Abstract
High-resolution (HR) quantitative MRI (qMRI) relaxometry provides objective tissue characterization but remains clinically underutilized due to lengthy acquisition times. We propose a physics-informed, self-supervised framework for qMRI super-resolution that uses routinely acquired HR weighted MRI (wMRI) scans as guidance, thus, removing the necessity for HR qMRI ground truth during training. We formulate super-resolution as Bayesian maximum a posteriori inference, minimizing two discrepancies: (1) between HR images synthesized from super-resolved qMRI maps and acquired wMRI guides via forward signal models, and (2) between acquired LR qMRI and downsampled predictions. This physics-informed objective allows the models to learn from clinical wMRI without HR qMRI supervision. To validate the concept, we generate training data by synthesizing wMRI guides from HR qMRI using signal equations, then degrading qMRI resolution via k-space truncation. A deep neural network learns the super-resolution mapping. Ablation experiments demonstrate that T1-weighted images primarily enhance T1 maps, T2-weighted images improve T2 maps, and combined guidance optimally enhances all parameters simultaneously. Validation on independently acquired in-vivo data from a different qMRI sequence confirms cross-qMRI sequence generalizability. Models trained on synthetic data can produce super-resolved maps from a 1-minute acquisition with quality comparable to a 5-minute reference scan, leveraging the scanner-independent nature of relaxometry parameters. By decoupling training from HR qMRI requirement, our framework enables fast qMRI acquisitions enhanced via routine clinical images, offering a practical pathway for integrating quantitative relaxometry into clinical workflows with acceptable additional scan time.
Abstract (translated)
高分辨率（HR）定量磁共振成像（qMRI）弛豫测量能够提供客观的组织特征描述，但由于采集时间过长，在临床上的应用相对较少。我们提出了一种基于物理信息的自监督框架，用于qMRI超分辨率处理，该框架使用常规获取的HR加权MRI (wMRI)扫描作为指导，因此在训练过程中不需要高分辨率qMRI的真实数据。我们将超分辨率问题形式化为贝叶斯最大后验概率推理，并最小化两种差异：（1）从超分辨率qMRI映射合成的HR图像与通过前向信号模型获得的wMRI引导之间的差异；（2）实际低分辨率（LR）qMRI和下采样预测之间的差异。这种基于物理信息的目标使得模型能够在没有高分辨率qMRI监督的情况下，从临床wMRI中学习。 为了验证这一概念的有效性，我们生成训练数据的方法是：通过信号方程从HR qMRI合成wMRI引导，然后通过对k空间截断来降低qMRI的分辨率。深度神经网络学习超分辨率映射。消融实验表明，T1加权图像主要增强T1图谱，T2加权图像改善T2图谱，而联合指导则最优地同时增强了所有参数。 在独立获得的不同qMRI序列的体内数据上进行验证，证明了模型具有跨qMRI序列的泛化能力。基于合成数据训练得到的模型可以从一个1分钟采集的数据中生成与5分钟参考扫描质量相当的超分辨率图谱，这得益于弛豫参数不受扫描仪限制的特性。 通过将训练过程从高分辨率qMRI需求中解耦出来，我们的框架能够利用常规临床图像来加速qMRI采集。这一方法提供了一条实用路径，可以在可接受的额外扫描时间内，将定量松弛测量集成到临床工作流程中。
URL
https://arxiv.org/abs/2512.17612
