使用潜在桥接匹配的快速跨场强多对比脑MRI翻译
Fast Cross-Strength Multi-Contrast Brain MRI Translation using Latent Bridge Matching
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中文总结 AI 辅助
提出基于条件潜在桥接匹配的统一模型，实现跨场强多对比脑MRI快速翻译，单步推理生成30个轴向切片<90秒，全容积<70秒，在MRIxFields2026挑战中表现优异。
中文摘要 AI 辅助
在不同场强下采集的磁共振成像（MRI）在噪声、均匀性、对比度和均匀性方面表现出显著差异，这限制了跨采集设置的可比性并使下游分析复杂化。我们通过一个基于条件潜在桥接匹配框架的统一条件模型来解决这一问题，该模型用于可控的场间合成。我们的单一模型在MRIxFields2026挑战的所有三个任务的验证阶段均取得了极具竞争力的结果，无需任务特定的架构或训练。我们实现了仅需单步推理的快速生成，在单个NVIDIA A5000 GPU上，生成所有模态和场强组合的30个轴向切片耗时不到90秒，而全容积的跨模态-场强翻译耗时不到70秒。我们进一步提供了关于解决方案不同组件的广泛消融研究。代码：此https URL
英文摘要
Magnetic Resonance Imaging (MRI) acquired at different field strengths exhibits pronounced variation in noise, resolution, homogeneity, and contrast, which limits comparability across acquisition settings and complicates downstream analysis. We address this with a unified conditional model for controllable field-to-field synthesis, built on the framework of conditional latent bridge matching. Our single model achieves highly competitive results across the validation phase for all three tasks of the MRIxFields2026 challenge without task-specific architectures or training. We achieve fast generation with only a single inference step, producing all modality and field-strength combinations for $30$ axial slices in under $90$ seconds, as well as cross-modality-strength translation for a full volume in under $70$ seconds, on a single NVIDIA A5000 GPU. We further provide extensive ablations regarding different components of our solution. Code: https://gitlab.com/siddharthsrivastava/mrixfields-2026
发表机构
- University of Warwick（华威大学）
机构由 AI 辅助整理，请以论文原文为准。
