发表机构
University of Texas at Arlington; New York University Grossman School of Medicine（德克萨斯大学阿灵顿分校; 纽约大学格罗斯曼医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对临床心脏MRI平面间分辨率粗糙的问题，提出STRMSR框架，利用参考视图和中间结果重建高分辨率心脏容积，通过粗到细匹配、动态特征聚合等方法，在WHS数据集实验中，相比基线在不同上采样因子下有一致改进。
AI 中文摘要
临床心脏MRI通常具有高平面内分辨率但平面间分辨率粗糙，这限制了3D分析和诊断准确性。我们提出了STRMSR，一个基于参考和记忆引导的平面间超分辨率框架，通过利用同一受试者的高分辨率参考视图和中间超分辨率结果作为记忆来重建高分辨率心脏容积。我们的方法使用从粗到细的上下文匹配来在空间未对齐下建立低分辨率目标与参考/记忆图像之间的稳健对应关系。一个可学习的逐块动态特征聚合模块预测每个局部块的内容自适应混合权重，有效融合动态信息同时抑制不可靠特征传递。存储在记忆库中的中间超分辨率结果确保了超分辨3D容积的切片间一致性。在WHS心脏MRI数据集上基于两种参考协议（正交平面视图和长轴腔室视图）的实验表明，在4倍和8倍上采样因子下相对于基线有一致的改进。
英文摘要
Clinical cardiac MRI is commonly acquired with high in-plane resolution but coarse through-plane resolution to reduce scan time and accommodate breath-hold and cardiac-motion constraints, which limits 3D analysis and diagnostic accuracy. We propose STRMSR, a reference- and memory-guided through-plane super-resolution (SR) framework that reconstructs high-resolution (HR) cardiac volumes by leveraging HR reference views acquired from the same subject and intermediate SR results as the memory. Our method uses coarse-to-fine contextual matching to establish robust correspondence between low-resolution target and reference/memory images under spatial misalignment. A learnable patch-wise dynamic feature aggregation module predicts content-adaptive mixture weights for each local patch, effectively fusing dynamic information while suppressing unreliable feature transfers. The intermediate SR results stored in the memory bank ensure slice-to-slice consistency for the super-resolved 3D volume. Experiments on the WHS cardiac MRI dataset under two reference protocols, orthogonal-plane views and long-axis chamber views, demonstrate consistent improvements over baselines at 4x and 8x upsampling factors.Code is available at https://github.com/030108ming/STRMSR
Comments8 pages, 3 figures 2 tables (accepted In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop STACOM, 2026 (oral))
