VR simulated training for less invasive vascular intervention

The lumbar puncture is performed by inserting a needle into the spinal chord of the patient to inject medicaments or to extract liquor. This procedure is usually trained on the patient guided by experienced supervisors. A virtual reality lumbar puncture simulator has been developed in order to enhance the training by providing new insight into a virtual patient and in order to minimize the patient’s risk. We use a haptic device with six degrees of freedom (6DOF) to feedback forces that resist needle insertion and rotation. An improved haptic volume rendering approach is used to calculate the forces for the haptic feedback. A real time 3D visualization with optional stereo view gives an overview of the punctured region. 2D visualizations of orthogonal slices enable a detailed impression of the anatomical context. In a first evaluation the visible human male data has been used to generate the virtual body. Several users with different medical experience tested the lumbar puncture trainer. The simulator gives a good haptic and visual impression of the needle insertion and the haptic volume rendering technique enables the feeling of segmented and unsegmented structures.

Background removal is a critical step in Visible Human Data (VHD) processing, which is the basic of all other researches. In this paper, a new segmentation algorithm based on the hybrid method for VHD background removal has been proposed, which combines a feature based segmentation method with a contour based one. The algorithm first determines the background part and the interested parts of an image at a coarse level by using its colour features, and then obtains a fine segmentation by using a Gradient Vector Flow (GVF) Snake model on the previous initial contour. Our test results on Chinese VHD show that the new algorithm is more robust and accurate than the previous methods.

This book covers all topics relevant for the design of haptic interfaces and teleoperation systems. The book provides the basic knowledge required for understanding more complex approaches and more importantly it introduces all issues that must be considered for designing efficient and safe haptic interfaces. Topics covered in this book provide insight into all relevant components of a haptic system. The reader is guided from understanding the virtual reality concept to the final goal of being able to design haptic interfaces for specific tasks such as nanomanipulation. The introduction chapter positions the haptic interfaces within the virtual reality context. In order to design haptic interfaces that will comply with human capabilities at least basic understanding of human sensors-motor system is required. An overview of this topic is provided in the chapter related to human haptics. The book does not try to introduce the state-of-the-art haptic interface solutions because these tend to change quickly. Only a careful selection of different kinematic configurations is shown to introduce the reader into this field. Mathematical models of virtual environment, collision detection and force rendering topics are strongly interrelated and are described in the next two chapters. The interaction with the virtual environment is simulated with a haptic interface. Impedance and admittance based approaches to haptic robot control are presented. Stability issues of haptic interaction are analyzed in details and solutions are proposed for guaranteeing stable and safe operation. Finally, haptic interaction is extended to teleoperation systems. Virtual fixtures which improve the teleoperation and human-robot cooperation in complex environments are covered next and the last chapter presents nanomanipulation as one specific example of teleoperation.

Extended-Reality Technologies: An Overview of Emerging Applications in Medical Education and Clinical Care.

In 1964, Dr. Barrows first introduced the standardized patients, who are individuals trained to imitate the pathological symptoms of the real patients, and involved them in teaching and clinical skills assessment for healthcare education. In recent decades, the application of the virtual patient has been rapidly grown and has been widely used in clinical or educational practice among residents, surgeons, or other medical professionals because the virtual patient is cost-effective and time-efficient [1]. The Food and Drug Administration (FDA) collaborated with the Foundation of Research on Information Technologies in Society (IT’IS Foundation, Zürich, Switzerland) to produce a virtual family, which is a set of anatomical computer-aided design (CAD) models of adults and children [2, 3], and those CAD models are used in electromagnetic, thermal, and computer fluid dynamics simulations. However, the meaning of virtual patients or models has varied across the recent years and more and more researchers tried to categorize the terminology of virtual patients. In general, virtual patients can be classified into seven major types including: case presentation, interactive patient scenarios, virtual patient game, high fidelity software simulation, human standardized patients, high fidelity manikins, and virtual standardized patients [4]. The virtual patients discussed in this study can be classified as interactive patient scenarios, whose application includes clinical reasoning, surgical planning, and disease diagnosis.

Inspection of the colon with an endoscope for early signs of cancer (colonoscopy) has become an extremely widespread procedure, since early treatment radically improves the outlook of patients. The procedure requires a close coordination between the sense of touch and vision to navigate the endoscope along the colon. This raises the need to develop efficient training methods for physicians. Training simulators based on virtual reality, where realistic graphics are combined with a mechatronic system providing haptic feedback, are alternative to traditional training methods. To provide physicians with realistic haptic sensations of an endoscopic procedure, we have designed a haptic interface, instrumented a clinical endoscope and combined them with a simulation software for colonoscopy. In this contribution, we present the mechatronic components of the simulator. The haptic interface is able to generate high forces using the combination of electrical motors and brakes in a compact design. Experiments were performed to determine the characteristics of the device. A model-based control has been implemented and the results show that the control successfully compensates for the device nonlinearities, such as friction. The proposed haptic interface, together with the virtual reality, form a highly realistic training simulator for endoscopic surgeons, applicable not only to colonoscopy, but also to similar interventions.

