用自监督k空间正则化提升动态MRI重建质量，尤其在高加速下表现优异。
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
- 基于并行成像自洽性设计自监督损失，无需额外数据
- 加速比达54时仍保持优越时空重建效果
- 适用于多种架构，适合动态MRI研究者使用
神经隐式k空间表示（NIK）在高时间分辨率动态磁共振成像中表现良好。然而，缩短采样时间导致训练数据减少，引发严重过拟合问题。为此，本文提出一种新型自监督k空间损失函数$/mathcal{L}_ ext{PISCO}$，用于规范化基于NIK的重建。该损失基于并行成像启发的自洽性（PISCO），在不依赖额外数据的前提下，强制全局k空间邻域关系一致性。静态与动态MR重建的定量与定性评估表明，引入PISCO可显著提升NIK表示性能。尤其在高加速因子（R≥54）下，采用PISCO的NIK相比现有最先进方法展现出更优的时空重建质量。此外，对损失假设与稳定性的广泛分析表明，PISCO具备作为通用自监督k空间损失函数的潜力，适用于更多应用场景与网络架构。代码已开源：https://github.com/compai-lab/2025-pisco-spieker。
原文摘要 · Abstract (English)
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time, and thereby available training data, results in severe performance drops due to overfitting. To address this, we introduce a novel self-supervised k-space loss function $\mathcal{L}_\mathrm{PISCO}$, applicable for regularization of NIK-based reconstructions. The proposed loss function is based on the concept of parallel imaging-inspired self-consistency (PISCO), enforcing a consistent global k-space neighborhood relationship without requiring additional data. Quantitative and qualitative evaluations on static and dynamic MR reconstructions show that integrating PISCO significantly improves NIK representations. Particularly for high acceleration factors (R$\geq$54), NIK with PISCO achieves superior spatio-temporal reconstruction quality compared to state-of-the-art methods. Furthermore, an extensive analysis of the loss assumptions and stability shows PISCO's potential as versatile self-supervised k-space loss function for further applications and architectures. Code is available at: https://github.com/compai-lab/2025-pisco-spieker
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