PurposeTo improve image quality and reduce data requirements for spatial electron paramagnetic resonance imaging (EPRI) by developing a novel reconstruction approach using compressed sensing (CS).MethodsEPRI 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.ResultsA 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).ConclusionA novel 3D EPRI reconstruction using compressed sensing was developed and offers superior SNR and reduced artifacts from highly undersampled data. Magn Reson Med 72:893–901, 2014. © 2013 Wiley Periodicals, Inc.