Learning from K-Space: Acquisition-Informed Pretraining for Label-Efficient Segmentation from Undersampled 3D MRI

Accurate three-dimensional (3D) brain tumor segmentation from MRI faces two practical challenges: costly voxel-wise annotations and accelerated acquisition through undersampling. Existing MRI segmentation paradigms either operate on reconstructed images without direct access to acquisition-domain information or incorporate k-space as an additional input without explicitly modeling its task-relevant structure, which can degrade segmentation performance under limited supervision. To address this, we propose an acquisition-informed self-supervised learning framework for effective label-efficient 3D brain tumor segmentation. Specifically, our network learns directly from undersampled k-space together with the corresponding zero-filled reconstructed image, combining acquisition-domain frequency information with essential spatial context without requiring a high-quality MRI reconstruction. To utilize k-space more effectively, our empirical analysis reveals structured pathology-associated spectral deviations that exhibit meaningful spatial correspondence with tumor regions. Consequently, we formulate these spectral residuals as a soft spectral prior during pretraining to guide representation learning. Finally, we introduce an auxiliary lesion-presence branch during fine-tuning that softly modulates dense predictions to effectively suppress unsupported false positives. Extensive experiments on multimodal brain tumor MRI demonstrate consistent improvements under limited supervision, highlighting the value of pathology-aware \(k\)-space learning for label-efficient segmentation from accelerated MRI.