Hawkes D J, Barratt D, Blackall J M, Chan C, Edwards P J, Rhode K, Penney G P, McClelland J, Hill D L G
Division of Imaging Sciences, GKT School of Medicine, King's College London, UK.
Med Image Anal. 2005 Apr;9(2):163-75. doi: 10.1016/j.media.2004.11.007. Epub 2004 Dec 28.
This paper promotes the concept of active models in image-guided interventions. We outline the limitations of the rigid body assumption in image-guided interventions and describe how intraoperative imaging provides a rich source of information on spatial location of anatomical structures and therapy devices, allowing a preoperative plan to be updated during an intervention. Soft tissue deformation and variation from an atlas to a particular individual can both be determined using non-rigid registration. Established methods using free-form deformations have a very large number of degrees of freedom. Three examples of deformable models--motion models, biomechanical models and statistical shape models--are used to illustrate how prior information can be used to restrict the number of degrees of freedom of the registration algorithm and thus provide active models for image-guided interventions. We provide preliminary results from applications for each type of model.
本文推广了图像引导介入中主动模型的概念。我们概述了图像引导介入中刚体假设的局限性,并描述了术中成像如何提供有关解剖结构和治疗设备空间位置的丰富信息源,从而使术前计划在介入过程中得以更新。软组织变形以及从图谱到特定个体的变异都可以通过非刚性配准来确定。使用自由形式变形的既定方法具有非常多的自由度。三种可变形模型的示例——运动模型、生物力学模型和统计形状模型——用于说明如何利用先验信息来限制配准算法的自由度数量,从而为图像引导介入提供主动模型。我们给出了每种模型应用的初步结果。