Model‐based reconstruction for looping‐star MRI

Abstract

AbstractPurposeThe aim of this study was to develop a reconstruction method that more fully models the signals and reconstructs gradient echo (GRE) images without sacrificing the signal to noise ratio and spatial resolution, compared to conventional gridding and model‐based image reconstruction method.MethodsBy modeling the trajectories for every spoke and simplifying the scenario to only echo‐in and echo‐out mixture, the approach explicitly models the overlapping echoes. After modeling the overlapping echoes with two system matrices, we use the conjugate gradient algorithm (CG‐SENSE) with the nonuniform FFT (NUFFT) to optimize the image reconstruction cost function.ResultsThe proposed method is demonstrated in phantoms and in‐vivo volunteer experiments for three‐dimensional, high‐resolution T2*‐weighted imaging and functional MRI tasks. Compared to the gridding method, the high resolution protocol exhibits improved spatial resolution and reduced signal loss as a result of less intra‐voxel dephasing. The fMRI task shows that the proposed model‐based method produced images with reduced artifacts and blurring as well as more stable and prominent time courses.ConclusionThe proposed model‐based reconstruction results shows improved spatial resolution and reduced artifacts. The fMRI task shows improved time series and activation map due to the reduced overlapping echoes and under‐sampling artifacts.