Computational Models In Image Guided Interventions

In image-guided surgery and image-directed therapy a plan based on pre-procedure imaging is registered to the patient in the operating or treatment room using a 3D spatial localizer. The plan can be used as long as the transformation between plan and patient remains valid. Most systems use a rigid-body transformation restricting guidance to bony structures (e.g. orthopaedic surgery or maxillo-facial surgery) or structures that are rigidly related to bone (e.g. neurosurgery). Fully 3D intra-operative imaging such as interventional MR allows image guidance to be extended to structures that move or deform during an intervention. However, this technology is expensive, interferes significantly with standard surgical protocols and requires computationally expensive non-rigid registration of the plan to the current patient scan. This talk will describe four examples where computational models of motion and anatomy are combined with 2D intra-operative imaging to extend the scope of image directed methods. In the first, image guided neurosurgery, we show how intra-operative imaging may account for distortion caused by the intervention itself. In two further applications - percutaneous ablation of metastatic liver disease and external beam radiotherapy of the lung - we show how computational models of motion might be used in conjunction with a therapy plan to guide the intervention. In the final example, selected from orthopaedic surgery, we show recent advances that demonstrate how a statistical shape model generated from example 3D images, can be used to provide image guidance without any pre-operative 3D imaging