DocumentCode :
385378
Title :
Neurological applications of quantitative diffusion tensor imaging
Author :
Ulug, Aziz M.
Author_Institution :
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
Volume :
2
fYear :
2002
fDate :
2002
Firstpage :
1171
Abstract :
Diffusion tensor imaging an emerging MR imaging modality which has started to be used in clinical setting. Here, we are describing use of quantitative DTI in diagnosis of upper motor neuron diseases: amyotropic lateral sclerosis and primary lateral sclerosis. Upper motor neuron diseases are difficult to diagnose using imaging means. Currently, diagnosis is usually one of exclusion requiring careful clinical examination and series of tests to rule out diseases that may mimic upper motor neuron diseases. Upper motor neuron diseases present with progressive degeneration of corticospinal tract. The relentless disease progression ultimately leads to severe spastic spinobulbar paresis. Since diffusion tensor imaging is sensitive to the changes in tissue microstructure, it is an ideal tool to use in diagnosis of upper motor neuron diseases where the damage in corticospinal tract is microscopic.
Keywords :
biodiffusion; biomedical MRI; diseases; neurophysiology; tensors; corticospinal tract damage; magnetic resonance imaging; medical diagnostic imaging; progressive degeneration; relentless disease progression; severe spastic spinobulbar paresis; tissue microstructure changes; upper motor neuron diseases diagnosis; Anisotropic magnetoresistance; Biomedical imaging; Degenerative diseases; Diffusion tensor imaging; Neurons; Pixel; Spinal cord; Tensile stress; Testing; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
ISSN :
1094-687X
Print_ISBN :
0-7803-7612-9
Type :
conf
DOI :
10.1109/IEMBS.2002.1106331
Filename :
1106331
Link To Document :
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