Learning data triage: Linear decoding works for compressive MRI.
Journal
2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Journal Volume
2016-May
Pages
4034-4038
Date Issued
2016
Author(s)
Cevher, Volkan
Abstract
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. © 2016 IEEE.
Subjects
Compressive sampling; least squares estimation; magnetic resonance imaging (MRI); statistical learning; sub-modular minimization
Description
Shanghai, China
Type
conference paper