DRIFT: Difficulty-aware Rectified Flows for Through-plane MRI Super-Resolution

• URL: http://arxiv.org/abs/2607.16649v1
• Date: Sat, 18 Jul 2026 05:47:12 GMT
• Title: DRIFT: Difficulty-aware Rectified Flows for Through-plane MRI Super-Resolution
• Abstract summary: DRIFT is a two-stage thickness-conditioned rectified flow framework for through-plane MRI super-resolution.<n>We show that DRIFT outperforms super-resolution baselines while reducing inference cost.
• Score: 31.418804762940436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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