Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. However, progress has been driven largely by public datasets focused on brain and knee imaging, shaping how models are trained and evaluated. As a result, careful studies of the reliability of these models across diverse anatomical settings remain limited. In this work, we introduce MosaicMRI, a large and diverse collection of fully sampled raw musculoskeletal (MSK) MR measurements designed for training and evaluating machine-learning-based methods. MosaicMRI is the largest open-source raw MSK MRI dataset to date, comprising 2,671 volumes and 80,156 slices. The dataset offers substantial diversity in volume orientation (e.g., axial, sagittal), imaging contrasts (e.g., PD, T1, T2), anatomies (e.g., spine, knee, hip, ankle, and others), and numbers of acquisition coils. Using VarNet as a baseline for accelerated reconstruction task, we perform a comprehensive set of experiments to study scaling behavior with respect to both model capacity and dataset size. Interestingly, models trained on the combined anatomies significantly outperform anatomy-specific models in low-sample regimes, highlighting the benefits of anatomical diversity and the presence of exploitable cross-anatomical correlations. We further evaluate robustness and cross-anatomy generalization by training models on one anatomy (e.g., spine) and testing them on another (e.g., knee). Notably, we identify groups of body parts (e.g., foot and elbow) that generalize well with each other, and highlight that performance under domain shifts depends on both training set size, anatomy, and protocol-specific factors.

翻译：深度学习支撑了磁共振成像中的广泛应用，包括重建、伪影去除和分割。然而，其进展主要由聚焦于脑部和膝关节成像的公开数据集驱动，这塑造了模型的训练与评估方式。因此，关于这些模型在不同解剖学场景下的可靠性的细致研究仍然有限。本文介绍了MosaicMRI——一个大规模、多样化的全采样原始肌肉骨骼（MSK）MR测量数据集，专为训练和评估基于机器学习的方法而设计。MosaicMRI是迄今为止最大的开源原始MSK MRI数据集，包含2,671个体积数据和80,156个切片。该数据集在体积方向（如轴向、矢状位）、成像对比度（如PD、T1、T2）、解剖部位（如脊柱、膝关节、髋关节、踝关节及其他）以及采集线圈数量方面提供了显著的多样性。以VarNet作为加速重建任务的基线，我们开展了一系列全面的实验，研究模型容量与数据集规模共同作用下的扩展行为。有趣的是，在低样本条件下，基于合并解剖部位训练的模型显著优于特定解剖部位的模型，凸显了解剖多样性的优势以及可挖掘的跨解剖相关性。我们进一步通过在一个解剖部位（如脊柱）上训练模型并在另一个部位（如膝关节）上进行测试，评估了模型的鲁棒性和跨解剖泛化能力。值得注意的是，我们识别出具有良好的相互泛化能力的身体部位组（如足部和肘部），并强调域偏移下的性能取决于训练集规模、解剖部位及协议特定因素。