Motion compensated SLAM for image guided surgery.

The effectiveness and clinical benefits of image guided surgery are well established for procedures where there is manageable tissue motion. In minimally invasive cardiac, gastrointestinal, or abdominal surgery, large scale tissue deformation prohibits accurate registration and fusion of pre- and intraoperative data. Vision based techniques such as structure from motion and simultaneous localization and mapping are capable of recovering 3D structure and laparoscope motion. Current research in the area generally assumes the environment is static, which is difficult to satisfy in most surgical procedures. In this paper, a novel framework for simultaneous online estimation of laparoscopic camera motion and tissue deformation in a dynamic environment is proposed. The method only relies on images captured by the laparoscope to sequentially and incrementally generate a dynamic 3D map of tissue motion that can be co-registered with pre-operative data. The theoretical contribution of this paper is validated with both simulated and ex vivo data. The practical application of the technique is further demonstrated on in vivo procedures.