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Neurosurgeons are faced with the challenge of learning, planning, and performing increasingly complex surgical procedures in which there is little room for error. With improvements in computational power and advances in visual and haptic display technologies, virtual surgical environments can now offer potential benefits for surgical training, planning, and rehearsal in a safe, simulated setting. This article introduces the various classes of surgical simulators and their respective purposes through a brief survey of representative simulation systems in the context of neurosurgery. Many technical challenges currently limit the application of virtual surgical environments. Although we cannot yet expect a digital patient to be indistinguishable from reality, new developments in computational methods and related technology bring us closer every day. We recognize that the design and implementation of an immersive virtual reality surgical simulator require expert knowledge from many disciplines. This article highlights a selection of recent developments in research areas related to virtual reality simulation, including anatomic modeling, computer graphics and visualization, haptics, and physics simulation, and discusses their implication for the simulation of neurosurgery.

One of the most important advantages of computer simulators for surgical training is the opportunity they afford for independent learning. However, if the simulator does not provide useful instructional feedback to the user, this advantage is significantly blunted by the need for an instructor to supervise and tutor the trainee while using the simulator. Thus, the incorporation of relevant, intuitive metrics is essential to the development of efficient simulators. Equally as important is the presentation of such metrics to the user in such a way so as to provide constructive feedback that facilitates independent learning and improvement. This paper presents a number of novel metrics for the automated evaluation of surgical technique. The general approach was to take criteria that are intuitive to surgeons and develop ways to quantify them in a simulator. Although many of the concepts behind these metrics have wide application throughout surgery, they have been implemented specifically in the context of a simulation of mastoidectomy. First, the visuohaptic simulator itself is described, followed by the details of a wide variety of metrics designed to assess the user's performance. We present mechanisms for presenting visualizations and other feedback based on these metrics during a virtual procedure. We further describe a novel performance evaluation console that displays metric-based information during an automated debriefing session. Finally, the results of several user studies are reported, providing some preliminary validation of the simulator, the metrics, and the feedback mechanisms. Several machine learning algorithms, including Hidden Markov Models and a Naïve Bayes Classifier, are applied to our simulator data to automatically differentiate users' expertise levels.

We describe the implementation of irrigation and blood simulation using Smoothed Particle Hydrodynamics (SPH) in a cranial base surgical simulator. Graphical accuracy of virtual surgery is a significant goal for improving the realism and immersive experience of computerized training environments. For temporal bone micro-surgery fluids contribute not only to the visual integrity of the surgical field but provide relevant anatomic cues as well. The skill of 3-D sensory and navigation has become increasingly viable in surgery with the rising popularity of laparoscopic, catheter angiography and other minimally invasive approaches. The introduction of realistic simulated blood and irrigation enables the practice and coordination of two-handed microdissection techniques and the timing needed for safe bone removal and cautery.

This paper presents several new metrics related to bone removal and suctioning technique in the context of a mastoidectomy simulator. The expertise with which decisions as to which regions of bone to remove and which to leave intact is evaluated by building a Naïve Bayes classifier using training data from known experts and novices. Since the bone voxel mesh is very large, and many voxels are always either removed or not removed regardless of expertise, the mutual information was calculated for each voxel and only the most informative voxels used for the classifier. Leave-out-one cross validation showed a high correlation of calculated expert probabilities with scores assigned by instructors. Additional metrics described in this paper include those for assessing smoothness of drill strokes, proper drill burr selection, sufficiency of suctioning, two-handed tool coordination, and application of appropriate force and velocity magnitudes as functions of distance from critical structures.

