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This research presents a novel low-cost pedagogical environment oriented to ease the use experiential learning methods for training on image-guided robotic surgery technologies. The environment proposes a simplified surgical simulation use case: the movement of insertion and extraction of a needle, similar to an image-guided biopsy situation. The training environment is composed of a 3D-printed phantom, a CT scan of the phantom, a virtual reality environment that includes haptic information, an ad-hoc 1 degree of freedom (DOF) robotic system for insertion/extraction and a Novint Falcon, a low cost 3 DOF manipulator that allows achieving haptic feedback. A first pilot experience has been carried out in the ?Surgical Simulation and Planning? course of the Bachelor of Biomedical Engineering at Universidad Politécnica de Madrid (UPM). Results of the surveys carried out (by teachers and learners) show that the new project-based methodology improves in all cases the values of student?s satisfaction obtained using the classical methodology based on master classes and practices.

We propose a fully automated methodology for hexahedral meshing of patient-specific structures of the human knee obtained from magnetic resonance images, i.e. femoral/tibial cartilages and menisci. We select eight patients from the Osteoarthritis Initiative and validate our methodology using MATLAB on a laptop computer. We obtain the patient-specific meshes in an average of three minutes, while faithfully representing the geometries with well-shaped elements. We hope to provide a fundamentally different means to test hypotheses on the mechanisms of disease progression by integrating our patient-specific FE meshes with data from individual patients. Download both our meshes and software at http://im.engr.uconn.edu/downloads.php.

Purpose: Current radiotherapy is progressing to the concept of adaptive radiotherapy, which implies the adaptation of planning along the treatment course. Nonrigid registration is an essential image processing tool for adaptive radiotherapy and image guided radiotherapy, and the three-dimensional (3D) nature of the current radiotherapy techniques requires a 3D quantification of the registration error that existing evaluation methods do not cover appropriately. The authors present a method for 3D evaluation of nonrigid registration algorithms’ performance, based on organ delineations, capable of working with near-spherical volumes even in the presence of concavities. Methods: The evaluation method is composed by a volume shape description stage, developed using a newad hoc volume reconstruction algorithm proposed by the authors, and an error quantification stage. The evaluation method is applied to the organ delineations of prostate and seminal vesicles, obtained by an automatic segmentation method over images of prostate cancer patients treated with intensity modulated radiation therapy. Results: The volume reconstruction algorithm proposed has been shown to accurately model complex 3D surfaces by the definition of clusters of control points. The quantification method, inspired by the Haussdorf–Chebysev distance, provides a measure of the largest registration error per control direction, defining a valid metric for concave-convex volumes. Summarizing, the proposed evaluation methodology presents accurate results with a high spatial resolution in a negligible computation time in comparison with the nonrigid registration time. Conclusions: Experimental results show that the metric selected for quantifying the registration error is of utmost importance in a quantitative evaluation based on measuring distances between volumes. The accuracy of the volume reconstruction algorithm is not so relevant as long as the reconstruction is tight enough on the actual volume of the organ. The new evaluation method provides a smooth and accurate volume reconstruction for both the reference and the registered organ, and a complete 3D description of nonrigid registration algorithms’ performance, resulting in a useful tool for study and comparison of registration algorithms for adaptive radiotherapy.

Due to limitations in conventional medical imaging and the restrictions imposed by both the anatomy and the surgical approach in pancreatic cancer, there is a need for methods to support intraoperative imaging in order to improve their accurate anatomical localization and the characterization of their nature. Laparoscopic ultrasounds (LUS) images and endoscopic videos can be used to extract useful information during the surgical procedures. A fast approach for acquiring an estimation of the tumor positioning and size through laparoscopic ultrasounds images has been developed. Based on the surgical video, endoscope 3D tracking is achieved by means of a Shape-from-Motion technique. Intraoperative imaging algorithms’ validation has been carried out in an ex vivo porcine model and results have shown the viability of exploiting them for structures characterization and their 3D reconstruction.

