Image registration is an important task in most medical imaging applications. Numerous algorithms have been
proposed and some are widely used. However, due to the vast amount of data collected by eg. a computed
tomography (CT) scanner, most registration algorithms are very slow and memory consuming. This is a huge
problem especially in atlas building, where potentially hundreds of registrations are performed. This paper
describes an approach for accelerated image registration. A grid-based warp function proposed by Cootes and
Twining, parameterized by the displacement of the grid-nodes, is used. Using a coarse-to-fine approach, the
composition of small diffeomorphic warps, results in a final diffeomorphic warp. Normally the registration is
done using a standard gradient-based optimizer, but to obtain a fast algorithm the optimization is formulated in
the inverse compositional framework proposed by Baker and Matthews. By switching the roles of the target and
the input volume, the Jacobian and the Hessian can be pre-calculated resulting in a very efficient optimization
algorithm. By exploiting the local nature of the grid-based warp, the storage requirements of the Jacobian and
the Hessian can be minimized. Furthermore, it is shown that additional constraints on the registration, such
as the location of markers, are easily embedded in the optimization. The method is applied on volumes built
from CT-scans of pig-carcasses, and results show a two-fold increase in speed using the inverse compositional
approach versus the traditional gradient-based method.
Myocardial perfusion Magnetic Resonance (MR) imaging has proven to be a powerful method to assess coronary artery diseases. The current work presents a novel approach to the analysis of registered sequences of myocardial perfusion MR images. A previously reported active appearance model (AAM) based segmentation and registration of the myocardium provided pixel-wise signal intensity curves that were analyzed using the Support Vector Domain Description (SVDD). In contrast to normal SVDD, the entire regularization path was calculated and used to calculate a generalized distance, which is used to discriminate between ischemic and healthy tissue. The results corresponded well to the ischemic segments found by assessment of the three common perfusion parameters; maximum upslope, peak and time-to-peak obtained pixel-wise.