Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616917
Title: Blind source separation in dynamic contrast enhanced magnetic resonance imaging renography
Author: Kiani, Saeed
ISNI:       0000 0004 5348 1645
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2014
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Abstract:
Dynamic contrast~enhanced magnetic resonance imaging (DCE-MRI) renography is a desirable kidney assessment methodology owing to the lack of ionizing radiation in MRI and its capability of producing high-resolution anatomical image data as well as physiological data. DCE-MRI renography emerged with the view to provide a minimally invasive framework to quickly and accurately assess kidney function, for example, to measure glomerular filtration rate (GFR). However, despite considerable developments, it is not yet considered a robust technique of renal assessment. This is due to a number of confounding factors ranging from · optimization of data acquisition parameters to data post-processing challenges such as organ motion (mainly due to breathing), segmentation, partial volume (PV) effect (a signal mixing phenomenon) and tracer kinetic modelling. Prior works including registration-based motion correction techniques, semi-automatic segmentation based on similarity measures and a template-based PV correction method have not provided a complete and practical solution. In this work, a blind source separation (BSS) approach based on time-delayed decorrelation and temporal independent component analysis (ICA) was proposed to unmix physiological signals and remove the undesired motion artefacts. To evahtate the technique, test data were constructed using kidney, liver and non- . specific tissue dynamic MR signals. The source signals were correctly identified with small errors and coefficient of determination r2 values of 0.85 - 0.99 between the independent components (ICs) and source signals.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.616917  DOI: Not available
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