MR images from fewer data (2012)
Type of ContentConference Contributions - Published
PublisherUniversity of Canterbury. Electrical and Computer Engineering
AuthorsVafadar, B., Bones, P.J.show all
There is a strong motivation to reduce the amount of acquired data necessary to reconstruct clinically useful MR images, since less data means faster acquisition sequences, less time for the patient to remain motionless in the scanner and better time resolution for observing temporal changes within the body. We recently introduced an improvement in image quality for reconstructing parallel MR images by incorporating a data ordering step with compressed sensing (CS) in an algorithm named `PECS'.1 That method requires a prior estimate of the image to be available. We are extending the algorithm to explore ways of utilising the data ordering step without requiring a prior estimate. The method presented here first reconstructs an initial image x1 by compressed sensing (with sparsity enhanced by SVD), then derives a data ordering from x1, R01, which ranks the voxels of x1 according to their value. A second reconstruction is then performed which incorporates minimisation of the rst norm of the estimate after ordering by R01, resulting in a new reconstruction x2. Preliminary results are encouraging.
CitationVafadar, B., Bones, P.J. (2012) MR images from fewer data. San Diego, CA, USA: "Optical Engineering & Applications: Image Reconstruction from Incomplete Data VII, 14-15 Aug 2012. Proceedings SPIE, 8500, 850003.
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KeywordsParallel Magnetic Resonance Imaging (pMRI); Compressed Sensing (CS)
ANZSRC Fields of Research08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080106 - Image Processing
11 - Medical and Health Sciences::1103 - Clinical Sciences::110320 - Radiology and Organ Imaging
09 - Engineering::0903 - Biomedical Engineering::090399 - Biomedical Engineering not elsewhere classified