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Improving the reliability of obtaining tumor hemodynamic parameters in the presence of contrast agent extravasation. Magn Reson Med 2005 Jun;53(6):1307-16 PMID: 15906288

Abstract

A new approach to improve the reliability of dynamic susceptibility contrast MRI for the evaluation of brain tumor hemodynamics in the presence of contrast agent extravasation is described. This model-based technique simultaneously estimates the voxel-wise tumor residue function and the temporal extravascular T(1) changes following contrast agent leakage. With these estimates the model corrects the measured MRI signal, which is then used to calculate tumor hemodynamic parameters. The feasibility of this technique is demonstrated with computer simulations that cover a wide range of hemodynamic conditions and by application to eight tumor-bearing rats. The simulations demonstrate that the corrected hemodynamic parameters precisely matched the actual values with a maximum percentage error of 4.2% compared to 68.6% for the uncorrected parameters. The corrected parameters are also essentially independent of the tumor hemodynamic state and degree of contrast extravasation. Consistent with these improvements, significant differences between corrected and uncorrected parameters, calculated from a gradient-echo sequence, are shown in a rat 9L gliosarcoma model. This method combined with the hemodynamic parameters derived from GE and SE sequences shows promise as a new tool to evaluate tumor angiogenesis and its therapy.

Author List

Quarles CC, Ward BD, Schmainda KM

Author

Kathleen M. Schmainda PhD Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Animals
Blood Volume Determination
Brain Neoplasms
Computer Simulation
Contrast Media
Extravasation of Diagnostic and Therapeutic Materials
Glioma
Hemodynamics
Magnetic Resonance Imaging
Male
Neovascularization, Pathologic
Rats
Rats, Inbred F344
Regional Blood Flow



View this publication's entry at the Pubmed website PMID: 15906288
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