ContextMRI：通过元数据条件增强压缩感知 MRI
ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning
AI总结：
提出 ContextMRI 文本条件扩散模型，将临床元数据经 CLIP 嵌入用于复值 MRI 重建，在多数据集、加速因子和欠采样模式下持续提升性能。
摘要：
压缩感知 MRI 旨在通过采样更少的 k 空间测量值，然后以算法方式重建缺失数据，从而加速 MRI 采集过程。这些方法的成功通常依赖于强先验或学习到的统计模型。尽管近期基于扩散模型的先验已展现出巨大潜力，但以往方法通常忽略了临床可用的元数据（例如患者人口统计学信息、成像参数、特定切片信息）。在实践中，元数据包含有关解剖结构和采集协议的有意义线索，表明其可以进一步约束重建问题。在本研究中，我们提出 ContextMRI，这是一种用于 MRI 的文本条件扩散模型，将细粒度元数据整合到重建过程中。我们直接在经过最少处理的复值 MRI 图像上训练像素空间扩散模型。推理期间，元数据被转换为结构化文本提示，并通过 CLIP 文本嵌入输入模型。通过以元数据对先验进行条件化，我们实现了更准确的重建，并在多个数据集、加速因子和欠采样模式上展示出一致提升。实验表明，提高元数据的保真度（范围从切片位置和对比度到患者年龄、性别和病理）会系统性地提升重建性能。本研究凸显了利用临床上下文解决逆问题的尚未开发的潜力，并为元数据驱动的 MRI 重建开辟了新方向。
英文摘要：
Compressed sensing MRI seeks to accelerate MRI acquisition processes by sampling fewer k-space measurements and then reconstructing the missing data algorithmically. The success of these approaches often relies on strong priors or learned statistical models. While recent diffusion model-based priors have shown great potential, previous methods typically ignore clinically available metadata (e.g. patient demographics, imaging parameters, slice-specific information). In practice, metadata contains meaningful cues about the anatomy and acquisition protocol, suggesting it could further constrain the reconstruction problem. In this work, we propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process. We train a pixel-space diffusion model directly on minimally processed, complex-valued MRI images. During inference, metadata is converted into a structured text prompt and fed to the model via CLIP text embeddings. By conditioning the prior on metadata, we unlock more accurate reconstructions and show consistent gains across multiple datasets, acceleration factors, and undersampling patterns. Our experiments demonstrate that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance. This work highlights the untapped potential of leveraging clinical context for inverse problems and opens a new direction for metadata-driven MRI reconstruction.
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