用Mamba模型融合双模MRI生成高质量CT，无辐射且更精准
MM2CT: MR-to-CT translation for multi-modal image fusion with mamba
- 基于Mamba架构融合T1/T2加权MRI，实现多模态图像合成
- 在骨盆数据集上SSIM和PSNR均达当前最优水平
- 适合医学影像生成、无辐射成像研究者参考
磁共振（MR）到计算机断层扫描（CT）的转换可消除CT扫描的辐射暴露并减少因患者运动引起的成像伪影。现有方法多基于单模态MR-to-CT转换，对多模态融合的研究有限。为此，我们提出一种利用T1和T2加权MRI数据的多模态MR-to-CT（MM2CT）转换方法，采用创新的Mamba基础框架进行多模态医学图像合成。Mamba有效克服了传统CNN局部感受野受限和Transformer计算复杂度高的问题，兼具长程依赖建模能力与多模态特征融合效率。此外，引入动态局部卷积模块和动态增强模块以提升图像合成质量。在公开的骨盆数据集上的实验表明，MM2CT在结构相似性指数（SSIM）和峰值信噪比（PSNR）方面达到当前最优性能。代码已开源：https://github.com/Gots-ch/MM2CT。
原文摘要 · Abstract (English)
Magnetic resonance (MR)-to-computed tomography (CT) translation offers significant advantages, including the elimination of radiation exposure associated with CT scans and the mitigation of imaging artifacts caused by patient motion. The existing approaches are based on single-modality MR-to-CT translation, with limited research exploring multimodal fusion. To address this limitation, we introduce Multi-modal MR to CT (MM2CT) translation method by leveraging multimodal T1- and T2-weighted MRI data, an innovative Mamba-based framework for multi-modal medical image synthesis. Mamba effectively overcomes the limited local receptive field in CNNs and the high computational complexity issues in Transformers. MM2CT leverages this advantage to maintain long-range dependencies modeling capabilities while achieving multi-modal MR feature integration. Additionally, we incorporate a dynamic local convolution module and a dynamic enhancement module to improve MRI-to-CT synthesis. The experiments on a public pelvis dataset demonstrate that MM2CT achieves state-of-the-art performance in terms of Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). Our code is publicly available at https://github.com/Gots-ch/MM2CT.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。