A simple fixed-point approach to invert a deformation field

• Published in: Medical physics (Lancaster) Vol. 35; no. 1; pp. 81 - 88
• Main Authors: Chen, Mingli, Lu, Weiguo, Chen, Quan, Ruchala, Kenneth J., Olivera, Gustavo H.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01-01-2008
• Subjects: Algorithms; deformable registration; fixed-point; Humans; Imaging, Three-Dimensional - methods; inverse; Lung - diagnostic imaging; Tomography, X-Ray Computed
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.2816107

Inversion of deformation fields is applied frequently to map images, dose, and contours between the reference frame and the study frame. A prevailing approach that takes the negative of the forward deformation as the inverse deformation is oversimplified and can cause large errors for large deformations or deformations that are composites of several deformations. Other approaches, including Newton's method and scatter data interpolation, either require the first derivative or are very inefficient. Here we propose an iterative approach that is easy to implement, converges quickly to the inverse when it does, and works for a majority of cases in practice. Our approach is rooted in fixed-point theory. We build a sequence to approximate the inverse deformation through iterative evaluation of the forward deformation. A sufficient but not necessary convergence condition (Lipschitz condition) and its proof are also given. Though this condition guarantees the convergence, it may not be met for an arbitrary deformation field. One should always check whether the inverse exists for the given forward deformation field by calculating its Jacobian. If nonpositive values of the Jacobian occur only for few voxels, this method will usually converge to a pseudoinverse. In case the iteration fails to converge, one should switch to other means of finding the inverse. We tested the proposed method on simulated 2D data and real 3D computed tomography data of a lung patient and compared our method with two implementations in the Insight Segmentation and Registration Toolkit (ITK). Typically less than ten iterations are needed for our method to get an inverse deformation field with clinically relevant accuracy. Based on the test results, our method is about ten times faster and yet ten times more accurate than ITK's iterative method for the same number of iterations. Simulations and real data tests demonstrated the efficacy and the accuracy of the proposed algorithm.