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.

翻译：近年来，基于学习的方法在计算机断层扫描等标定医学影像领域取得了惊人进展，但在非标定模态——尤其是磁共振成像（MRI）中难以泛化，其性能高度敏感于训练数据与测试数据之间的MR对比度、分辨率和取向差异。这阻碍了该方法对真实世界中多样化临床采集协议的广泛适用性。我们提出Brain-ID——一种面向脑影像的鲁棒特征表示学习策略，该策略具有对比度无关性，且对每个受试者的大脑解剖结构具有鲁棒性，不受获取图像外观（如形变、对比度、分辨率、取向、伪影等）影响。Brain-ID完全基于合成数据训练，并可通过我们提出的简单单层解决方案轻松适配下游任务。我们验证了Brain-ID特征的鲁棒性，并在多种下游应用中评估其性能，包括对比度无关任务（解剖重建/对比度合成、脑分割）和对比度依赖任务（超分辨率、偏置场估计）。在6个公开数据集上的大量实验表明，Brain-ID在所有任务中均达到最先进性能，更重要的是，在仅有限训练数据可用时仍能保持其性能。