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.
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.
Authors: David H Johnson; Rizwan Ahmad; Yangping Liu; Zhiyu Chen; Alexandre Samouilov; Jay L Zweier Journal: Magn Reson Med Date: 2014-02 Impact factor: 4.668
Authors: Sankaran Subramanian; Gadisetti V R Chandramouli; Alan McMillan; Rao P Gullapalli; Nallathamby Devasahayam; James B Mitchell; Shingo Matsumoto; Murali C Krishna Journal: Magn Reson Med Date: 2012-10-08 Impact factor: 4.668