We introduce a new highly parallel and memory efficient deformable image registration algorithm to handle challenging clinical applications. The algorithm is based on the normalized gradient fields (NGF) distance measure and Gauss-Newton numerical optimization. By carefully analyzing the mathematical structure of the problem, a matrix-free Hessian-vector multiplication for NGF is derived, giving a highly integrated formulation. Embedding the new scheme in a full, non-linear image registration algorithm enables fast calculations on high resolutions with dramatically reduced memory consumption. The new approach provides linear scalability compared with a traditional sparse-matrix-based scheme. The algorithm is evaluated on a challenging problem from radiotherapy, where pelvis cone-beam CT and planning CT images are registered. Speedups up to a factor of 149.3 for a single Hessian-vector multiplication and of 20.3 for a complete non-linear registration are achieved.
|Title of host publication||2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)|
|Number of pages||5|
|Place of Publication||New York City, USA|
|Publication status||Published - 01.04.2015|
|Event||12th IEEE International Symposium on Biomedical Imaging - New York, United States|
Duration: 16.04.2015 → 19.04.2015