Grid powered nonlinear image registration with locally adaptive regularization

• Published in: Medical image analysis Vol. 8; no. 3; pp. 325 - 342
• Main Authors: Stefanescu, Radu, Pennec, Xavier, Ayache, Nicholas
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2004; Elsevier
• Subjects: Adaptive regularization; Algorithms; Bioengineering; Brain - anatomy & histology; Brain atlas; Brain Mapping - methods; Computer Graphics; Computer Science; Engineering Sciences; Grid computing; Humans; Image Processing, Computer-Assisted - methods; Image registration; Imaging; Life Sciences; Magnetic Resonance Imaging; Medical Imaging; Modeling and Simulation; Multi-subject image fusion; Non-rigid transformation; Nonlinear diffusion; Parallel computing; Signal and Image processing; Image registration; Parallel computing; Multi-subject image fusion; Adaptive regularization; Brain atlas; Grid computing; Non-rigid transformation; Nonlinear diffusion; adaptive regularization; non-rigid registration; parallel; grid
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2004.06.010

Multi-subject non-rigid registration algorithms using dense deformation fields often encounter cases where the transformation to be estimated has a large spatial variability. In these cases, linear stationary regularization methods are not sufficient. In this paper, we present an algorithm that uses a priori information about the nature of imaged objects in order to adapt the regularization of the deformations. We also present a robustness improvement that gives higher weight to those points in images that contain more information. Finally, a fast parallel implementation using networked personal computers is presented. In order to improve the usability of the parallel software by a clinical user, we have implemented it as a grid service that can be controlled by a graphics workstation embedded in the clinical environment. Results on inter-subject pairs of images show that our method can take into account the large variability of most brain structures. The registration time for images of size 256 × 256 × 124 is 5 min on 15 standard PCs. A comparison of our non-stationary visco-elastic smoothing versus solely elastic or fluid regularizations shows that our algorithm converges faster towards a more optimal solution in terms of accuracy and transformation regularity.