综述深度学习从2D MRI重建3D解剖结构的四种主流方法
From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Review
- 按点云、网格、形状感知、体素四类梳理重建方法
- 覆盖心脏、神经、肺等多部位，分析临床适用性与数据影响
- 适合医学影像、AI建模与临床转化方向的研究者参考
基于深度学习的二维磁共振成像（2D MRI）到三维（3D）解剖结构重建，在疾病诊断、治疗规划和计算建模中日益重要。本文综述了3D MRI重建的方法学全景，聚焦四大主流技术：点云、网格、形状感知和体素模型。针对每类方法，分析其前沿技术、理论基础、局限性及在心脏、神经、肺等解剖结构中的应用。涵盖训练与测试数据对模型临床适用性的影响，评估公开数据集、计算需求与评价指标。最后展望多模态融合与跨模态框架等新兴方向。旨在为研究者提供系统性视角，推动深度学习在更鲁棒、可泛化和临床有效的方向发展。
原文摘要 · Abstract (English)
Deep learning-based 3-dimensional (3D) shape reconstruction from 2-dimensional (2D) magnetic resonance imaging (MRI) has become increasingly important in medical disease diagnosis, treatment planning, and computational modeling. This review surveys the methodological landscape of 3D MRI reconstruction, focusing on 4 primary approaches: point cloud, mesh-based, shape-aware, and volumetric models. For each category, we analyze the current state-of-the-art techniques, their methodological foundation, limitations, and applications across anatomical structures. We provide an extensive overview ranging from cardiac to neurological to lung imaging. We also focus on the clinical applicability of models to diseased anatomy, and the influence of their training and testing data. We examine publicly available datasets, computational demands, and evaluation metrics. Finally, we highlight the emerging research directions including multimodal integration and cross-modality frameworks. This review aims to provide researchers with a structured overview of current 3D reconstruction methodologies to identify opportunities for advancing deep learning towards more robust, generalizable, and clinically impactful solutions.
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