Robust automated intensity-based image registration is a core capability needed for most brain image analysis applications. Ideally it provides a fast, accurate, robust and objective way to align images of the same or different MR modalities, crucial for many applications such as localising functional activations within a subject's own neuro-anatomy and for allowing group comparisons via the registration to a standard image. However, a common problem is that registration methods sometimes fail to produce ``sensible'' results, with gross misalignment clearly visible. These failures often occur when the images being registered are initially in different orientations. For automated analysis methods that rely on registration (e.g., FMRI analysis and atrophy analysis), such failures are very problematic.
The standard framework for intensity-based registration involves the minimisation of a cost function (which quantitates how well aligned two images are) as the registration parameters (such as rotation and translation) are varied. Consequently, the cause of misregistrations arises from either non-ideal cost functions (which return minimum values for poor alignments) or from non-ideal optimisation methods which fail to find the (global) minimum value of the cost function. Much work has gone into proposing suitable cost functions for image registration, for example, using information theory [38]. However, little work has been done on improving optimisation methods for image registration, even though ``getting stuck'' in a local minimum is the main cause of failure for registration methods.
The research conducted at FMRIB in affine registration [23,22] has concentrated on trying to eliminate the occurrence of gross misalignments (increasing robustness) by using a multi-start, multi-resolution global optimisation method. This optimisation starts with a large-scale search strategy (e.g., trying ``all'' possible initial rotations) at an 8mm image resolution, followed by a series of multi-start local optimisations at a 4mm resolution (based on perturbations of the best candidate alignments found at the 8mm stage), and finishes with a progressive sequence of local optimisations at 2mm and 1mm resolutions to refine the final alignment. This strategy is considerably more robust than optimisation based purely on local minimisation (even if a multi-resolution approach is used). A major advantage of this optimisation method versus generic global optimisation methods is that it is specifically designed for affine image registration, incorporating knowledge of the expected and achievable parameter changes. As a consequence it is more efficient in searching the parameter space, allowing it to run significantly faster than would be possible using other techniques such as Simulated Annealing or Genetic Algorithms.
In addition to the work on the global optimisation method, modifications of the standard cost functions to down-weight voxels at the edge of the common overlapping field-of-view and use fuzzy-binning techniques for histogram estimation have been applied to reduce the number of local cost function minima present. As well as standard within-modality cost functions such as normalised correlation, we have implemented inter-modal cost functions such as mutual information [38] and correlation ratio [29], which allow the robust registration of images with different contrasts such as FMRI to structural MRI, or PET to MRI. The combination of the optimisation technique and cost function modifications has led to the development of FLIRT (FMRIB's Linear Image Registration Tool) which has been proven to be highly robust [23,22] and is incorporated in many fully automated image analysis methods (e.g., SIENA and FEAT).