The da Vinci research interface allows third party developers and research collaborators to retrieve a real-time stream of kinematic and user event data from the da Vinci Surgical System during clinical use. This data includes the motion of all master and slave manipulators, as well as a number of user console events such as button and pedal activations. This paper provides a brief outline of the technical scope of the da Vinci research interface, as well as some discussion of the criteria for accessing this interface, and requirements for establishing a research partnership with Intuitive Surgical Inc.

The da Vinci Research Interface

Computer assisted intervention (CAI) systems require the integration of an increasing number of devices,
including medical monitors, sensors, tracking devices and robots. This complexity makes applications
harder to develop, more difficult to debug and the accumulation of ad hoc interfaces reduces the overall
portability. We describe a set of libraries, the cisst libraries, developed at the Johns Hopkins University
to address some of the problems encountered when integrating devices for CAI. We focus on three main
characteristics of the cisst libraries: software architecture, multi-threading and CAI specific interfaces.

The cisst libraries for computer assisted intervention systems

The Surgical Assistant Workstation (SAW) is a software development framework that can be used to
develop new applications in robot-assisted surgery with augmented visualization. Robot-assisted laparoscopic
surgery and micro-surgery as presented by the da Vinci telerobotic system and the Johns
Hopkins Steady Hand, respectively can be improved by providing fully-integrated image guidance and
information-enhanced intra-operative assistance to the surgical team and to the surgeon in particular.
This paper describes several use case applications of the SAW framework, as well as a novel user interface
library being developed to support 3D interactive menu systems and overlays for surgical guidance.

The Surgical Assistant Workstation

This paper is a position statement on the benefits that an Open Source toolkit can bring to the field of image guided surgery. The particular example discussed here is the IGSTK toolkit. IGSTK is an open source software project designed for supporting the development of image guided surgery software applications. It provides functionalities for interfacing with optical and electromagnetic trackers, reading and visualizing DICOM datasets, as well as loading and displaying geometrical models resulting from image segmentation. These functionalities are enveloped by a simplified API implemented in a layer designed with emphasis on patient safety. The Toolkit is designed to ease the development of IGS applications, providing the basic functionalities related to the management of basic components. IGSTK is based on the Insight Toolkit (ITK) and the Visualization Toolkit (VTK), and can be combined with GUI toolkits such as FLTK and Qt. The IGSTK toolkit is distributed under a BSD license. 
Enter the names of any sponsors and/or funding codes in the box below.

IGSTK: Building High Quality Roads with Open Source Software

The finite element (FE) method is a powerful tool for the study of biomechanics. One of the limiting factors in transitioning this tool into the clinic is the time required to generate high quality meshes for analysis. Previously, we developed a mapped meshing technique that utilized force control and a finite element solver to warp a template mesh onto subject specific surfaces. This paper describes a displacement based method that directly warps the template mesh onto subject specific surfaces using distance as the driving measure for the deformable registration. The resulting meshes were evaluated for mesh quality and compared to the force based method. An initial evaluation was performed using a mathematical phantom. The algorithm was then applied to generate meshes for the phalanx bones of the human hand. The algorithm successfully mapped the template bone to all of the bony surfaces, with the exception of the distal phalanx bone. In this one case, significant differences existed between the geometries of the template mesh and the distal phalanx. Further refinement of the algorithm may allow the algorithm to successfully generate meshes even in the presence of large geometric shape differences.

Comparison of Displacement-Based and Force-Based Mapped Meshing

A novel partnership between surgeons and machines, made possible by advances in computing and engineering technology, could overcome many of the limitations of traditional surgery. By extending surgeons' ability to plan and carry out surgical interventions more accurately and with less trauma, Computer-Integrated Surgery (CIS) systems could help to improve clinical outcomes and the efficiency of health care delivery. CIS systems could have a similar impact on surgery to that long since realized in Computer-Integrated Manufacturing (CIM). Mathematical modeling and computer simulation have proved tremendously successful in engineering. Computational mechanics has enabled technological developments in virtually every area of our lives. One of the greatest challenges for mechanists is to extend the success of computational mechanics to fields outside traditional engineering, in particular to biology, the biomedical sciences, and medicine. 

Computational Biomechanics for Medicine Workshop series was established in 2006 with the first meeting held in Copenhagen. The third workshop was held in conjunction with the Medical Image Computing and Computer Assisted Intervention Conference (MICCAI 2008) in New York on 10 September 2008. It provided an opportunity for specialists in computational sciences to present and exchange opinions on the possibilities of applying their techniques to computer-integrated medicine. 

