Complex difference constrained compressed sensing reconstruction for accelerated PRF thermometry with application to MRI-induced RF heating.

Purpose: Introduce a novel compressed sensing reconstruction method to accelerate proton resonance frequency shift temperature imaging for MRI-induced radiofrequency heating evaluation.

Methods: A compressed sensing approach that exploits sparsity of the complex difference between postheating and baseline images is proposed to accelerate proton resonance frequency temperature mapping. The method exploits the intra-image and inter-image correlations to promote sparsity and remove shared aliasing artifacts. Validations were performed on simulations and retrospectively undersampled data acquired in ex vivo and in vivo studies by comparing performance with previously published techniques.

Results: The proposed complex difference constrained compressed sensing reconstruction method improved the reconstruction of smooth and local proton resonance frequency temperature change images compared to various available reconstruction methods in a simulation study, a retrospective study with heating of a human forearm in vivo, and a retrospective study with heating of a sample of beef ex vivo.

Conclusion: Complex difference based compressed sensing with utilization of a fully sampled baseline image improves the reconstruction accuracy for accelerated proton resonance frequency thermometry. It can be used to improve the volumetric coverage and temporal resolution in evaluation of radiofrequency heating due to MRI, and may help facilitate and validate temperature-based methods for safety assurance.