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Soft-tissue deformation can be a problem if a preoperative modality is used to help guide a surgical or interventional procedure. We present a method that can warp a preoperative CT image to represent the intraoperative scene shown by an interventional fluoroscopy image. The method is a novel combination of a 2D–3D image registration algorithm and a deformation algorithm that allows rigid bodies to be incorporated into a nonlinear deformation based on radial basis functions. The 2D–3D registration algorithm is used to obtain information on relative vertebral movements between preoperative and intraoperative images. The deformation algorithm uses this information to warp the preoperative image to represent the intraoperative scene more accurately. Images from an aortic stenting procedure were used. The observed deformation in our experiment was 5° flexion and 5 mm lengthening of the lumbar spine over a distance of four vertebrae. The vertebral positions in the warped CT volume represent the intraoperative scene more accurately than in the preoperative CT volume. Although we had no gold standard with which to assess the registration accuracy of soft-tissue structures, the position of such structures within the warped CT volume appeared visually realistic. Comp Aid Surg 7:63–73 (2002). © 2002 Wiley-Liss, Inc.

Soft-tissue deformation can be a problem if a preoperative modality is used to help guide a surgical or interventional procedure. We present a method that can warp a preoperative CT image to represent the intraoperative scene shown by an interventional fluoroscopy image. The method is a novel combination of a 2D–3D image registration algorithm and a deformation algorithm that allows rigid bodies to be incorporated into a nonlinear deformation based on radial basis functions. The 2D–3D registration algorithm is used to obtain information on relative vertebral movements between preoperative and intraoperative images. The deformation algorithm uses this information to warp the preoperative image to represent the intraoperative scene more accurately. Images from an aortic stenting procedure were used. The observed deformation in our experiment was 5° flexion and 5 mm lengthening of the lumbar spine over a distance of four vertebrae. The vertebral positions in the warped CT volume represent the intraoperative scene more accurately than in the preoperative CT volume. Although we had no gold standard with which to assess the registration accuracy of soft-tissue structures, the position of such structures within the warped CT volume appeared visually realistic. Comp Aid Surg 7:63–73 (2002). © 2002 Wiley-Liss, Inc.

Compared to open surgery, minimally invasive liver resection has improved short term outcomes. It is however technically more challenging. Navigated image guidance systems (IGS) are being developed to overcome these challenges. The aim of this systematic review is to provide an overview of their current capabilities and limitations.

We present a system for surgical navigation using stereo overlays in the operating microscope aligned to the operative scene. This augmented reality system provides 3D information about nearby structures and offers a significant advancement over pointer-based guidance, which provides only the location of one point and requires the surgeon to look away from the operative scene. With a previous version of this system, we demonstrated feasibility, but it became clear that to achieve convincing guidance through the magnified microscope view, a very high alignment accuracy was required. We have made progress with several aspects of the system, including automated calibration, error simulation, bone-implanted fiducials and a dental attachment for tracking. We have performed experiments to establish the visual display parameters required to perceive overlaid structures beneath the operative surface. Easy perception of real and virtual structures with the correct transparency has been demonstrated in a laboratory and through the microscope. The result is a system with a predicted accuracy of 0.9 mm and phantom errors of 0.5 mm. In clinical practice errors are 0.5–1.5 mm, rising to 2–4 mm when brain deformation occurs.

Medical image registration algorithms invariably assume that the objects in the images can be treated as a single rigid body. In practice, some parts of a patient, usually bony structures, may move as rigid bodies, while others may deform. To address this, we have developed a new technique that allows identified objects in the image to move as rigid bodies, while the remainder smoothly deforms. The transformation is based on a radial basis function solution with a basis function modified by use of distance transforms based on rigid structures within the image. These distance transforms are also used to form an underlying transformation which ensures an interpolating solution. The resulting deformation technique is valid in any dimension, subject to the choice of the basis function. We demonstrate this algorithm in two dimensions on illustrative images as well as on sagittal magnetic resonance images collected from a volunteer.