发表机构
University of Texas Southwestern Medical Center; University of California, San Francisco（德克萨斯大学西南医学中心; 加州大学旧金山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出联合监督与自监督训练及采集鲁棒策略的4D血流MRI重建方法，在主动脉数据集上显著降低误差并提升泛化能力。
AI 中文摘要
4D血流MRI测量时间分辨的三方向血流速度，但需要较长的采集时间，其诊断信号由速度编码之间的相位差承载，而非图像幅度。近期工作开发了逐编码变分网络来解决该领域的图像重建问题。在本工作中，我们结合了联合监督与自监督训练机制，并在监督过程中同时利用幅度和速度数据。同时，我们基于加速因子添加了多种采集鲁棒和条件化策略。在CMRx4DFlow~2026主动脉数据集（1.5T和3T）上，与训练匹配的基线相比，我们的模型在$R{=}10$--$50$范围内将RelErr降低了$38$--$50\\%$，AngErr降低了$7.0$--$8.7^\circ$，在每个加速倍数下均优于所有保留受试者。我们的模型还通过联合训练方案展现出对分布外数据迁移的强泛化能力，SSIM提高了$7.2\\%$，AngErr和RelErr分别降低了$38\\%$和$31\\%$。
英文摘要
4D flow MRI measures time-resolved, three-directional blood velocity but requires long acquisition times, and its diagnostic signal is carried by the phase difference \emph{between} velocity encodings, not by image magnitude. Recent work has developed a per-encoding variational network to address image reconstruction in this field. In this work, we incorporate a joint supervised and self-supervised training regime and utilize both magnitude and velocity data during supervision. At the same time, we add multiple acquisition-robust and conditioning strategies based on the acceleration factors. On the CMRx4DFlow~2026 aortic dataset (1.5 and 3T), our model lowers RelErr by $38$--$50\%$ and AngErr by $7.0$--$8.7^\circ$ against a training-matched baseline across $R{=}10$--$50$, improving on every held-out subject at every acceleration. Our model also shows strong generalization ability to transfer on out-of-distribution data by employing the joint training scheme, with an increase of $7.2\%$ in SSIM and decrease of $38\%$ and $31\%$ in AngErr and RelErr respectively.
