Rohlfing Torsten, Maurer Calvin R, Bluemke David A, Jacobs Michael A
Image Guidance Laboratories, Department of Neurosurgery, Stanford University, Stanford, CA 94305-5327, USA.
IEEE Trans Med Imaging. 2003 Jun;22(6):730-41. doi: 10.1109/TMI.2003.814791.
In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An information theoretic measure, normalized mutual information, is used as an intensity-based image similarity measure. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure and a regularization term. The novel regularization term is a local volume-preservation (incompressibility) constraint, which is motivated by the assumption that soft tissue is incompressible for small deformations and short time periods. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. We apply the nonrigid registration algorithm with and without the incompressibility constraint to precontrast and post-contrast magnetic resonance (MR) breast images from 17 patients. Without using a constraint, the volume of contrast-enhancing lesions decreases by 1%-78% (mean 26%). Image improvement (motion artifact reduction) obtained using the new constraint is compared with that obtained using a smoothness constraint based on the bending energy of the coordinate grid by blinded visual assessment of maximum intensity projections of subtraction images. For both constraints, volume preservation improves, and motion artifact correction worsens, as the weight of the constraint penalty term increases. For a given volume change of the contrast-enhancing lesions (2% of the original volume), the incompressibility constraint reduces motion artifacts better than or equal to the smoothness constraint in 13 out of 17 cases (better in 9, equal in 4, worse in 4). The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.
在本文中，我们通过使用一种新颖的正则化项来约束变形，扩展了先前报道的基于强度的非刚性配准算法。全局运动由刚性变换建模，而局部运动由基于B样条的自由形式变形描述。一种信息论度量，即归一化互信息，被用作基于强度的图像相似性度量。通过搜索使由图像相似性度量和正则化项的加权组合构成的代价函数最小化的变形来执行配准。新颖的正则化项是局部体积保持（不可压缩性）约束，其基于软组织在小变形和短时间段内不可压缩的假设。通过惩罚变形的雅可比行列式与单位矩阵的偏差来实现不可压缩性约束。我们将具有和不具有不可压缩性约束的非刚性配准算法应用于17名患者的对比剂增强前和对比剂增强后的磁共振（MR）乳腺图像。在不使用约束的情况下，对比剂增强病变的体积减少1% - 78%（平均26%）。通过对减法图像的最大强度投影进行盲法视觉评估，将使用新约束获得的图像改善（运动伪影减少）与使用基于坐标网格弯曲能量的平滑约束获得的图像改善进行比较。对于这两种约束，随着约束惩罚项的权重增加，体积保持得到改善，而运动伪影校正变差。对于对比剂增强病变的给定体积变化（原始体积的2%），在17个病例中的13个病例中，不可压缩性约束减少运动伪影的效果优于或等于平滑约束（9个病例中更好，4个病例中相等，4个病例中更差）。初步结果表明，纳入不可压缩性正则化项通过大大减少对比剂增强结构的收缩问题，同时大幅减少运动伪影，改善了基于强度的自由形式非刚性配准对比剂增强MR乳腺图像的效果。
