Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery with Patches-to-Partial Matching.

In image-guided liver surgery, the initial rigid alignment between preoperative and intraoperative data, often represented as point clouds, is crucial for providing sub-surface information from preoperative CT/MRI images to the surgeon during the procedure. Currently, this alignment is typically performed using semi-automatic methods, which, while effective to some extent, are prone to errors that demand manual correction. Alternatively, correspondence-based point cloud registration methods further offer a promising fully automatic solution.

Boundary Constraint-Free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction.

In image-guided liver surgery, 3D-3D non-rigid registration methods play a crucial role in estimating the mapping between the preoperative model and the intraoperative surface represented as point clouds, addressing the challenge of tissue deformation. Typically, these methods incorporate a biomechanical model, represented as a finite element model (FEM), into the strain energy term to regularize a surface matching term. We propose a 3D-3D non-rigid registration method that incorporates a modified FEM into the surface matching term.

Developing and Evaluating the Fidelity of Patient Specific Kidney Emulating Phantoms for Image-guided Intervention Applications.

Patient specific organ and tissue mimicking phantoms are used routinely to develop and assess new image-guided intervention tools and techniques in laboratory settings, enabling scientists to maintain acceptable anatomical relevance, while avoiding animal studies when the developed technology is still in its infancy. Gelatin phantoms, specifically, offer a cost-effective and readily available alternative to the traditional manufacturing of anatomical phantoms, and also provide the necessary versatility to mimic various stiffness properties specific to various organs or tissues. In this study, we describe the protocol to develop patient specific anthropomorphic gelatin kidney phantoms and we also assess the faithfulness of the developed phantoms against the patient specific CT images and corresponding virtual anatomical models used to generate the phantoms.

Investigating uncertainty in augmented reality enhanced renal navigation using in vitro patient-specific tissue-mimicking phantoms.

To improve the outcome of minimally invasive renal interventions, traditional video-guided needle navigation can be enhanced by tracking the needle, guiding the needle using video imaging, and augmenting the surgical scene with pre-procedural images or models of the anatomy. In our previous work we studied, both through simulations and in vitro experiments, the uncertainty associated with the model-to-phantom registration, as well as the camera-tracker calibration and video-guided navigation. In this work, we characterize the overall navigation uncertainty using tissue emulating patient-specific kidney phantoms featuring both virtual and physical internal targets.

Assessing and reducing registration and targeting uncertainty in video-based image-guided renal navigation: An study featuring virtual targets.

Minimally invasive image-guided interventions (IGIs) enable better therapy outcomes for patients, but navigation accuracy is highly dependent on the accuracy of the image-/model-to-patient registration. This requires methods to reduce the uncertainty to a level appropriate for the procedure being performed. Since sub-surface tissue landmarks cannot be easily sampled using a tracked stylus and used to perform the patient registration, here we present a method that employs a tracked camera (that mimics a laparoscope) to perform the patient registration or update this registration in case of suspected misalignment within the context of an image-guided renal navigation procedure.