Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
IEEE Trans Med Imaging. 2012 Apr;31(4):860-9. doi: 10.1109/TMI.2011.2171498. Epub 2011 Oct 13.
A novel algorithm is presented to segment and reconstruct injected bone cement from a sparse set of X-ray images acquired at arbitrary poses. The sparse X-ray multi-view active contour (SxMAC-pronounced "smack") can 1) reconstruct objects for which the background partially occludes the object in X-ray images, 2) use X-ray images acquired on a noncircular trajectory, and 3) incorporate prior computed tomography (CT) information. The algorithm's inputs are preprocessed X-ray images, their associated pose information, and prior CT, if available. The algorithm initiates automated reconstruction using visual hull computation from a sparse number of X-ray images. It then improves the accuracy of the reconstruction by optimizing a geodesic active contour. Experiments with mathematical phantoms demonstrate improvements over a conventional silhouette based approach, and a cadaver experiment demonstrates SxMAC's ability to reconstruct high contrast bone cement that has been injected into a femur and achieve sub-millimeter accuracy with four images.
提出了一种新的算法，用于从任意姿势采集的稀疏 X 射线图像中分割和重建注入的骨水泥。稀疏 X 射线多视图主动轮廓（SxMAC-发音为“smack”）可以 1）重建部分遮挡 X 射线图像中物体的背景的物体，2）使用非圆形轨迹采集的 X 射线图像，以及 3）合并先前计算的断层扫描（CT）信息。该算法的输入是预处理后的 X 射线图像、相关的姿势信息以及可用的先验 CT。该算法使用来自稀疏数量的 X 射线图像的视觉外壳计算自动启动重建。然后，通过优化测地线主动轮廓来提高重建的准确性。数学幻影实验表明，该方法优于传统的基于轮廓的方法，尸体实验表明 SxMAC 能够重建已注入股骨的高对比度骨水泥，并使用四张图像实现亚毫米精度。
