Learning Data Triage: Linear Decoding Works for Compressive MRI

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and solving a non-smooth convex minimization problem (e.g., basis pursuit). In this paper, another approach is considered: We learn a good sub-sampling pattern based on available training signals, without knowing the signal structure in advance, and reconstruct an accordingly sub-sampled signal by computationally much cheaper linear reconstruction. We provide a theoretical guarantee on the recovery error, and show via experiments on real-world MRI data the effectiveness of the proposed compressive MRI scheme.


Published in:
2016 Ieee International Conference On Acoustics, Speech And Signal Processing Proceedings, 4034-4038
Presented at:
41st IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2016
Publisher:
New York, Ieee
ISSN:
1520-6149
ISBN:
978-1-4799-9988-0
Keywords:
Laboratories:




 Record created 2016-02-01, last modified 2018-09-13

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