Co-registration is an important step in image analysis tasks where information is extracted from a combination of sources. Medicine (monitoring tumour growth, treatment verification, and the comparison of patient data with anatomical atlases) is currently the most prominent field of application [Sotiras 2013]. The major limitation associated with non-linear image co-registration is its high computational cost and long running time >24 hours. As a result, it has limited application where fast execution times are required.
Non-linear deformable registration is based on the assumption that evenly spaced meshes of control points can be placed over fixed and moving images. In order for registration to be performed, each moving control point plus underlying image intensities are transformed and compared with their fixed counterpart until an acceptable level of similarity is achieved.
Such algorithms have been successfully hosted in tightly-coupled parallel architectures by assigning subsets of control points to individual cores. Conveniently, the use of free-form deformations [Loeckx 2004] permits the independent movement of control points. Similarly, spline-based representations [ITK 2017], where each control point has localised influence resulting in the movement of neighbouring control points is also realistic. Crucially, the resulting implementations address the computational burden associated non-linear registration in highly specialised architectures commonly found in supercomputing environments.
The aim of this project will be to determine the practicality of non-linear co-registration in grid engine style environments. It will also involve an investigation into the performance of any resulting grid engine style algorithms with increasing granularity.
Sotiras, A. Davatzikos, C. and Paragios, N. Deformable Medical Image Registration: A Survey, IEEE Transactions on Medical Imaging, 2013, 32(7), 1153-1190.
Loeckx, D. Maes, F. Vandermeulen, D. and Suetens, P. Non-rigid Image Registration Using Free-form Deformations with a Local Rigidity Constraint, Lecture Notes in Computer Science, 2004, 3216, 639-646.
ITK, The National Library of Medicine, Insight Segmentation and Registration Toolkit, Available at: <http://www.itk.org>
Informal enquiries should be directed to: Dr Roger Tait (email@example.com)
Duration: 36 Months