用合成数据训练模型，提升纵向脑MRI刚性配准精度
Learning accurate rigid registration for longitudinal brain MRI from synthetic data
- 基于合成数据生成含刚性与微小非线性变换的配对图像进行训练
- 在跨模态纵向配准任务中表现优于以往跨被试网络，精度更高
- 适合需要高精度纵向脑部影像分析的研究者使用
刚性配准旨在确定使一对图像特征对齐所需的平移和旋转。尽管近年来机器学习方法在跨被试的线性与非线性配准中已达到领先水平，但在纵向（同被试）配准中仍存在局限，而精确对齐在此类任务中至关重要。本文在现有解剖感知、采集无关仿射配准框架基础上，提出一种专为纵向刚性脑部配准优化的模型。通过在包含刚性及细微非线性变换的合成同被试图像对上训练，该模型估计的刚性变换比以往跨被试网络更准确，并在同被试及跨磁共振成像（MRI）对比度的纵向配准对上表现出稳健性能。
原文摘要 · Abstract (English)
Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-art for linear and deformable registration across subjects, they have demonstrated limitations when applied to longitudinal (within-subject) registration, where achieving precise alignment is critical. Building on an existing framework for anatomy-aware, acquisition-agnostic affine registration, we propose a model optimized for longitudinal, rigid brain registration. By training the model with synthetic within-subject pairs augmented with rigid and subtle nonlinear transforms, the model estimates more accurate rigid transforms than previous cross-subject networks and performs robustly on longitudinal registration pairs within and across magnetic resonance imaging (MRI) contrasts.
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