One of the primary barriers to the acceptance of surgical simulators is that most simulators still require a significant amount of an instructing surgeon's time to evaluate and provide feedback to the students using them. Thus, an important area of research in this field is the development of metrics that can enable a simulator to be an essentially self-contained teaching tool, capable of identifying and explaining the user's weaknesses. However, it is essential that these metrics be validated in able to ensure that the evaluations provided by the "virtual instructor" match those that the real instructor would provide were he/she present. We have previously proposed a number of algorithms for providing automated feedback in the context of a mastoidectomy simulator. In this paper, we present the results of a user study in which we attempted to establish construct validity (with inter-rater reliability) for our simulator itself and to validate our metrics. Fifteen subjects (8 experts, 7 novices) were asked to perform two virtual mastoidectomies. Each virtual procedure was recorded, and two experienced instructing surgeons assigned global scores that were correlated with subjects' experience levels. We then validated our metrics by correlating the scores generated by our algorithms with the instructors' global ratings, as well as with metric-specific sub-scores assigned by one of the instructors.

One important technique common throughout surgery is achieving proper exposure of critical anatomic structures so that their shapes, which may vary somewhat among patients, can be confidently established and avoided. In this paper, we present an algorithm for determining which regions of selected structures are properly exposed in the context of a mastoidectomy simulation. Furthermore, our algorithm then finds and displays all other points along the surface of the structure that lie along a sufficiently short and straight path from an exposed portion such that their locations can be safely inferred. Finally, we present an algorithm for providing realistic visual cues about underlying structures with view-dependent shading of the bone.

Evaluating a trainee's performance on a simulated procedure involves determining whether a specified objective was met while avoiding certain "injurious" actions that damage vulnerable structures. However, it is also important to teach the stylistic behaviors that minimize overall risk to the patient, even though these criteria may be more difficult to explicitly specify and detect. In this paper, we address the development of metrics that evaluate the risk in a trainee's behavior while performing a simulated mastoidectomy. Specifically, we measure the trainee's ability to maintain an appropriate field of view so as to avoid drilling bone that is hidden from view, as well as to consistently apply appropriate forces and velocities. Models of the maximum safe force and velocity magnitudes as functions of distances from key vulnerable structures are learned from model procedures performed by an expert surgeon on the simulator. In addition to quantitatively scoring the trainee's performance, these metrics allow for interactive 3D visualization of the performance by distinctive coloring of regions in which excessive forces or velocities were applied or insufficient visibility was maintained, enabling the trainee to pinpoint his/her mistakes and how to correct them. Although these risky behaviors relate to a mastoidectomy simulator, the objectives of maintaining visibility and applying safe forces and velocities are common in surgery, so it may be possible to extend much of this methodology to other procedures.

In this paper we describe a new solution for stable haptic interaction with deformable object simulations featuring low servo rates and computational delays. The solution presented is a combination of the local model and the virtual coupling concepts proposed in the past. By varying the local model impedance depending on the local stiffness of the deformable object, the interaction between local model and simulation can always be made stable independently of low servo rates or computational delays. Moreover by using more complex local impedances that feature an integral term, we are able to control the steady-state error between the device and the surface of the deformable object This allows us to maximize the Z-width of the simulation, while obtaining overall stable behavior without using any added damping. The local model is always computed using the current deformable object surface, thus allowing for multi- point contact interaction, i.e., allowing multiple users to feel each other's influence on the object The proposed solution is presented and analyzed in a multirate setting. Experimental results employing a Phantom haptic interface are presented. [ABSTRACT FROM AUTHOR]

Haptic rendering allows users to "feel" virtual objects in a simulated environment. This article provides an overview of the haptics field with a particular focus on the architecture of haptic rendering systems and the devices that enable force feedback based haptic interaction. Unlike graphic and auditory rendering, the rendering of haptic interactions requires modeling the physical interactions between objects and generating forces that arise during contact and motion in real time. As in the field of computer graphics, practitioners of computer haptics are intensely interested in the best rendering methods of objects, so that humans can meaningfully perceive them. The article gives a basic view of methods used to render the way an object feels, it discusses modeling these physical interactions and the dynamic stability issues that arise in real-world implementations. Development of multimodal workstations that provide haptic, visual, and auditory engagement will offer opportunities for more integrated interactions. INSET: History of Haptics..