Mu Zhiping, Fu Dongshan, Kuduvalli Gopinath
Accuray Incorporated, 1310 Chesapeake Terrace, Sunnyvale, CA 94089, USA.
IEEE Trans Med Imaging. 2008 Sep;27(9):1288-300. doi: 10.1109/TMI.2008.922693.
Fiducial tracking is a common target tracking method widely used in image-guided procedures such as radiotherapy and radiosurgery. In this paper, we present a multifiducial identification method that incorporates context information in the process. We first convert the problem into a state sequence problem by establishing a probabilistic framework based on a hidden Markov model (HMM), where prior probability represents an individual candidate's resemblance to a fiducial; transition probability quantifies the similarity of a candidate set to the fiducials' geometrical configuration; and the Viterbi algorithm provides an efficient solution. We then discuss the problem of identifying fiducials using stereo projections, and propose a special, higher order HMM, which consists of two parallel HMMs, connected by an association measure that captures the inherent correlation between the two projections. A novel algorithm, the concurrent viterbi with association (CVA) algorithm, is introduced to efficiently identify fiducials in the two projections simultaneously. This probabilistic framework is highly flexible and provides a buffer to accommodate deformations. A simple implementation of the CVA algorithm is presented to evaluate the efficacy of the framework. Experiments were carried out using clinical images acquired during patient treatments, and several examples are presented to illustrate a variety of clinical situations. In the experiments, the algorithm demonstrated a large tracking range, computational efficiency, ease of use, and robustness that meet the requirements for clinical use.
基准跟踪是一种常见的目标跟踪方法，广泛应用于放射治疗和放射外科等图像引导手术中。在本文中，我们提出了一种在过程中纳入上下文信息的多基准识别方法。我们首先通过基于隐马尔可夫模型（HMM）建立概率框架，将问题转化为状态序列问题，其中先验概率表示单个候选对象与基准的相似性；转移概率量化候选集与基准几何配置的相似性；维特比算法提供了一种有效的解决方案。然后，我们讨论了使用立体投影识别基准的问题，并提出了一种特殊的高阶HMM，它由两个并行的HMM组成，通过一种关联度量连接，该关联度量捕获两个投影之间的内在相关性。引入了一种新颖的算法，即带关联的并发维特比（CVA）算法，以同时有效地识别两个投影中的基准。这种概率框架具有高度的灵活性，并提供了一个缓冲来适应变形。给出了CVA算法的一个简单实现，以评估该框架的有效性。使用患者治疗期间获取的临床图像进行了实验，并给出了几个例子来说明各种临床情况。在实验中，该算法展示了满足临床使用要求的大跟踪范围、计算效率、易用性和鲁棒性。
