Reconstruction of 3D dynamic contrast-enhanced magnetic resonance imaging using nonlocal means

To develop and test a nonlocal means-based reconstruction algorithm for undersampled 3D dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of tumors.

We propose a reconstruction technique that is based on the recently proposed nonlocal means (NLM) filter which can relax trade-offs in spatial and temporal resolutions in dynamic imaging. Unlike the original application of NLM for image denoising, the MR reconstruction framework here can offer high-quality images from undersampled k-space data. The method is based on enforcing similarity constraints in terms of neighborhoods of pixels rather than individual pixels. The method was applied on undersampled 3D DCE imaging of breast and brain tumor datasets and the results were compared to sliding window reconstructions and to a compressed sensing method using total variation constraints on the images.

Undersampling factors of up to five were obtained with the proposed approach while preserving the spatial and temporal characteristics. The NLM reconstruction method offered improved performance over the sliding window and the total variation constrained reconstruction techniques.

The reconstruction framework here can give high-quality images from undersampled DCE MRI data and has the potential to improve the quality of DCE tumor imaging.