Learning data triage: Linear decoding works for compressive MRI

Yen-Huan Li; Volkan Cevher

ICASSP 2016accepted0 citations

Learning data triage: Linear decoding works for compressive MRI

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.

BibTeX

@inproceedings{icassp2016_learningdatatria,
  title = {Learning data triage: Linear decoding works for compressive MRI},
  author = {Yen-Huan Li and Volkan Cevher},
  booktitle = {ICASSP 2016},
  year = {2016}
}