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Application of Spectral Decomposition Analysis to In Vivo Quantification of Aluminum / In Vivo Quantification of Aluminum

Aluminum is a non-essential trace element that accumulates in human bone tissue (Nayak, 2002). Its toxic effects are cumulative and result in painful forms of renal osteodystrophy, most notably a dynamic bone disease and osteomalacia, but also other forms of disease (Yokel, 2001; Cannata-Andia, 2002). Presently, histological tests of bone biopsies are the only approach for the diagnosis of aluminum-related pathologies (Malluche, 2002). Neutron Activation Analysis was proposed as an alternative method for quantifying aluminum. The Trace Element Group at McMaster University has developed an in vivo procedure for detecting aluminum levels in the bones of the hand, exploiting an accelerator-based approach. A minimum detectable limit (MDL) of 1.14mg of aluminum could be distinguished for a local dose to the hand of 48mSv (Pejovic-Milic, 2001). For the procedure to be clinically effective, the MDL should be comparable to the levels normally contained in healthy subjects (0.3-0.4 mg AI). Further refining of the method is therefore necessary. This dissertation presents an improved algorithm for data analysis, based on Spectral Decomposition. Following phantom measurements, a new MDL of(0.7±0.1)mg AI was reached for a local dose of (20±1)mSv, representing an improvement by a factor of 1.60±0.04. In addition, a time-dependent variant of this algorithm was proposed.
The study also addresses the feasibility of a new data acquisition technique, the electronic rejection of the coincident events detected by the Nai(Tl) system. It is expected that the application of this technique, together with Spectral Decomposition Analysis, would provide an acceptable MDL for the method to be valuable in a clinical setting. / Thesis / Master of Science (MS)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24174
Date09 1900
CreatorsDaria, Cosma
ContributorsPrestwich, William, Medical Physics
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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