King Andrew P, Ma Ying-Liang, Yao Cheng, Jansen Christian, Razavi Reza, Rhode Kawal S, Penney Graeme P
Division of Imaging Sciences, King's College London, UK.
Inf Process Med Imaging. 2009;21:188-201. doi: 10.1007/978-3-642-02498-6_16.
We present a technique for automatic intensity-based image-to-physical registration of a 3-D segmentation for image-guided interventions. The registration aligns the segmentation with tracked and calibrated 3-D ultrasound (US) images of the target region. The technique uses a probabilistic framework and explicitly incorporates a model of the US image acquisition process. The rigid body registration parameters are varied to maximise the likelihood that the real US image(s) were formed using the US imaging model from the probe transducer position. The proposed technique is validated on images segmented from cardiac magnetic resonance imaging (MRI) data and 3-D US images acquired from 3 volunteers and 1 patient. We show that the accuracy of the algorithm is 2.6-4.2mm and the capture range is 9-18mm. The proposed technique has the potential to provide accurate image-to-physical registrations for a range of image guidance applications.
我们提出了一种用于图像引导介入的三维分割基于强度的自动图像到物理配准技术。该配准将分割结果与目标区域的跟踪和校准后的三维超声（US）图像对齐。该技术使用概率框架，并明确纳入了超声图像采集过程的模型。通过改变刚体配准参数，以最大化使用超声成像模型从探头换能器位置形成真实超声图像的可能性。所提出的技术在从心脏磁共振成像（MRI）数据分割的图像以及从3名志愿者和1名患者获取的三维超声图像上得到了验证。我们表明该算法的精度为2.6 - 4.2毫米，捕获范围为9 - 18毫米。所提出的技术有可能为一系列图像引导应用提供准确的图像到物理配准。
