发表机构
Yonsei University; King Fahd University of Petroleum & Minerals (KFUPM); University of California at Merced（延世大学; 法赫德国王石油与矿业大学; 加州大学默塞德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对平面内MRI超分辨率中效率与保真度的权衡问题，提出DRIFT框架，通过两阶段厚度条件整流流，利用解剖投影网络初始化，引入物理感知难度度量指导自适应积分调度器，降低推理成本并优于超分辨率基线。
AI 中文摘要
磁共振成像（MRI）通常采用各向异性分辨率采集以减少扫描时间，在平面内方向会产生阶梯状伪影。在平面内MRI超分辨率中，存在效率与保真度的权衡：前馈回归器速度快，但在大切片厚度时过度平滑，而基于采样的方法虽能提高保真度，但推理成本高。我们提出DRIFT，这是一种用于平面内MRI超分辨率的两阶段厚度条件整流流框架，输入切片厚度连续。第一阶段使用解剖投影网络（APN）将低分辨率补丁映射到粗高分辨率流形，提供确定性解剖初始化以缩短第二阶段的剩余传输并稳定逐片细化。第二阶段通过整流流细化细节，并引入基于切片厚度引起的平面内带宽不足的物理感知难度（PAD）度量来指导自适应积分调度器（AIS），按厚度分配常微分方程步骤。一致性端点轨迹对齐（CETA）损失强制实现厚度一致的重建。实验表明DRIFT在降低推理成本的同时优于超分辨率基线。代码、模型和交互式演示可在该https网址获取。
英文摘要
Magnetic Resonance Imaging (MRI) is often acquired with anisotropic resolution to reduce scan time, producing stair-step artifacts along the through-plane direction. In through-plane MRI super-resolution, an efficiency-fidelity trade-off arises: feed-forward regressors are fast but oversmooth at large slice-thicknesses, while sampling-based methods improve fidelity at high inference cost. We propose DRIFT, a two-stage thickness-conditioned rectified flow framework for through-plane MRI super-resolution with continuous input slice-thickness. Stage 1 employs an Anatomical Projection Network (APN) to map low-resolution patches to a coarse high-resolution manifold, providing a deterministic anatomical initialization that shortens the residual transport of Stage 2 and stabilizes slice-wise refinement. Stage 2 refines details via rectified flow and introduces a Physics-Aware Difficulty (PAD) metric derived from slice-thickness induced through-plane bandwidth deficit to guide an Adaptive Integration Scheduler (AIS), allocating ODE steps by thickness. A Consistent Endpoint Trajectory Alignment (CETA) loss enforces thickness-consistent reconstructions. Experiments show that DRIFT outperforms super-resolution baselines while reducing inference cost. Code, models, and interactive demos are available at https://yoonseokchoi-ai.github.io/drift-eccv2026/.
