We present a super fast variational algorithm for the challenging problem of multimodal image registration. It is capable of registering full-body CT and PET images in about a second on a standard CPU with virtually no memory requirements. The algorithm is founded on a Gauss-Newton optimization scheme with specifically tailored, mathematically optimized computations for objective function and derivatives. It is fully parallelized and perfectly scalable, thus directly suitable for usage in many-core environments. The accuracy of our method was tested on 21 PET-CT scan pairs from clinical routine. The method was able to correct random distortions in the range from -10 cm to 10 cm translation and from -15° to 15° degree rotation to subvoxel accuracy. In addition, it exhibits excellent robustness to noise

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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

Since the first clinical interventions in the late 1980s, Deep Brain Stimulation (DBS) of the subthalamic nucleus has evolved into a very effective treatment option for patients with severe Parkinson's disease. DBS entails the implantation of an electrode that performs high frequency stimulations to a target area deep inside the brain. A very accurate placement of the electrode is a prerequisite for positive therapy outcome. The assessment of the intervention result is of central importance in DBS treatment and involves the registration of pre-and postinterventional scans.In this paper, we present an image processing pipeline for highly accurate registration of postoperative CT to preoperative MR. Our method consists of two steps: a fully automatic pre-alignment using a detection of the skull tip in the CT based on fuzzy connectedness, and an intensity-based rigid registration. The registration uses the Normalized Gradient Fields distance measure in a multilevel Gauss-Newton optimization framework and focuses on a region around the subthalamic nucleus in the MR.The accuracy of our method was extensively evaluated on 20 DBS datasets from clinical routine and compared with manual expert registrations. For each dataset, three independent registrations were available, thus allowing to relate algorithmic with expert performance. Our method achieved an average registration error of 0.95 mm in the target region around the subthalamic nucleus as compared to an inter-observer variability of 1.12 mm. Together with the short registration time of about five seconds on average, our method forms a very attractive package that can be considered ready for clinical use. (a) AC / PC location in T1 scan (b) STN location in T2 scan (c) Volume rendering of post-op CT scan Figure 1. Annotated anatomical sites for DBS planning and a corresponding postoperative CT scan Medical Imaging 2015: Image Processing, edited by Sébastien Ourselin, Martin A. Styner, Proc. of SPIE Vol. 9413, 941337 · © 2015 SPIE · CCC code: 1605-7422/15/$18 ·