Aganj Iman, Reuter Martin, Sabuncu Mert R, Fischl Bruce
Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, 149, 13th St., Room 2301, Charlestown, MA 02129, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Harvard Medical School, 149, 13th St., Room 2301, Charlestown, MA 02129, USA; Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139, USA.
Neuroimage. 2015 Feb 1;106:238-51. doi: 10.1016/j.neuroimage.2014.10.059. Epub 2014 Oct 30.
The choice of a reference image typically influences the results of deformable image registration, thereby making it asymmetric. This is a consequence of a spatially non-uniform weighting in the cost function integral that leads to general registration inaccuracy. The inhomogeneous integral measure--which is the local volume change in the transformation, thus varying through the course of the registration--causes image regions to contribute differently to the objective function. More importantly, the optimization algorithm is allowed to minimize the cost function by manipulating the volume change, instead of aligning the images. The approaches that restore symmetry to deformable registration successfully achieve inverse-consistency, but do not eliminate the regional bias that is the source of the error. In this work, we address the root of the problem: the non-uniformity of the cost function integral. We introduce a new quasi-volume-preserving constraint that allows for volume change only in areas with well-matching image intensities, and show that such a constraint puts a bound on the error arising from spatial non-uniformity. We demonstrate the advantages of adding the proposed constraint to standard (asymmetric and symmetrized) demons and diffeomorphic demons algorithms through experiments on synthetic images, and real X-ray and 2D/3D brain MRI data. Specifically, the results show that our approach leads to image alignment with more accurate matching of manually defined neuroanatomical structures, better tradeoff between image intensity matching and registration-induced distortion, improved native symmetry, and lower susceptibility to local optima. In summary, the inclusion of this space- and time-varying constraint leads to better image registration along every dimension that we have measured it.
参考图像的选择通常会影响可变形图像配准的结果，从而使其具有不对称性。这是成本函数积分中空间加权不均匀的结果，会导致一般的配准不准确。不均匀的积分度量——即变换中的局部体积变化，因此在配准过程中会发生变化——导致图像区域对目标函数的贡献不同。更重要的是，优化算法被允许通过操纵体积变化来最小化成本函数，而不是对齐图像。使可变形配准恢复对称性的方法成功地实现了反向一致性，但并没有消除作为误差来源的区域偏差。在这项工作中，我们解决了问题的根源：成本函数积分的不均匀性。我们引入了一种新的准体积保持约束，该约束仅允许在图像强度匹配良好的区域发生体积变化，并表明这种约束限制了由空间不均匀性引起的误差。我们通过对合成图像、真实X射线和2D/3D脑MRI数据进行实验，展示了将所提出的约束添加到标准（不对称和对称化）恶魔算法和微分同胚恶魔算法中的优势。具体而言，结果表明我们的方法能够实现图像对齐，更准确地匹配手动定义的神经解剖结构，在图像强度匹配和配准引起的失真之间实现更好的权衡，改善原始对称性，并降低对局部最优的敏感性。总之，包含这种时空变化的约束会在我们测量的每个维度上带来更好的图像配准效果。
