Imaging the Lymph Nodes: CT, MRI, and PET View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

E. E. Coche , T. Duprez , M. Lonneux

ABSTRACT

The detection of malignant lymph nodes remains a major challenge in spite of the marked improvement in currently available imaging modalities (Van den Brekel and Castelijns 1999). Cross-sectional techniques capable of 3D reconstruction such as computed tomography (CT) and magnetic resonance imaging (MRI) are now in standard use for the radiological staging of nodal status. However in spite of improved technology regarding the speed of image acquisition, spatial resolution, 3D image post-processing and even tissue contrast modulation, their capacity for tissue characterization is limited (Carrington 1998). Integrating other modalities to obtain additional information on e.g. the vascular architecture of the nodes (color Doppler ultrasound), or on metabolic indexes such as glucose uptake [positron emission tomography (PET)] is essential to achieve increased sensitivity and specificity of the pre- and post-treatment nodal work-up of patients with neoplastic disease (Jabour et al. 1993; Moritz et al. 2000). Vast research areas are being explored in the field of MRI on intrinsic tissue parameter measurements and organ-targeted contrast agents (Anzai and Prince 1997; Dooms et al. 1985; Hoffman et al. 2000). The combined anatomical (CT/MR) and metabolic (PET) data provided in a single view through image fusion is being increasingly used, as this technological refinement has been reported to provide enhanced sensitivity and specificity thresholds in malignant lymph node depiction (Wahl et al. 1994). More... »

PAGES

37-67

References to SciGraph publications

Book

TITLE

Clinical Target Volumes in Conformal and Intensity Modulated Radiation Therapy

ISBN

978-3-642-07463-9
978-3-662-06270-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-662-06270-8_2

DOI

http://dx.doi.org/10.1007/978-3-662-06270-8_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029554684


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