用于4D医学图像插值的相位对齐有限傅里叶周期变形
Phase-Aligned Finite-Fourier Periodic Deformation for 4D Medical Image Interpolation
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中文总结 AI 辅助
该研究针对4D医学图像插值中周期结构编码不足、运动非均匀的问题，提出相位对齐有限傅里叶周期变形方法，在ACDC和4D-Lung数据集上实现了优于现有基线的性能。
中文摘要 AI 辅助
4D医学图像插值旨在从稀疏观测的时间点恢复缺失的体积数据，对心脏MRI、胸部CT等应用中的动态解剖分析至关重要，这类应用中运动在临床相关区间内通常是重复或接近周期性的。一个关键挑战是，这种结构并不总是直接编码在插值的变形表示中；此外，生理运动往往是非均匀的，因此相等的时间间隔不一定对应相等的解剖变化量。为解决这些问题，我们将插值建模为学习具有相位结构化先验的连续变形过程。给定两个端点体积，我们用有限傅里叶基参数化相位条件速度场，该基将近周期运动模式直接嵌入变形空间，并支持在任意目标时间进行连续查询。我们进一步引入相位对齐的时间重参数化，根据变形变化强度将区间内的归一化时间映射到潜在运动相位，从而更好地建模非均匀运动进展。中间体积通过对两个端点进行连续变形，再经双向融合和轻量级残差细化合成。在ACDC和4D-Lung数据集上的实验表明，所提方法相较于现有基线实现了最先进的性能，同时从稀疏观测中生成解剖学上合理且连贯的中间体积。
英文摘要
4D medical image interpolation aims to recover missing volumes from sparsely observed time points and is important for dynamic anatomical analysis in applications such as cardiac MRI and thoracic CT, where motion is often repetitive or near-periodic over clinically relevant intervals. A key challenge is that this structure is not always encoded directly in deformation representations for interpolation. In addition, physiological motion is often non-uniform, so equal temporal intervals do not necessarily correspond to equal amounts of anatomical change. To address these issues, we formulate interpolation as learning a continuous deformation process with a phase-structured prior. Given two endpoint volumes, we parameterize a phase-conditioned velocity field with a finite Fourier basis, which embeds near-periodic motion patterns directly into the deformation space and supports continuous querying at arbitrary target times. We further introduce a phase-aligned temporal reparameterization that maps normalized within-interval time to a latent motion phase according to deformation variation intensity, thereby better modeling non-uniform motion progression. Intermediate volumes are then synthesized by continuously warping both endpoints, followed by bidirectional fusion and lightweight residual refinement. Experiments on ACDC and 4D-Lung show that the proposed method achieves state-of-the-art performance over existing baselines while producing anatomically plausible and coherent intermediate volumes from sparse observations.
发表机构
- Southern University of Science and Technology（南方科技大学）
- Shenzhen University of Advanced Technology（深圳理工大学）
- Shenzhen University Medical School（深圳大学医学院）
- Shenzhen University（深圳大学）
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
