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Inspired by the recently proposed magnetic resonance fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are a random pulse excitation sequence following the MRF technique, a random EPI subsampling strategy, and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that, theoretically, as long as the excitation sequence possesses an appropriate form of persistent excitation, we are able to accurately recover the proton density, T1, T2, and off-resonance maps simultaneously from a limited number of samples. These results are further supported through extensive simulations using a brain phantom.
|Number of pages||34|
|Journal||Siam journal on imaging sciences|
|Early online date||16 Dec 2014|
|Publication status||E-pub ahead of print - 16 Dec 2014|
- Compressed sensing
- Bloch equations
- Johnston-Linderstrauss embedding
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- 1 Finished
- School of Engineering - Jeffrey Collins Chair of Signal Processing
Person: Academic: Research Active