Multimodality and advanced biomedical imaging for clinical and research applications

Bidaut, Luc (2010) Multimodality and advanced biomedical imaging for clinical and research applications. In: ASME 2010 First Global Congress on NanoEngineering for Medicine and Biology, 7 - 10 February 2010, Houston, Texas, USA.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Driven in particular by the availability of ever more refined imaging modalities - such as faster and higher resolution CT and MR, or hybrid PET/CT and SPECT/CT - and by a marked increase in computing power, advanced biomedical imaging - e.g. through its multidimensional and multimodal paradigms - is taking an ever bigger place in both research and clinical routine, and for both diagnostic applications and therapy management. While human modalities are widely publicized, there is a similar plethora of imaging systems for small animals, which permits relatively fast translation of successful image-based protocols from research evaluation to the clinic. Because of their characteristics and the need for more detailed information about normal vs. diseased tissues, modern modalities produce huge amounts of information and images. The only way to digest these is through advanced paradigms that combine or reduce the complexity of the information so that it can be interpreted by normal human beings. This presentation will introduce techniques and research or clinical applications of advanced imaging for both animals and humans. Copyright © 2010 by ASME.

Additional Information:Conference Code:81282
Keywords:Biomedical imaging, Clinical application, Clinical routine, Computing power, Diagnostic applications, Diseased tissues, Higher resolution, Human being, Image-based, Imaging modality, Multi-modal, Multi-modality, Research evaluation, Small Animal, SPECT/CT, Therapy management, Animals, Health care, Medical imaging, Research, Computerized tomography
Subjects:F Physical Sciences > F350 Medical Physics
Divisions:College of Science > School of Computer Science
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ID Code:24134
Deposited On:07 Apr 2017 11:46

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