Denoising Induced Iterative Reconstruction for Fast $$$T_{1\rho}$$$ Parameter Mapping

We propose a novel DenOising induCed iTerative recOnstRuction framework (DOCTOR) to realize fast $$$T_{1\rho}$$$ parameter mapping from under-sampled k-space measurements. The proposed formulation constrains simultaneously intensity-based and orientation-based similarity between the reconstructed images and denoised prior images. Two state-of-art 3D denoising technologies are utilized including NLM3D and BM4D. The reconstruction alternates between two steps of denoising and a quadratic programming attacked by non-linear conjugate gradient method. The parameter maps are created from the reconstructed images using conventional fitting with an established relaxometry model. Through experiments in-vivo $$$T_{1\rho}$$$-weighted brain MRI datasets, we can observe superior image-quality of the proposed DOCTOR scheme.