用深度学习优化心脏参数映射的基函数，提升图像清晰度与抗运动伪影能力。
Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace Models
- 分两阶段估计空间基函数：先用低秩张量模型提取，再用深度网络精细优化。
- 相比传统傅里叶重建和并行成像，新方法显著降低噪声并增强边缘锐度。
- 适用于需要高分辨率心脏纤维化/水肿评估的临床MRI场景。
心脏参数映射有助于评估心肌纤维化和水肿。该技术依赖于单次心跳成像，易受成像窗口内运动干扰。缩短成像窗口需采用欠采样重建以保持图像保真度与空间分辨率。本文提出基于多维数据低秩张量模型的方法，从辅助并行成像重建中联合估计空间基图像与时间基时间曲线。通过全监督训练的深度神经网络对张量估计的空间基进一步优化，利用学习到的心脏基函数表示提升空间基的保真度。该两阶段空间基估计方法将与基于傅里叶的重建及仅并行成像进行对比，验证其在锐化与去噪方面的优势。
原文摘要 · Abstract (English)
Cardiac parametric mapping is useful for evaluating cardiac fibrosis and edema. Parametric mapping relies on single-shot heartbeat-by-heartbeat imaging, which is susceptible to intra-shot motion during the imaging window. However, reducing the imaging window requires undersampled reconstruction techniques to preserve image fidelity and spatial resolution. The proposed approach is based on a low-rank tensor model of the multi-dimensional data, which jointly estimates spatial basis images and temporal basis time-courses from an auxiliary parallel imaging reconstruction. The tensor-estimated spatial basis is then further refined using a deep neural network, trained in a fully supervised fashion, improving the fidelity of the spatial basis using learned representations of cardiac basis functions. This two-stage spatial basis estimation will be compared against Fourier-based reconstructions and parallel imaging alone to demonstrate the sharpening and denoising properties of the deep learning-based subspace analysis.
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