L-TGVN：利用纵向先验进行个性化快速MRI
L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI
AI总结：
提出L-TGVN，一种利用纵向先验作为侧信息从高度欠采样测量中重建当前扫描的变分网络，无需显式配准并适应协议差异，在定量指标和结构保持上优于基线方法。
摘要：
MRI提供优异的软组织对比度且无电离辐射，但长采集时间增加患者不适，同时提高检查成本并限制扫描仪吞吐量。减少扫描时间的常见方法是采集更少的测量值，这会产生一个病态线性逆问题；因此，恢复诊断质量的图像需要结合测量数据之外的先验知识。在随访检查中，患者最近的先前扫描可以提供高度信息化的受试者特定背景，但实际应用因时间变化（包括病理进展）、扫描间错位以及跨采集协议漂移而复杂化。在这项工作中，我们引入了L-TGVN，一种纵向信任引导变分网络，利用先前扫描作为侧信息，从高度欠采样测量中重建当前扫描。关键是，L-TGVN约束先前扫描的影响与获取的测量一致。与许多现有的纵向重建方法不同，它不需要先前扫描和当前扫描之间的显式预配准。它进一步适应不同就诊间的采集协议差异（例如，序列参数的变化）。我们在匹配容量的基线上评估L-TGVN，包括先验引导方法和不使用纵向先验的方法，并观察到标准定量指标的一致改进，以及在挑战性加速下更好地保留精细结构。源代码可在github.com/sodicksonlab/L-TGVN获取。
英文摘要：
MRI provides excellent soft-tissue contrast without ionizing radiation, but long acquisition times increase patient discomfort while also raising exam costs and limiting scanner throughput. A common approach to reduce scan time is to acquire fewer measurements, which yields an ill-posed linear inverse problem; recovering diagnostic-quality images therefore requires incorporating prior knowledge beyond the measured data. In follow-up exams, the most recent prior scan of a patient can provide a highly informative subject-specific context, but practical use is complicated by temporal changes (including pathology progression), misalignment between scans, and protocol drift across acquisitions. In this work, we introduce L-TGVN, a Longitudinal Trust-Guided Variational Network that leverages prior scans as side information to reconstruct the current scan from heavily undersampled measurements. Crucially, L-TGVN constrains the influence of prior scans to be consistent with the acquired measurements. Unlike many existing longitudinal reconstruction methods, it does not require explicit pre-registration between prior and current scans. It further accommodates differences in acquisition protocols across visits (e.g., changes in sequence parameters). We evaluate L-TGVN against matched-capacity baselines, including prior-guided methods and methods that do not use longitudinal priors, and observe consistent improvements in standard quantitative metrics together with better preservation of fine structures at challenging accelerations. Source code is available at github.com/sodicksonlab/L-TGVN.
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。
