4.3 Article Proceedings Paper

Physiologic characterisation of glioblastoma multiforme using MRI-based hypoxia mapping, chemical shift imaging, perfusion and diffusion maps

期刊

JOURNAL OF CLINICAL NEUROSCIENCE
卷 13, 期 8, 页码 811-817

出版社

CHURCHILL LIVINGSTONE
DOI: 10.1016/j.jocn.2005.12.025

关键词

glioblastoma multiforme; MRI; hypoxia; CSI; diffusion; perfusion

资金

  1. NIBIB NIH HHS [R01 EB00448-02] Funding Source: Medline
  2. NINDS NIH HHS [1F31NS046168-01A1] Funding Source: Medline

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Purpose: A multi parametric, physiologic MRI approach was considered to more completely characterise biopsy-confirmed glioblastoma multiforme (GBM). Chemical shift imaging (CSI) supplied biochemical information in metabolite ratios, while perfusion images provided data on presumed vascularity from regional cerebral blood volume (rCBV) and permeability maps. Diffusion-weighted images were reduced to apparent diffusion coefficient (ADC) maps to evaluate cellularity, and blood oxygen level-dependent imaging was used to create maps of putative hypoxic regions. Methods: Six post-treatment GBM patients were scanned at 3-month intervals until recurrence was suggested by conventional MRI parameters., yielding 20 scans for consideration. The percentage of extreme values in each technique that overlapped with other parameters was measured and compared across hemispheres to assess utility. Results: We found significantly better performance in selecting the diseased hemisphere for overall percent overlap when compared to voxel counts from individual thresholded parameter maps. Parameters were selected on the basis of highest overlap, and corresponding composite overlap maps show increased specificity to likely recurrent regions by reducing the number of falsely positive voxels, and offer insight into relationships between various parameters. Conclusion: In a pilot group of patients, percent overlap appears to be sensitive to recurrent disease. When used to combine multiple parameters, voxels containing overlap can specifically target probable recurrent areas. (c) 2006 Published by Elsevier Ltd.

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