COMPUTER-SIMULATION OF A NEUROSURGICAL OPERATION - CRANIOTOMY FOR HYPOTHALAMIC HAMARTOMA

Although magnetic resonance imaging has revolutionised the management of intracranial lesions with improved visualisation of anatomical stru ctures, it only produces two-dimensional images, from which the clinic ian has to extrapolate a three-dimensional interpretation. Several app roaches can be used to create 3D images; the discipline of image segme ntation has encompassed a number of these techniques. Such techniques allow the clinician to delineate areas of interest. The resulting comp uter-generated outlines can be reconstructed in a three-dimensional ar rangement. Although a plethora of ''generic'' segmentation techniques exist, we have developed a refined form, dependent on general and part icular properties of the anatomical structures under investigation. Hi gh-contrast structures such as the ventricles and external surface of the head are found by using a localised adaptive thresholding techniqu e. Less definable structures, with poor or nonexistent signal change a cross neighbouring structures, such as brain stem or pituitary, are fo und by applying an ''energy minimisation'' based technique. To demonst rate the techniques we used the example of an 8-year-old boy with unco ntrolled gelastic seizures due to a hypothalamic hamartoma, who is bei ng considered for surgery. We were able to demonstrate the anatomical relationships between the hypothalamic hamartoma and adjacent structur es such as optic chiasm, brain stem and ventricular system. We were su bsequently able to create a video, reproducing the stages of craniotom y for excision of this tumour. By creating true 3D objects, we were ab le at any stage of the simulation to visualise structures situated con tralaterally to the approaching surgical dissector. These 3D represent ations of the structures can be either invisible or opaque, in order t o afford 3D localisation as the ''virtual'' surgical dissection procee ds. The clinical application of such techniques will enable surgeons t o improve their understanding of anatomical relations of intracranial lesions and has obvious implications in image-guided surgery.