Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography, yet they struggle to generalize in uncalibrated modalities -- notoriously magnetic resonance imaging (MRI), where performance is highly sensitive to the differences in MR contrast, resolution, and orientation between the training and testing data. This prevents broad applicability to the diverse clinical acquisition protocols in the real world. We introduce Brain-ID, a robust feature representation learning strategy for brain imaging, which is contrast-agnostic, and robust to the brain anatomy of each subject regardless of the appearance of acquired images (i.e., deformation, contrast, resolution, orientation, artifacts, etc). Brain-ID is trained entirely on synthetic data, and easily adapts to downstream tasks with our proposed simple one-layer solution. We validate the robustness of Brain-ID features, and evaluate their performance in a variety of downstream applications, including both contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks. Extensive experiments on 6 public datasets demonstrate that Brain-ID achieves state-of-the-art performance in all tasks, and more importantly, preserves its performance when only limited training data is available.

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