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Title: Data Driven Reconstruction Methods for Dynamic Undersampled MRI
Author: Malik, Shaihan
ISNI:       0000 0001 3617 5070
Awarding Body: Imperial College London (University of London)
Current Institution: Imperial College London
Date of Award: 2007
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Dynamic undersampling of MRI data can be used in order to accelerate image acquisition by exploiting the inherent information redundancy existing in sequences of dynamic images. Regions within the field of view (FOV) are forced to share temporal bandwidth, this leads to more efficient encoding so long as regions requiring a large bandwidth are not forced to share. It is noted that existing image reconstruction techniques (for example k-t SENSE) can cause temporal blurring whilst attempting to filter noise from reconstructed images. A new reconstruction technique named x-f choice is proposed, with the aim of reducing this effect. Image reconstruction techniques for dynamic undersampled data in general require some estimate of the expected temporal variation. Existing methods use low resolution images as a pragmatic solution, it is shown that errors can result from this. In this project methods for extracting this information from undersampled data have been investigated. The focus has been on identifying temporally correlated signals within the undersampled data, so that information lost by undersampling may be estimated from elsewhere without the need for extra data. X-f choice in conjunction with analysis of temporal correlations has been used to successfully reconstructed DCE-MRA data acquired in vivo without the need for any extra information at reduction factors of up to 9. It is shown that temporal correlations may be used in order to improve image reconstruction quality in a variety of cases including cardiac imaging, using both x-f choice and the existing reconstruction technique k-t SENSE.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available