Self-Supervised Learning for Improved Calibrationless Radial MRI with NLINV-Net

Moritz Blumenthal; Chiara Fantinato; Christina Unterberg-Buchwald; Markus Haltmeier; Xiaoqing Wang; Martin Uecker

doi:10.1002/mrm.30234

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arXiv · 2402.06550

Self-Supervised Learning for Improved Calibrationless Radial MRI with NLINV-Net

Moritz Blumenthal·Chiara Fantinato·Christina Unterberg-Buchwald·Markus Haltmeier·Xiaoqing Wang·Martin Uecker

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Abstract

Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via non-linear 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 FoV 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. Results: NLINV-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 focussed 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. Conclusion: NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

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Moritz Blumenthal, Chiara Fantinato, Christina Unterberg-Buchwald, Markus Haltmeier, Xiaoqing Wang, Martin Uecker. 2024-02-09. Self-Supervised Learning for Improved Calibrationless Radial MRI with NLINV-Net. https://doi.org/10.1002/mrm.30234

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