一种用于MRI-US匹配和配准的3D跨模态关键点描述符
A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration
AI总结：
本文提出一种3D跨模态关键点描述符，通过合成iUS体积进行患者特异性匹配，学习共享描述空间，实现鲁棒且旋转不变的配准。
摘要：
术中将实时超声（iUS）与术前磁共振成像（MRI）配准仍因模态差异严重而未解决。为此，我们提出了一种新的3D跨模态关键点描述符用于MRI-iUS匹配和配准。我们的方法采用患者特异性匹配-by-synthesis方法，从术前MRI生成合成iUS体积。这使得能够通过监督对比训练学习共享描述空间。随后采用概率关键点检测策略来识别解剖学显著且模态一致的点。在训练过程中，使用基于课程的三元组损失和动态难负样本挖掘来学习对iUS伪影（如斑点噪声和有限覆盖）鲁棒且旋转不变的描述符。在推理过程中，该方法在MRI和真实iUS图像中检测关键点并识别稀疏匹配，然后用于刚体配准。我们的方法在ReMIND数据集上进行了评估。实验表明，我们的方法在11名患者上优于现有关键点匹配方法，平均精度为69.8%。对于图像配准，我们的方法在ReMIND2Reg基准上实现了具有竞争力的平均目标配准误差为2.39毫米。与现有iUS-MR配准方法相比，我们的框架具有可解释性，不需要手动初始化，并且对iUS视野变化具有鲁棒性。代码、数据和模型权重可在https://github.com/morozovdd/CrossKEY上获得。
英文摘要：
Intraoperative registration of real-time ultrasound (iUS) to preoperative Magnetic Resonance Imaging (MRI) remains an unsolved problem due to severe modality-specific differences in appearance, resolution, and field-of-view. To address this, we propose a novel 3D cross-modal keypoint descriptor for MRI-iUS matching and registration. Our approach employs a patient-specific matching-by-synthesis approach, generating synthetic iUS volumes from preoperative MRI. This enables supervised contrastive training to learn a shared descriptor space. A probabilistic keypoint detection strategy is then employed to identify anatomically salient and modality-consistent locations. During training, a curriculum-based triplet loss with dynamic hard negative mining is used to learn descriptors that are i) robust to iUS artifacts such as speckle noise and limited coverage, and ii) rotation-invariant. At inference, the method detects keypoints in MR and real iUS images and identifies sparse matches, which are then used to perform rigid registration. Our approach is evaluated using 3D MRI-iUS pairs from the ReMIND dataset. Experiments show that our approach outperforms state-of-the-art keypoint matching methods across 11 patients, with an average precision of 69.8%. For image registration, our method achieves a competitive mean Target Registration Error of 2.39 mm on the ReMIND2Reg benchmark. Compared to existing iUS-MR registration approaches, our framework is interpretable, requires no manual initialization, and shows robustness to iUS field-of-view variation. Code, data and model weights are available at https://github.com/morozovdd/CrossKEY.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
