arXiv cs.AI· Eduardo Nieto, Ibai Genua, Davide Scorza, Karen L\'opez-Linares·· 13 小时前AI 评分31
基于解剖引导伪配对与对抗扩散的脊柱 MRI 到 CT 转换
MRI-to-CT Translation of Spinal Images via Anatomy-Guided Pseudo-Pairing and Adversarial Diffusion
AI 导读
研究提出三阶段框架实现脊柱 MRI 到 CT 合成：先用脊柱分割与刚性配准从无配对扫描构建伪配对数据，再训练对抗扩散模型完成转换，最后将合成切片组装为 3D 体并做层间连续性精修。框架在超 10,000 张无配对矢状位 MRI 与 CT 切片上训练，并在 83 例 MRI 体数据的 1,911 张切片上评估，二值 3D 椎体 Dice 从单用扩散模型的 0.666 提升至 0.739。
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