Noninvasive Imaging in Drug Discovery and Development

  • M. Rudin
  • P. Allegrini
  • N. Beckmann
  • H.-U. Gremlich
  • R. Kneuer
  • D. Laurent
  • M. Rausch
  • M. Stoeckli
Part of the Ernst Schering Research Foundation Workshop book series (SCHERING FOUND, volume 48)


Traditionally, medical imaging is applied to radiological diagnostics, i.e., to characterize a disease phenotype based on morphological or physiological readouts. Criteria determining image quality and, hence, its diagnostic value are spatial resolution and contrast-tonoise ratio (CNR), the ability to discriminate an anatomical structure from its environment. Image contrast is based on the biophysical properties of tissue such as absorption of radiation in X-ray, or proton density and spin relaxation in magnetic resonance imaging (MRI). Structure definition can be enhanced by administration of exogenous contrast agents, which are electron-dense materials for X-ray and para- or superparamagnetic compounds for MRI, the contract enhancement being governed by both the properties of the contrast agent and the tissue microstructure. The dynamic measurement of contrast changes induced by administration of a contrast agent provides information on physiological tissue parameters such as tissue perfusion, vascular permeability, and function of excretory organs. When combining the contrast-enhancing principle (reporter group) with a target-specific carrier moiety (receptor ligand, antibody, cell), specific information on target expression and function can be obtained. Such target-specific or molecular imaging approaches have raised considerable interest both from a diagnostic and therapeutic point-of-view (Rudin and Weissleder 2003). Considering the broad spectrum of applications of modern imaging technologies their increasing impact on drug discovery and development programs is not surprising.


Positron Emission Tomography Noninvasive Image Single Photon Emission Computer Tomography NIRF Imaging Positron Emission Tomography Ligand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • M. Rudin
  • P. Allegrini
  • N. Beckmann
  • H.-U. Gremlich
  • R. Kneuer
  • D. Laurent
  • M. Rausch
  • M. Stoeckli

There are no affiliations available

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