标题： 基于信任引导的MR图像重建方法及其辅助信息 显示英文标题

标题： A Trust-Guided Approach to MR Image Reconstruction with Side Information

摘要： 减少MRI扫描时间可以提高患者护理质量并降低医疗成本。许多加速方法旨在通过一个病态或条件数差的线性逆问题（LIP）从稀疏的k空间数据重建出具有诊断质量的图像。为了解决由此产生的歧义，关键是要将先验知识纳入优化问题中，例如以正则化形式。在医学成像中较少使用的一种先验知识形式是从当前采集之外的来源获得的现成辅助数据（也称为侧信息）。在本文中，我们提出了信任引导变分网络（TGVN），这是一种端到端的深度学习框架，能够有效地且可靠地将侧信息集成到LIP中。我们展示了它在多线圈、多对比度MRI重建中的有效性，其中来自一种对比度的不完整或低信噪比测量被用作侧信息，用于从严重欠采样的数据中重建另一种对比度的高质量图像。TGVN在不同的对比度、解剖结构和场强下表现稳健。与利用侧信息的基线方法相比，TGVN在保持细微病理特征的同时，实现了更好的图像质量，即使在挑战性的加速水平下也是如此，大幅加快了采集速度，同时最大限度地减少了幻象。源代码和数据集分割可在github.com/sodicksonlab/TGVN获取。 显示英文摘要

摘要： Reducing MRI scan times can improve patient care and lower healthcare costs. Many acceleration methods are designed to reconstruct diagnostic-quality images from sparse k-space data, via an ill-posed or ill-conditioned linear inverse problem (LIP). To address the resulting ambiguities, it is crucial to incorporate prior knowledge into the optimization problem, e.g., in the form of regularization. Another form of prior knowledge less commonly used in medical imaging is the readily available auxiliary data (a.k.a. side information) obtained from sources other than the current acquisition. In this paper, we present the Trust- Guided Variational Network (TGVN), an end-to-end deep learning framework that effectively and reliably integrates side information into LIPs. We demonstrate its effectiveness in multi-coil, multi-contrast MRI reconstruction, where incomplete or low-SNR measurements from one contrast are used as side information to reconstruct high-quality images of another contrast from heavily under-sampled data. TGVN is robust across different contrasts, anatomies, and field strengths. Compared to baselines utilizing side information, TGVN achieves superior image quality while preserving subtle pathological features even at challenging acceleration levels, drastically speeding up acquisition while minimizing hallucinations. Source code and dataset splits are available on github.com/sodicksonlab/TGVN.