用自监督学习让MRI图像学会看懂解剖结构，不被扫描序列干扰。
Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
- 从单个3D MRI扫描生成多种成像对比，强制模型学一致特征。
- 在低数据量下分割和去噪任务性能提升最高达8.3%和4.2dB。
- 模型可跨医院站点通用，适合临床大规模部署。
自监督深度学习在2D自然图像分析中已取得进展，但在3D MRI领域仍面临数据稀缺、2D预训练模型无法捕捉体积上下文的挑战。本文提出一种序列无关的自监督框架，利用定量MRI（qMRI）特性：从单一3D qMRI扫描中模拟多种MRI对比，并强制这些对比下的表示保持一致，从而学习以解剖为中心而非依赖扫描序列的特征。结果是一个统一的3D编码器，在多个任务与协议下表现优异。在健康脑分割（IXI）、中风病灶分割（ARC）及MRI去噪任务上的实验表明，相比基线自监督方法显著提升，尤其在低数据场景下（最高+8.3% Dice，+4.2 dB PSNR）。模型还具备对未见站点的良好泛化能力，支持可扩展的临床应用。代码与训练模型已公开于 https://github.com/liamchalcroft/contrast-squared。
原文摘要 · Abstract (English)
Self-supervised deep learning has accelerated 2D natural image analysis but remains difficult to translate into 3D MRI, where data are scarce and pre-trained 2D backbones cannot capture volumetric context. We present a \emph{sequence-invariant} self-supervised framework leveraging quantitative MRI (qMRI). By simulating multiple MRI contrasts from a single 3D qMRI scan and enforcing consistent representations across these contrasts, we learn anatomy-centric rather than sequence-specific features. The result is a single 3D encoder that excels across tasks and protocols. Experiments on healthy brain segmentation (IXI), stroke lesion segmentation (ARC), and MRI denoising show significant gains over baseline SSL approaches, especially in low-data settings (up to +8.3\% Dice, +4.2 dB PSNR). It also generalises to unseen sites, supporting scalable clinical use. Code and trained models are publicly available at https://github.com/liamchalcroft/contrast-squared
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