MultiNet PyGRAPPA: Multiple neural networks for reconstructing variable density GRAPPA (a 1H FID MRSI study)
In conclusion, using multiple neural networks to predict the missing points in GRAPPA reconstruction results in a more reliable data recovery while keeping the noise levels under control. Combining this high fidelity reconstruction with variable density undersampling (MultiNet PyGRAPPA) enables higher in-plane acceleration factors even for non-lipid suppressed 1H FID MRSI, without introducing any structured aliasing artifact in the image.Graphical abstract
Source: NeuroImage - Category: Neuroscience Source Type: research
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