1. Self-supervised learning for improved calibrationless radial MRI with NLINV-Net.
- Author
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Blumenthal M, Fantinato C, Unterberg-Buchwald C, Haltmeier M, Wang X, and Uecker M
- Subjects
- Humans, Artifacts, Phantoms, Imaging, Calibration, Supervised Machine Learning, Reproducibility of Results, Magnetic Resonance Imaging, Image Processing, Computer-Assisted methods, Neural Networks, Computer, Algorithms, Heart diagnostic imaging
- 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 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., 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 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., 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., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
- Published
- 2024
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