自监督学习用于改进无校准径向MRI的NLINV-Net
Self-Supervised Learning for Improved Calibrationless Radial MRI with NLINV-Net
AI总结：
针对无训练真实标签的径向MRI无校准重建问题，提出结合数据欠采样自监督（SSDU）训练策略的NLINV-Net架构，在实时心脏成像和定量T1映射中验证了其噪声抑制、伪影减少及无需参数调整的优势。
摘要：
目的：开发一种神经网络架构，在没有训练用的真实标签时，改进径向数据的无校准重建。方法：NLINV-Net是一种基于模型的神经网络架构，通过非线性反演（NLINV）直接从（径向）k空间数据估计图像和线圈灵敏度。结合通过数据欠采样自监督（SSDU）的训练策略，可用于无真实标签重建的成像问题。我们在（1）实时心脏成像和（2）基于单次子空间的定量T1映射中验证了该方法。此外，区域优化虚拟（ROVir）线圈用于抑制视场外伪影，并将基于k空间的SSDU损失聚焦于感兴趣区域。将NLINV-Net重建与传统NLINV和PI-CS（并行成像+压缩感知）重建进行比较，定性评估了区域优化虚拟线圈和训练损失类型的效果。结果：NLINV-Net重建的噪声显著低于NLINV重建。ROVir线圈有效抑制了神经网络未抑制的条纹伪影，且基于ROVir的聚焦损失使心脏实时成像中心肌壁运动的时间序列视觉上更清晰。定量成像中，NLINV-Net重建的T1图质量与PI-CS相似，但无需切片特异性正则化参数调整。结论：NLINV-Net是无校准成像的通用工具，可用于无真实标签的挑战性成像场景。
英文摘要：
Purpose: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via non-linear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the FoV and to focus the k-space based SSDU loss on the region of interest. NLINV-Net based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. Results: NLINV-Net based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir-based focussed loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real-time imaging. For quantitative imaging, T1-maps reconstructed using NLINV-Net show similar quality as PI-CS reconstructions, but NLINV-Net does not require slice-specific tuning of the regularization parameter. Conclusion: NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
