Deep brain structures are frequently used as targets in neurosurgical procedures. However, the boundaries of these
structures are often not visible in clinically used MR and CT images. Techniques based on anatomical atlases and
indirect targeting are used to infer the location of these targets intraoperatively. Initial errors of such approaches may be
up to a few millimeters, which is not negligible. E.g. subthalamic nucleus is approximately 4x6 mm in the axial plane
and the diameter of globus pallidus internus is approximately 8 mm, both of which are used as targets in deep brain
stimulation surgery. To increase the initial localization accuracy of deep brain structures we have developed an atlas-based
segmentation method that can be used for the surgery planning. The atlas is a high resolution MR head scan of a
healthy volunteer with nine deep brain structures manually segmented. The quality of the atlas image allowed for the
segmentation of the deep brain structures, which is not possible from the clinical MR head scans of patients. The subject
image is non-rigidly registered to the atlas image using thin plate splines to represent the transformation and normalized
mutual information as a similarity measure. The obtained transformation is used to map the segmented structures from
the atlas to the subject image. We tested the approach on five subjects. The quality of the atlas-based segmentation was
evaluated by visual inspection of the third and lateral ventricles, putamena, and caudate nuclei, which are visible in the
subject MR images. The agreement of these structures for the five tested subjects was approximately 1 to 2 mm.
Deep brain stimulation (DBS) surgery is a treatment for patients suffering from Parkinson's disease and other
movement disorders. The success of the procedure depends on the implantation accuracy of the DBS electrode
array. Surgical planning and navigation are done based on the pre-operative patient scans, assuming that brain
tissues do not move from the time of the pre-operative image acquisition to the time of the surgery. We performed
brain shift analysis on nine patients that underwent DBS surgery using a 3D non-rigid registration algorithm. The
registration algorithm automatically aligns the pre-operative and the post-operative 3D MRI scans and provides
the shift vectors over the entire brain. The images were first aligned rigidly and then non-rigidly registered with
an algorithm based on thin plate splines and maximization of the normalized mutual information. Brain shift of
up to 8 mm was recorded in the nine subjects, which is significant given that the size of the targets in the DBS
surgery is a few millimeters.
Most of the suggested image registration methods are based on the optimization of an objective function. Drawbacks of this approach are the problem of local minima and the need to initialize the transformation close to the true solution. This paper presents a method for N-dimensional rigid and similarity image registration that is not optimization-based and consequently it doesn't involve local minima and initialization. Instead of obtaining the transformation parameters implicitly through an iterative optimization process, they are obtained explicitly. The proposed method has advantages over existing explicit methods. The explicit expressions for transformation parameters involve image integrals and no image derivatives, which makes the method robust to noise. It is shown that the method has a few desired properties including symmetry and transitivity, and that it is invariant to initial alignment of the images. The method has been tested on simulated and real brain 2D and 3D MR image pairs and the achieved average registration error was one voxel.
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