The alignment of image-space to physical-space lies at the heart of all image-guided procedures. In intracranial
surgery, point-based registrations can be used with either skin-affixed or bone-implanted extrinsic objects called fiducial
markers. The advantages of point-based registration techniques are that they are robust, fast, and have a well developed
mathematical foundation for the assessment of registration quality. In abdominal image-guided procedures such
techniques have not been successful. It is difficult to accurately locate sufficient homologous intrinsic points in imagespace
and physical-space, and the implantation of extrinsic fiducial markers would constitute "surgery before the
surgery." Image-space to physical-space registration for abdominal organs has therefore been dominated by surfacebased
registration techniques which are iterative, prone to local minima, sensitive to initial pose, and sensitive to
percentage coverage of the physical surface.
In our work in image-guided kidney surgery we have developed a composite approach using "virtual fiducials."
In an open kidney surgery, the perirenal fat is removed and the surface of the kidney is dotted using a surgical marker. A
laser range scanner (LRS) is used to obtain a surface representation and matching high definition photograph. A surface
to surface registration is performed using a modified iterative closest point (ICP) algorithm. The dots are extracted from
the high definition image and assigned the three dimensional values from the LRS pixels over which they lie. As the
surgery proceeds, we can then use point-based registrations to re-register the spaces and track deformations due to
vascular clamping and surgical tractions.
The development of an image-guided renal surgery system may aid tumor resection during partial nephrectomies. This
system would require the registration of pre-operative kidney CT or MR scans to the physical kidney; however, the
amount of non-rigid deformation occurring during surgery and whether it can be corrected for in an image-guided
system is unknown. One possible source of non-rigid deformation is a change in pressure within the kidney: during
surgery, clamping of the renal artery and vein results in a loss of perfusion, such that the subsequent cutting of the
kidney and fluid outflow may cause a decrease in intrarenal pressure. In this work, we attempt to characterize the
deformation due to cutting of the kidney and subsequent changes in intrarenal pressure. To accomplish this, we perfused
a resected porcine kidney at a physiologically realistic pressure, clamped the renal vessels, and cut the kidney using a
tracked scalpel. The resulting deformation was tracked in a CT scanner using 15-20 glass bead fiducials attached to the
kidney surface. A modified form of Biot's consolidation model was used to simulate the deformation, and the accuracy
was assessed by calculating the target registration error and image similarity.
In order to facilitate the removal of tumors during partial nephrectomies, an image-guided surgery system may be useful.
This system would require a registration of the physical kidney to a pre-operative image volume; however, it is unclear
whether a rigid registration would be sufficient. One possible source of non-rigid deformation is the clamping of the
renal artery during surgery and the subsequent loss of pressure as the kidney is punctured and blood loss occurs. To
explore this issue, a model of kidney deformation due to loss of perfusion and pressure was developed based on Biot's
consolidation model. The model was tested on two resected porcine kidneys in which the renal artery and vein were
clamped. CT image volumes of the kidney were obtained before and after the deformation caused unclamping, and
fiducial markers embedded on the kidney surface allowed the deformation to be tracked. The accuracy of the kidney
model was accessed by calculating the model error at the fiducial locations and using image similarity measures.
Preliminary results indicate that the model may be useful in a non-rigid registration scheme; however, further
refinements to the model may be necessary to better simulate the deformation due to loss of perfusion and pressure.
This work explores an inverse problem technique of extracting soft tissue elasticity information via nonrigid model-based
image registration. The algorithm uses the elastic properties of the tissue in a biomechanical model to achieve
maximal similarity between image data acquired under different states of loading. A framework capable of handling
fully three-dimensional models and image data has been recently developed utilizing parallel computing and iterative
sparse matrix solvers. For this preliminary investigation, a series of simulation experiments with clinical image data of
human breast are used to test the robustness of the algorithm to expected mis-estimation of displacement boundary
conditions encountered in real-world situations. Three methods of automated point correspondence are also examined as
means of generating boundary conditions for the algorithm.
Recent advances in breast cancer imaging have generated new ways to characterize the disease. Many analysis
techniques require a method for determining correspondence between a pendant breast surface before and after a
deformation. In this paper, an automated point correspondence method that uses the surface Laplacian or the diffusion
equation coupled to an isocontour matching and interpolation scheme are presented. This method is compared to a TPS
interpolation of surface displacements tracked by fiducial markers. The correspondence methods are tested on two
realistic finite element simulations of a breast deformation and on a breast phantom. The Laplace correspondence
method resulted in a mean TRE ranging from 1.0 to 7.7 mm for deformations ranging from 13 to 33 mm, outperforming
the diffusion method. The TPS method, in part because it utilizes fiducial information, performed better than the
Laplace method, with mean TRE ranging from 0.3 to 1.9 mm for the same range of deformations. The Laplace and TPS
methods have the potential to be used by analyses requiring point correspondence between deforming surfaces.
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