for studying the vasculature of different tissues/lesions based on their response to the passage of intravenously injected contrast agent. The study of tissue vasculature is essential for understanding a wide range of disease processes. DCE-MRI data results in a signal intensity time curve, S(t), at individual voxels that can be converted into a concentration time curve, C(t) (1-3). The shape of C(t) is an important criterion for differentiation/characterization of different tissues. In the case of an intact blood brain barrier (BBB) the contrast remains intravascular. BBB breakdown results in leakage of contrast into the extracellular extravascular space, which leads to the enhancement of contrast (1). The shape of the curve in these tissues is different from that in normal tissues. A set of well-recognized mathematical models is available for T 1 -weighted (W) DCE-MRI data that can provide useful information on tissue vasculature (1-4). Accurate and precise estimation of the indices of these models is essential for understanding tissue vasculature that may help in the diagnosis of different pathologies. With the presently available resolution and the number of feasible scans the understanding and the accuracy of the resulting C(t) is still not fully resolved, as issues like compartmentalization of a voxel, effect of water exchange between different compartments of the same voxel, partial volume effect (PVE), inflow of fresh spins (time of flight), etc, continue to be gray areas. Continuous efforts are being made to achieve accurate and precise quantitation of ...

DCE-MRI may be used to differentiate between high-grade and low-grade brain tumors non-invasively, which may be helpful in appropriate treatment planning and management of these patients. The correlation of its indices with immunohistochemical markers suggests that this imaging technique is useful in tissue characterization of gliomas.

Perilesional inflammation, which varies from symptomatic to asymptomatic subjects, can be quantified using DCE-MRI in calcified cysticercosis and may help distinguish these 2 groups with similar imaging findings. The observed increase in k(ep) with serum MMP-9 levels suggests that the former may serve as a biomarker of MMP-9 levels in these subjects. The significant MMP-9 (R279Q) gene polymorphism in symptomatic subjects might explain the differences in the observed DCE-MRI indices between symptomatic and asymptomatic subjects.

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