Compressed sensing of spatial electron paramagnetic resonance imaging

David H. Johnson, Rizwan Ahmad, Guanglong He, Alexandre Samouilov, Jay L. Zweier

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


PURPOSE: To improve image quality and reduce data requirements for spatial electron paramagnetic resonance imaging (EPRI) by developing a novel reconstruction approach using compressed sensing (CS).

METHODS: EPRI is posed as an optimization problem, which is solved using regularized least-squares with sparsity promoting penalty terms, consisting of the l1 norms of the image itself and the total variation of the image. Pseudo-random sampling was employed to facilitate recovery of the sparse signal. The reconstruction was compared with the traditional filtered back-projection reconstruction for simulations, phantoms, isolated rat hearts, and mouse gastrointestinal (GI) tracts labeled with paramagnetic probes.

RESULTS: A combination of pseudo-random sampling and CS was able to generate high-fidelity EPR images at high acceleration rates. For three-dimensional (3D) phantom imaging, CS-based EPRI showed little visual degradation at nine-fold acceleration. In rat heart datasets, CS-based EPRI produced high quality images with eight-fold acceleration. A high resolution mouse GI tract reconstruction demonstrated a visual improvement in spatial resolution and a doubling in signal-to-noise ratio (SNR).

CONCLUSION: A novel 3D EPRI reconstruction using compressed sensing was developed and offers superior SNR and reduced artifacts from highly undersampled data.

Original languageEnglish (US)
Pages (from-to)893-901
Number of pages9
JournalMagnetic Resonance in Medicine
Issue number3
StatePublished - Sep 1 2014
Externally publishedYes


  • compressed sensing
  • electron paramagnetic resonance imaging
  • filtered backprojection
  • image processing

ASJC Scopus subject areas

  • Medicine(all)


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