A novel method for generating patient-specific high quality conforming hexahedral meshes is presented. The meshes are directly obtained from the segmentation of patient magnetic resonance (MR) images of abdominal aortic aneu-rysms (AAA). The MRI permits distinguishing between struc-tures of interest in soft tissue. Being so, the contours of the lumen, the aortic wall and the intraluminal thrombus (ILT) are available and thus the meshes represent the actual anato-my of the patient?s aneurysm, including the layered morpholo-gies of these structures. Most AAAs are located in the lower part of the aorta and the upper section of the iliac arteries, where the inherent tortuosity of the anatomy and the presence of the ILT makes the generation of high-quality elements at the bifurcation is a challenging task. In this work we propose a novel approach for building quadrilateral meshes for each surface of the sectioned geometry, and generating conforming hexahedral meshes by combining the quadrilateral meshes. Conforming hexahedral meshes are created for the wall and the ILT. The resulting elements are evaluated on four patients? datasets using the Scaled Jacobian metric. Hexahedral meshes of 25,000 elements with 94.8% of elements well-suited for FE analysis are generated.

This work presents a real time non-rigid registration method for a parametric model of the aorta based on computer animation techniques. The endovascular system is modeled as a polygonal mesh and a skeletal structure, both calculated automatically from segmented pre-operative images. The skeleton, registered using linear regression methods, supports an adaptive triangular mesh, both adapting in real time to the longitudinal deformations and changes affecting the aorta during the procedure. These changes are offered to the surgeon as part of a virtual-reality catheterization guidance system. The initial evaluation shows that the algorithm and implementation are able to handle up to 5 situation updates per second while maintaining a mean registration error of 1,012 mm and a maximum of 4.346 mm, thus effectively supporting real-time navigation.

In order to perform finite element (FE) analyses of patient-specific abdominal aortic aneurysms, geometries derived from medical images must be meshed with suitable elements. We propose a semi-automatic method for generating conforming hexahedral meshes directly from contours segmented from medical images. Magnetic resonance images are generated using a protocol developed to give the abdominal aorta high contrast against the surrounding soft tissue. These data allow us to distinguish between the different structures of interest. We build novel quadrilateral meshes for each surface of the sectioned geometry and generate conforming hexahedral meshes by combining the quadrilateral meshes. The three-layered morphology of both the arterial wall and thrombus is incorporated using parameters determined from experiments. We demonstrate the quality of our patient-specific meshes using the element Scaled Jacobian. The method efficiently generates high-quality elements suitable for FE analysis, even in the bifurcation region of the aorta into the iliac arteries. For example, hexahedral meshes of up to 125,000 elements are generated in less than 130 s, with 94.8 % of elements well suited for FE analysis. We provide novel input for simulations by independently meshing both the arterial wall and intraluminal thrombus of the aneurysm, and their respective layered morphologies. QC 20140306

A new method for three-dimensional quantitative evaluation of target volume delineation is presented. It is composed by a new 3D reconstruction method called Origami, based on the combination of two bidimensional analysis of the volume of each organ instead of one 3D analysis, and a 3D description of the error distribution in the space. The Origami method avoid external errors introduced by a 3D rendering and has shown to work correctly in both convex and convex-concave volumes, accurately fitting the contours of the planning study. Its performance has been evaluated comparing it with the Convex Hull algorithm using Delaunay triangulation, resulting in a much more adjusted evaluation. Moreover, Origami computes thousands of control points in less than one second in a common PC.

Two deformable registration methods, the Demons and the Morphon algorithms, have been used for registration of CT datasets to evaluate their usability in radiotherapy planning for prostate cancer. These methods were chosen because they can perform deformable registration in a fully automated way. The experiments show that for intrapatient registration both of the methods give useful results, although some differences exist in the way they deform the template. The Morphon method has, however, some advantageous compared to the Demons method. It is invariant to the image intensity and it does not distort the deformed data. The conclusion is therefore to recommend the Morphon method as a registration tool for this application. A more flexible regularization model is needed, though, in order to be able to catch the full range of deformations required to match the datasets.