用参考图和记忆机制提升心脏MRI的纵向分辨率，让3D分析更精准。
Cardiac MRI Through-Plane Super-Resolution Guided by Reference and Memory
- 通过参考图像与中间结果构建记忆，实现跨切面信息融合
- 在4倍和8倍上采样下，相比基线方法显著提升重建质量
- 适合临床心脏MRI后处理，尤其适用于受呼吸运动影响的扫描
临床心脏MRI通常采用高平面分辨率但粗略的纵向分辨率以缩短扫描时间并适应屏气和心脏运动限制，这限制了三维分析与诊断准确性。本文提出STRMSR，一种基于参考图与记忆引导的纵向超分辨率框架，通过同一受试者的高分辨率参考视图及中间超分辨率结果作为记忆，重建高分辨率心脏体积。方法采用粗到细的上下文匹配，在空间错位条件下建立低分辨率目标与参考/记忆图像间的鲁棒对应关系；引入可学习的局部动态特征聚合模块，为每个局部块预测内容自适应混合权重，有效融合动态信息并抑制不可靠特征传递；存储于记忆库中的中间超分辨率结果确保了超分辨三维体积的切片间一致性。在WHS心脏MRI数据集上，针对正交切面视图和长轴心腔视图两种参考协议，在4倍和8倍上采样因子下均实现对基线方法的一致性提升。代码已开源。
原文摘要 · Abstract (English)
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
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