基于解剖引导伪配对与对抗扩散的脊柱 MRI 到 CT 图像转换方法
MRI-to-CT Translation of Spinal Images via Anatomy-Guided Pseudo-Pairing and Adversarial Diffusion
AI 导读
研究者提出三阶段脊柱 MRI 到 CT 合成框架：先用解剖引导的椎体匹配从非配对扫描构建伪配对数据，再训练对抗扩散模型进行图像转换，最后用 3D 网络优化切片间连续性。该框架在超过 10,000 张非配对矢状位切片上训练，在 83 个 MRI 体数据的 1,911 张切片上评估，将 3D 椎体 Dice 从 0.666 提升至 0.739，有助于术前规划中减少额外辐射暴露。
正文 · 原文
Abstract:Magnetic Resonance Imaging (MRI) is routinely used to diagnose spinal disorders, but surgical planning often requires Computed Tomography (CT) for better visualization of vertebral bone anatomy. Synthesizing CT from MRI could avoid additional radiation exposure and streamline preoperative assessment.
We propose a three-stage framework for spine MRI-to-CT synthesis. First, an anatomy-guided vertebral matching system identifies similar sagittal MRI and CT slices using spine segmentations and aligns them through rigid registration, creating pseudo-paired data from unpaired scans. Second, these pairs train an adversarial diffusion model for MRI-to-CT translation. Third, synthesized slices are assembled into 3D volumes and refined by a network that improves inter-slice continuity and structural coherence.
The framework was trained on over 10,000 unpaired sagittal MRI and CT slices and evaluated on 1,911 slices from 83 MRI volumes spanning all spinal regions. Assessment combined distribution-based measures with vertebral segmentation metrics. Compared with diffusion alone, the proposed approach increased binary 3D vertebral Dice from 0.666 to 0.739. Volumetric post-processing reduced visible discontinuities between stacked slices and improved vertebral coherence in axial and coronal views.
Combining anatomy-guided pseudo-pairing, adversarial diffusion and 3D refinement improves spine MRI-to-CT synthesis. Vertebral matching primarily enhanced anatomical alignment and fidelity, while volumetric refinement improved 3D consistency. These findings support further development of MRI-derived CT-like visualization for preoperative planning, particularly to preserve fine intensity details while maintaining volumetric coherence.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37134 [cs.CV] |
| (or arXiv:2610.37134v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37134 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Karen López-Linares Román [view email]
[v1]
Tue, 27 Oct 2026 12:32:47 UTC (10,716 KB)
来源：arXiv cs.AI · arxiv.org