Low-Rank Velocity Fields as a Structural Prior for Unsupervised 4D Medical Image Interpolation

2026-08-24

AI Summary

为解决4D医学图像插值中边界不稳定和非生理运动问题，本文提出使用低秩速度场作为结构先验，通过分解运动为全局共享的空间基和紧凑的样本特定核心来抑制高频伪影，提高解剖结构的一致性和稳定性。

Abstract

Endpoint-only unsupervised 4D medical image interpolation synthesizes intermediate volumes from sparsely sampled sequences with only the start and end volumes available for training; however, this weakly constrained setting often yields intermediates with unstable boundaries and non-physiological motion, limiting interpretability and downstream analysis. We propose low-rank velocity fields as a structural prior, constraining motion to a structured Tucker low-rank velocity field space that decomposes motion into globally shared spatial bases and a compact sample-specific core, thereby encouraging spatially correlated, anatomy-consistent deformation while suppressing voxel-wise high-frequency artifacts. To capture global coordination and local non-rigid details, we model motion in a coarse-to-fine multi-scale scheme and compose scale-wise deformations at inference to synthesize volumes at arbitrary times. We further provide a theoretical analysis showing that, under Tucker parameterization, low-rank parameters control the smoothness energy of the velocity field, explaining why low-rank modeling promotes smoother motion. Experiments on ACDC and 4D-Lung demonstrate state-of-the-art performance, remaining competitive with methods trained with intermediate-frame supervision, and producing intermediates with improved structural coherence and more stable anatomical contours.

Problem

• unsupervised 4D medical image interpolation
• unstable boundaries
• non-physiological motion

Innovation

• Low-rank velocity fields
• Tucker low-rank decomposition
• Unsupervised 4D medical image interpolation
• Spatially correlated deformation
• Multi-scale scheme