New method completes missing brain tumor MRI sequences preserving 3D details
MOSAIC-Flow: Orthogonal-Axis and Status-Conditioned Fusion for Missing-Sequence Completion in Brain Tumor MRI
Computer Vision and Pattern Recognition
Summary
MRI scans of brain tumors often have missing image types, which makes it hard to fully understand the tumor. The authors developed a new method called MOSAIC-Flow that can fill in these missing images while keeping fine 3D details intact. Their approach processes each MRI type separately, looks along three different body directions, and adapts based on which images are available. Tested on a dataset of brain tumor MRIs, MOSAIC-Flow showed consistent quality in completing different missing image types, especially structural MRI scans.
What this means in practice
- •For hospital radiology teams: Improve diagnostic completeness by reconstructing missing MRI sequences for brain tumor patients without rescanning.
- •For medical imaging software developers: Integrate a method that completes missing brain MRI sequences while preserving 3D anatomical detail to enhance image analysis tools.$Commercial implications: Enables software products offering automated MRI sequence completion, improving clinical workflow and imaging consistency.
Authors
Pengli Zhu, Zhihao Peng, Yixuan Yuan
Abstract
Missing-sequence completion is important for comprehensive brain tumor characterization, but cross-modal fusion can obscure sequence-specific contrast and lose fine-grained three-dimensional tumor detail. To address this challenge, we introduce Modality-aware Orthogonal-axis Status-conditioned Attentive Integration for Completion (MOSAIC-Flow), a unified framework for missing-sequence completion that learns a target-conditioned velocity field while keeping the seven acquired volumes fixed throughout the transport. Specifically, modality-aware stems independently encode each sequence, and orthogonal-axis refinement captures axial, coronal, and sagittal dependencies while retaining the three-dimensional representation. Status-conditioned prototype fusion uses compact modality and availability tokens to route and modulate pooled queries from the fused visual representation, enabling target-adaptive conditioning without dense voxel-to-voxel attention. A conditional flow matching objective transports target-channel noise toward the single missing sequence, while four Heun trajectories are averaged to produce the completion. Evaluation across structural, diffusion, and contrast-enhanced MRI in UCSF-PDGM demonstrates consistent whole-brain and tumor-region fidelity across all eight single-target completion tasks, with the strongest performance on structural sequences. Code is available at https://github.com/CUHK-AIM-Group/MOSAIC-FLOW.