让医学影像配准更符合群体解剖特征，提升结果的生物学合理性。
MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
- 基于对变形场的主对数分解，构建保持微分同胚性质的线性潜在空间
- 在OASIS-1数据集上实现更解剖合理、计算高效且统计可解释的配准结果
- 适合关注群体形态学分析与高保真图像配准的研究者使用
捕捉群体水平形态统计信息的空间变换对医学图像分析至关重要。现有平滑正则化方法未能整合群体统计特征，导致解剖不一致的形变；逆一致性正则化虽保证几何一致性，但缺乏群体形态学集成。基于低维流形的方法虽部分解决此问题，却牺牲可解释性并忽略微分同胚特性（如群组合性与逆一致性）。本文提出MORPH-LER，一种面向群体感知的无监督图像配准的对数欧氏正则化框架。该框架从空间变换中学习群体形态特征，指导并正则化配准网络，确保解剖合理的形变。其核心为瓶颈自编码器，通过迭代平方根预测计算形变场的主对数，生成尊重微分同胚性质的线性化潜在空间，并强制实现逆一致性。结合配准网络与微分同胚自编码器，MORPH-LER生成平滑且有意义的形变场。主要贡献：(1) 数据驱动的正则化策略，融合群体解剖统计以提升变换有效性；(2) 线性化潜在空间，实现紧凑可解释的形变场，支持高效群体形态学分析。在两类深度学习配准网络上验证，MORPH-LER在OASIS-1脑影像数据集上展现出解剖准确、计算高效、统计有意义的配准性能。
原文摘要 · Abstract (English)
Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness regularizers for image registration fail to integrate population statistics, leading to anatomically inconsistent transformations. Inverse consistency regularizers promote geometric consistency but lack population morphometrics integration. Regularizers that constrain deformation to low-dimensional manifold methods address this. However, they prioritize reconstruction over interpretability and neglect diffeomorphic properties, such as group composition and inverse consistency. We introduce MORPH-LER, a Log-Euclidean regularization framework for population-aware unsupervised image registration. MORPH-LER learns population morphometrics from spatial transformations to guide and regularize registration networks, ensuring anatomically plausible deformations. It features a bottleneck autoencoder that computes the principal logarithm of deformation fields via iterative square-root predictions. It creates a linearized latent space that respects diffeomorphic properties and enforces inverse consistency. By integrating a registration network with a diffeomorphic autoencoder, MORPH-LER produces smooth, meaningful deformation fields. The framework offers two main contributions: (1) a data-driven regularization strategy that incorporates population-level anatomical statistics to enhance transformation validity and (2) a linearized latent space that enables compact and interpretable deformation fields for efficient population morphometrics analysis. We validate MORPH-LER across two families of deep learning-based registration networks, demonstrating its ability to produce anatomically accurate, computationally efficient, and statistically meaningful transformations on the OASIS-1 brain imaging dataset. https://github.com/iyerkrithika21/MORPH_LER
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