Chen Xin, Graham Jim, Hutchinson Charles, Muir Lindsay
ISBE, School of Cancer and Enabling Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):680-7. doi: 10.1007/978-3-642-23629-7_83.
We present a novel framework for inferring 3D carpal bone kinematics and bone shapes from a single view fluoroscopic sequence. A hybrid statistical model representing both the kinematics and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone kinematics and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences. It was found that the relative positions between carpal bones can be accurately estimated, which is potentially useful for detection of conditions such as scapholunate dissociation.
我们提出了一种新颖的框架,用于从单视角荧光透视序列推断三维腕骨运动学和骨骼形状。基于从不同受试者在不同姿势下获取的多个三维CT数据集,构建了一个同时表示腕骨运动学和形状变化的混合统计模型。给定一个荧光透视序列,通过将统计模型与二维图像进行匹配,迭代估计腕部姿势、腕骨运动学和骨骼形状。一个专门设计的代价函数能够在各帧之间进行平滑的参数估计。我们在模拟数据和真实荧光透视序列上对所提出的方法进行了评估。结果发现,腕骨之间的相对位置能够被准确估计,这对于检测诸如舟月骨分离等病症可能是有用的。