Modelling Prostate Motion for Data Fusion During Image-Guided Interventions
Modelling Prostate Motion for Data Fusion During Image-Guided Interventions
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DOI：
10.1109/tmi.2011.2158235
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发表时间：
2011-11-01
影响因子：
10.6
通讯作者：
Barratt, Dean C.
中科院分区：
文献类型：
--
作者：
Hu, Yipeng;Carter, Timothy J.;Barratt, Dean C.
临床对图像配准技术的需求不断增长，这些技术允许多模态数据融合，以准确定位穿刺活检和消融性前列腺癌治疗。然而，在使用经直肠超声 (TRUS) 引导的手术过程中，由于 TRUS 探头压力，可能会发生明显的腺体变形。在本文中，研究了使用有限元模拟训练的统计形状/运动模型预测和补偿该运动源的能力。使用非刚性、基于表面的方法记录在 TRUS 探头引起变形之前和之后在 5 名患者前列腺上采集的三维超声图像，并比较不同变形模型的准确性。发现使用统计运动模型的配准在准确性和鲁棒性方面优于替代弹性变形方法，并且需要更少的目标表面点来实现成功配准。使用此方法的平均最终目标配准误差（基于解剖标志）为 1.8 毫米。我们得出的结论是，前列腺变形的统计模型提供了一种根据稀疏表面数据预测前列腺变形的准确、快速和稳健的方法，因此非常适合需要变形补偿的许多介入应用。
There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.