Self‐supervised learning for improved calibrationless radial MRI with NLINV‐Net

Abstract

AbstractPurposeTo develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training.MethodsNLINV‐Net is a model‐based neural network architecture that directly estimates images and coil sensitivities from (radial) k‐space data via nonlinear inversion (NLINV). Combined with a training strategy using self‐supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real‐time cardiac imaging and (2) single‐shot subspace‐based quantitative T1 mapping. Furthermore, region‐optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k‐space‐based SSDU loss on the region of interest. NLINV‐Net‐based reconstructions were compared with conventional NLINV and PI‐CS (parallel imaging + compressed sensing) reconstruction and the effect of the region‐optimized virtual coils and the type of training loss was evaluated qualitatively.ResultsNLINV‐Net‐based reconstructions contain significantly less noise than the NLINV‐based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir‐based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real‐time imaging. For quantitative imaging, T1‐maps reconstructed using NLINV‐Net show similar quality as PI‐CS reconstructions, but NLINV‐Net does not require slice‐specific tuning of the regularization parameter.ConclusionNLINV‐Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.