A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction

We evaluate a wavelet-based algorithm to estimate the coil sensitivity modulation from surface coils. This information is used to improve the image homogeneity of magnetic resonance imaging when a surface coil is used for reception, and to increase image encoding speed by reconstructing images from under-sampled (aliased) acquisitions using parallel magnetic resonance imaging (MRI) methods for higher spatiotemporal image resolutions. The proposed algorithm estimates the spatial sensitivity profile of surface coils from the original anatomical images directly without using the body coil for additional reference scans or using coil position markers for electromagnetic model-based calculations. No prior knowledge about the anatomy is required for the application of the algorithm. The estimation of the coil sensitivity profile based on the wavelet transform of the original image data was found to provide a robust method for removing the slowly varying spatial sensitivity pattern of the surface coil image and recovering full FOV images from two-fold acceleration in 8-channel parallel MRI. The results, using bi-orthogonal Daubechies 97 wavelets and other members in this family, are evaluated for T1-weighted and T2-weighted brain imaging.