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Chapter 23 - Diffusion MR imaging in adult neoplasia
- from Section 3 - Adult neoplasia
- Edited by Jonathan H. Gillard, University of Cambridge, Adam D. Waldman, Imperial College London, Peter B. Barker, The Johns Hopkins University School of Medicine
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- Book:
- Clinical MR Neuroimaging
- Published online:
- 05 March 2013
- Print publication:
- 26 November 2009, pp 321-340
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- Chapter
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Summary
Introduction
Diffusion-weighted MRI (DWI) is increasingly being incorporated into standard clinical evaluation of brain tumors. Driven by compelling results from experimental animal models, an increasing number of clinical research groups have begun to evaluate the potential of DWI to improve clinical management of brain tumors. The principles of DWI are covered in Ch. 4. This chapter contains an overview of the current developments in DWI as applied to adult neuro-oncology. Specific examples have been drawn from the literature to demonstrate how DWI can provide unique and valuable information with regard to diagnosis, treatment planning, and therapeutic monitoring of brain neoplasia (Table 23.1).
The current clinical value of conventional MRI resides in its ability to demonstrate gross tumor morphology and temporal changes non-invasively. Conventional MRI exploits a variety of tissue properties that allow the neuro-oncologist/neuroradiologist to assess gross tumor extent on the resultant contrasts, such as T2-weighted (Fig. 23.1), gadolinium (Gd)-enhanced T1-weighted and diffusion-weighted (Fig. 23.1) images. The typical radiological assessment is somewhat interpretive and based on the spatial extent and location of abnormal tissue contrast. The actual image contrast values are rarely quantified, as these are usually scaled arbitrarily and do not have a simple relationship to microscopic tissue properties. There is significant clinical potential for MR techniques that provide additional quantitative or semiquantitative functional, structural, or molecular information related to tumor biology and physiology. Such information may be derived from MR signals that reflect perfusion dynamics, oxygenation levels, biochemistry/metabolism, cellularity, and levels of gene expression.