High resolution magnetic resonance thermometry based on the partial separable function model

A long-standing practical problem lies in achieving magnetic resonance thermometry (MRT) with high spatiotemporal resolution because the amount of data required increases exponentially as the physical dimension increases. To solve this problem, a novel method based on a partial separable function (PSF) model was proposed by exploiting the data redundancy. In this PSF model, two datasets (image data and navigating data) are applied for image reconstruction, which determine the spatial and temporal resolution respectively. After the phase information was extracted from the images reconstructed by the PS model, high spatial and temporal resolution MRT was realized by using the reference (proton resonance frequency) PRF shift technique. The simulation and experiment results of this novel method show that the spatial and dynamic characteristics of MRT images were accurately realized by use of PSF model in MRT. This method also has a smaller distortion of the temperature measurement than the conventional MRT. The proposed data acquisition and reconstruction method may facilitate the use of MR-monitored thermal ablations as an effective treatment option especially in moving tissues, such as liver and kidney.