Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, undersampling, averaging, data samplingLow signal-to-noise (SNR) ratios inherent to low-field (LF) MRI challenge its relevance in clinical applications. Accelerating the acquisition by undersampling k-space followed by reconstruction techniques has already shown promising results. Yet, undersampling is usually done by skipping high-frequency information which can lead to misdiagnosis as small lesions can be missed. In this study, we exploited a specificity of low-SNR regimes, that is signal averaging, to explore different acceleration strategies without skipping crucial information in k-space. The DL-reconstructed images arising from those sampling schemes have been evaluated on acquired in-vivo and ex-vivo LF-MRI datasets, showcasing high-frequency preservation and potential for generalization.
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