Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions
Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions
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DOI：
10.1109/tmi.2015.2440253
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发表时间：
2015-11-01
影响因子：
10.6
通讯作者：
Abolmaesumi, Purang
中科院分区：
文献类型：
--
作者：
Khallaghi, Siavash;Sanchez, C. Antonio;Abolmaesumi, Purang
在用于图像引导介入的基于表面的配准中，缺失数据的存在可能是一个重要问题。这通常会出现在实时成像模式中，例如超声，其中对比度差会使组织边界难以与周围组织区分开。缺失数据带来了两个挑战：建立对应关系的模糊性;以及将变形场外推到这些缺失区域。为了解决这些问题，我们提出了一种新的非刚性配准方法。为了建立对应关系，我们使用基于高斯混合模型（GMM）的概率框架，该模型将一个表面视为潜在的部分观测。为了外推和约束变形场，我们将生物力学先验知识以有限元模型（FEM）的形式结合起来。我们验证的算法，称为GMM-FEM，在前列腺介入的背景下。我们的方法导致一个显着减少目标配准误差（TRE）相比，类似的国家的最先进的配准算法的情况下，丢失的数据高达30%，平均TRE为2.6毫米。该方法也表现良好时，完整的分割是可用的，导致TRE是可比的或优于其他基于表面的技术。我们还分析了我们的方法的鲁棒性，表明GMM-FEM是一个实用和可靠的解决方案，基于表面的注册。
In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.