Computational Biomechanics for Medicine III was organized into two streams: Computational Biomechanics of Soft Tissues, and Computational Biomechanics of Tissues of Musculoskeletal System. The application of advanced computational methods to the following areas was discussed:
- Medical image analysis; 
- Image-guided surgery; 
- Surgical simulation; 
- Surgical intervention planning; 
- Disease prognosis and diagnosis; 
- Injury mechanism analysis; 
- Implant and prostheses design; 
- Medical robotics. 

After rigorous review of full (eight-to-twelve page) manuscripts we accepted 15 papers, collected in this volume. They were split equally between podium and poster presentations. The proceedings also include abstracts of two invited lectures by world-leading researchers Professor Chwee Teck Lim from national University of Singapore and Dr. David Lloyd from The University of Western Australia.

Information about Computational Biomechanics for Medicine Workshops, including Proceedings of previous meetings is available at http://cbm.mech.uwa.edu.au/ .

We would like to thank the MICCAI 2008 organizers for help with administering the Workshop, invited lecturers for deep insights into their research fields, the authors for submitting high quality work, and the reviewers for helping with paper selection.

Computational Biomechanics for Medicine III - Workshop proceedings

A new Combinatorial Ricci curvature and Laplacian operators for grayscale images are introduced and tested on 2D medical images. These notions are based upon more general concepts developed by R. Forman. Further applications are also suggested.

Combinatorial Ricci Curvature for Image Processing

Diﬀusion tensor imaging has become an important research and clinical tool, owing to its unique ability to infer microstructural properties of living tissue. Increased use has led to a demand for statistical tools to analyze diﬀusion tensor data and perform, for example, conﬁdence estimates, ROI analysis, and group comparisons. A ﬁrst step towards developing a statistical framework is establishing the basic notion of distance between tensors. We investigate the properties of two previously proposed metrics that deﬁne a Riemannian manifold: the aﬃne-invariant and Euclidean metrics. We ﬁnd that the Euclidean metric is more appropriate for intra-voxel comparisons, and suggest that a context-dependent metric may be required for inter-voxel comparisons.

On What Manifold do Diffusion Tensors Live?

The task of classifying or labeling cortical sulci is made difficult by the fact that individual sulci may not have unique distinguishing features and usually need to be identified by a multivariate feature set that takes the relative spatial arrangement into account. In this paper, classical multidimensional scaling (MDS), which gives a geometric interpretation to input dissimilarity data, is used to classify 180 sulci drawn from the ten major classes of sulci. Using a leave-one-out validation strategy, we acheive a success rate of 100% in the best case and 78% in the worst case. For these more difficult cases, we propose a second stage of classification using shape based features. One of these features is the geodesic distance between sulcal curves obtained from a new open curve representation in a geometric framework.
With MDS, we oﬀer a simple and intuitive approach to a challenging problem. Not only can we easily separate left and right brain sulci, but we also narrow the classification problem from, in this case, a 10-class to a 2-class problem. More generally, we can identify a region-of-interest (ROI) within which one can carry out further classification.

The Labeling of Cortical Sulci using Multidimensional Scaling

Abstract. Statistical shape models (SSM) capture the variation of shape across a population, in order to allow further analysis. Previous work demonstrates that deformation fields contain global transformation components, even if global pre-
registration is performed. It is crucial to construction of SSMs to remove these global transformation components from the local deformations - thus obtaining minimal deformations - prior to using these as input for SSM construction. In medical image processing, parameterized SSMs based on control points of free-form deformations (FFD) are a popular choice, since they offer several advantages compared to SSMs based on dense deformation ﬁelds. In this work, we extend the previous approach by presenting a framework for construction of both, unparameterized and FFD-based SSMs from minimal deformations. The core of the method is computation of minimal deformations by extraction of the linear part from the original dense deformations. For FFD-based SSMs, the FFD-parameterization of the minimal deformations is performed by projection onto the space of FFDs. Both steps are computed by close-form solutions optimally in the least-square sense. The proposed method is evaluated on a data set of 62 MR images of the corpus callosum. The results show a significant improvement achieved by the proposed method for SSMs built on dense ﬁelds, as well as on FFD-based SSMs.