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.

Restricted diffusion in brain abscess is assumed to be due to a combination of inflammatory cells, necrotic debris, viscosity, and macromolecules present in the pus. We performed diffusion-weighted imaging (DWI) on 41 patients with proven brain abscesses (36 pyogenic and five tuberculous), and correlated the apparent diffusion coefficient (ADC) from the abscess cavity with viable cell density, viscosity, and extracellular-protein content quantified from the pus. On the basis of the correlation between cell density and ADC in animal tumor models and human tumors in the literature, we assumed that the restricted ADC represents the cellular portion in the abscess cavity. We calculated restricted and unrestricted lesion volumes, and modeled cell density over the restricted area with viable cell density per mm 3 obtained from the pus. The mean restricted ADC in the cavity (0.65 ؎ 0.01 ؋ 10 -3 mm 2 /s) correlated inversely with restricted cell density in both the pyogenic (r ‫؍‬ -0.90, P ‫؍‬ <0.05) and tuberculous (0.60 ؎ 0.04 ؋ 10 -3 mm 2 /s, r ‫؍‬ -0.94, P ‫؍‬ <0.05) abscesses. We conclude that viable cell density is the main biological parameter responsible for restricted diffusion in brain abscess, and it is not influenced by the etiological agents responsible for its causation. Magn Reson Med 54:878 -885, 2005.

LTKM with local AIF provides more accurate estimation of physiological parameters and improves discrimination between low-grade and high-grade gliomas as compared with GTKM.

Relative cerebral blood volume is a measure of angiogenesis in the cellular fraction of the brain tuberculoma. This information may be of value in predicting the therapeutic response in future.

DTI-derived FA may be of value in the noninvasive assessment of meningeal inflammatory activity and treatment response in neonates.

Purpose: To modify the generalized tracer kinetic model (GTKM) by introducing an additional tissue uptake leakage compartment in extracellular extravascular space (LTKM). In addition, an implicit determination of voxel‐wise local arterial input function (AIF) Cp(t) was performed to see whether these changes help in better discrimination between low‐ and high‐grade glioma using dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI). Materials and Methods: The modified model (LTKM) was explored and fitted to the concentration–time curve C(t) of each voxel, in which the local AIF Cp(t) could be estimated by a time invariant convolution approximation based on a separately measured global AIF Ca(t). A comparative study of tracer kinetic analysis was performed on 184 glioma patients using DCE‐MRI data on 1.5T and 3T MRI systems. Results: The LTKM analysis provided more accurate pharmacokinetic parameters as evidenced by their relative constancy with respect to the length of concentration–time curve used. In addition, LTKM with local AIF resulted in improved discrimination between low‐grade and high‐grade gliomas. Conclusion: LTKM with local AIF provides more accurate estimation of physiological parameters and improves discrimination between low‐grade and high‐grade gliomas as compared with GTKM. J. Magn. Reson. Imaging 2013;38:677–688. © 2013 Wiley Periodicals, Inc.

While there is growing evidence that DCE-MRI may provide insights about the response of patients to therapies, there is a lack of standardized software quantification tools, resulting in variability in reported Ktrans values across different studies and limiting its utility in clinical applications. We have designed and launched the Open Science Initiative for Perfusion Imaging (OSIPI)-DCE challenge to provide recommended and benchmarked analysis tools for Ktrans estimation in the brain, by evaluating and comparing DCE software tools in terms of accuracy, repeatability, and reproducibility. Here, we report on the preliminary results of this challenge.