用AI从MRI自动重建儿童脊柱3D模型，免受辐射伤害
AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis
- 基于GAN合成MRI图像，结合U-Net实现全自动分割
- 分割准确率88% Dice，处理时间从1小时缩至1分钟
- 适合儿科脊柱侧弯患者临床评估与手术规划
MRI因无电离辐射被优先用于儿童影像，但其在脊柱畸形评估中受限于缺乏自动化高分辨率3D骨性重建，仍依赖CT。MRI-based 3D重建因手动流程及全脊柱标注数据稀缺而难以实用。本研究提出一种AI框架，可仅凭MRI实现胸腰段（T1-L5）的全自动分割与3D重建。通过生成对抗网络（GAN）将既往低剂量CT扫描转换为类MRI图像，并与现有胸段MRI标注数据联合训练基于U-Net的模型。该算法实现了连续胸腰段3D重建，分割准确率达88% Dice，处理时间由约1小时缩短至1分钟以内，同时保留了青少年特发性脊柱侧弯（AIS）的特异性畸形特征。该方法支持基于MRI的无辐射3D畸形评估，适用于临床评估、手术规划与导航。
原文摘要 · Abstract (English)
MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by the lack of automated, high-resolution 3D bony reconstruction, which continues to rely on CT. MRI-based 3D reconstruction remains impractical due to manual workflows and the scarcity of labelled full-spine datasets. This study introduces an AI framework that enables fully automated thoracolumbar spine (T1-L5) segmentation and 3D reconstruction from MRI alone. Historical low-dose CT scans from adolescent idiopathic scoliosis (AIS) patients were converted into MRI-like images using a GAN and combined with existing labelled thoracic MRI data to train a U-Net-based model. The resulting algorithm accurately generated continuous thoracolumbar 3D reconstructions, improved segmentation accuracy (88% Dice score), and reduced processing time from approximately 1 hour to under one minute, while preserving AIS-specific deformity features. This approach enables radiation-free 3D deformity assessment from MRI, supporting clinical evaluation, surgical planning, and navigation in paediatric spine care.
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