Accurate image reconstruction from reduced data in pulsed electron paramagnetic resonance imaging

Area of Science:

• Medical Imaging
• Biophysics
• Magnetic Resonance

Background:

• Pulsed electron paramagnetic resonance imaging (EPRI) offers potential for fast imaging.
• Acquiring full data for EPRI can be time-consuming.
• Reconstructing images from sparse views (SVs) or limited-angular range (LAR) data is challenging.

Purpose of the Study:

• To develop and investigate algorithms for reconstructing effective probe-density (EPR) images from SV or LAR data.
• To obtain oxygen-concentration images from reconstructed EPR images.
• To enable faster pulsed EPRI for in vivo studies.

Main Methods:

• Formulated image reconstruction as an optimization problem with total variation (TV) or directional-TV (DTV) constraints.
• Developed TV and DTV algorithms for EPR image reconstruction from SV and LAR data, respectively.
• Estimated oxygen-concentration images from the reconstructed EPR images.

Main Results:

• Demonstrated the potential of TV and DTV algorithms using numerical, physical phantom, and mouse model data.
• Achieved numerically accurate EPR and oxygen-concentration images from SV and LAR data, respectively.
• Validated the algorithms' performance in realistic imaging scenarios.

Conclusions:

• The developed TV and DTV algorithms successfully reconstruct accurate EPR and oxygen-concentration images from SV and LAR data in pulsed EPRI.
• These algorithms may facilitate the design of minimized scanning time protocols.
• This research could enable in vivo studies using fast pulsed EPRI.