Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceModel-based Deep Unrolled Networks offer high quality reconstructions but the performance degrades if any mismatch occurs in acquisition settings. Different networks for different acquisition settings require extensive training data, in addition to making the clinical deployment difficult. We propose a single unrolled deep-learning algorithm called as Ada-MoDL, whose parameters are conditioned on the acquisition information (metadata) of a dataset, using a Multi-Layer Perceptron (MLP) that maps the metadata to network parameters. Ada-MoDL outperforms models trained for a specific acquisition setting or a single model trained with all the available contrasts, when the training data in each acquisition setting is limited.
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