The U S. National Library of Medicine (NLM) has long been a world leader in the archiving and distribution of the print-based images of biology and medicine. NLM has also been a pioneer in the use of computer systems to encode and distribute textual knowledge of the life sciences. NLM's Long Range Planning effort of 1985-86 foresaw a coming era where NLM's Bibliographic and factual database services would be complemented by libraries of digital images, distributed over high speed computer networks. The NLM Planning Panel on Electronic Imaging recommended that NLM should undertake the building a digital image library consisting of computerized tomography (CT) and magnetic resonance (MR) images, and cryosection images of a representative, carefully selected and prepared male and female cadaver -- the “Visible Human Project®.” The male and female Visible Human data sets are now being made available through a license agreement with the NLM. The data sets are supporting a wide range of educational, diagnostic, treatment planning, and commercial uses. The NLM, in partnership with other U.S. government research agencies has begun a three prong effort within the Visible Human Project to address: the creation of a new online, interactive, digital head-and-neck atlas; the development of a tool kit of computational programs capable of automatically performing many of the basic data handling functions required for using Visible Human data in applications; and the improved resolution of future Visible Human data sets through the reduction of the anatomical artifacts introduced by the methods used to stabilize and section the anatomical materials and the development of staining and wide-spectrum methods for increasing tissue contrast.

The four-dimensional (4-D) NCAT phantom was developed to realistically model human anatomy based on the visible human data and cardiac and respiratory motions based on 4-D tagged magnetic resonance imaging and respiratory-gated CT data from normal human subjects. Currently, the 4-D NCAT phantom does not include the airway tree or its motion within the lungs. Also, each lung is defined with a single surface; the individual lobes are not distinguished. The authors further the development of the phantom by creating dynamic models for the individual lung lobes and for the airway tree in each lobe. NURBS surfaces for the lobes and an initial airway tree model (/spl sim/ 4 generations) were created through manual segmentation of the visible human data. A mathematical algorithm with physiological constraints was used to extend the original airway model to fill each lobe. For each parent airway branch inside a lobe, the algorithm extends the airway tree by creating two daughter branches modeled with cylindrical tubes. Parameters for the cylindrical tubes such as diameter, length, and angle are constrained based on flow parameters and available lung space.

Visible human data has been widely used in various medical research and computer science applications. We present a new application for this data: a method to classify which body segment a transverse cross section image belongs to. The labeling of the data is created with the guidance of an online body cross section tutorial. The visual properties of the images are represented using a variety of feature descriptors. To avoid problems that arise from the large dimensionality of features, feature selection is applied. The multi-class SVM is employed as the classifier. Both the CT scans and the color photographs of cryosections of the whole body (male and female) are used to test the proposed method. The high performance with overall accuracy above 98% on both the 2160 CT dataset and the 1870 cryosectional photos show the method is very promising. Because of its observed effectiveness on visible human data, we will extend our approach to classify figures in biomedical articles.

This study investigated the design of a virtual reality (VR) simulation integrating a haptic control interface for motor skill training. Twenty-four healthy participants were tested and trained in standardized psychomotor control tasks using native and VR forms with their nondominant hands in order to identify VR design features that might serve to accelerate motor learning. The study was also intended to make preliminary observations on the degree of specific motor skill development that can be achieved with a VR-based haptic simulation. Results revealed significant improvements in test performance following training for the VR with augmented haptic features with insignificant findings for the native task and VR with basic haptic features. Although performance during training was consistently better with the native task, a correspondence between the VR training and test task interfaces led to greater improvement in test performance as reported by a difference between baseline and post-test scores. These findings support use of VR-based haptic simulations of standardized psychomotor tests for motor skill training, including visual and haptic enhancements for effective pattern recognition and discrete movement of objects. The results may serve as an applicable guide for design of future haptic VR features.

Pressure contours on forming dies: Part 1. Simulation using physical modelling technique

A haptic interface is a device for operators to touch, feel and manipulate virtual environments. It senses the contact forces and gives operators a sensation of interacting with a real physical environment. Force feedback can improve the quality of the haptic feedback control in virtual reality applications. In this paper, a real time haptic interface is developed via control models in the Labview virtual environment, which is a combination of a touch force measurement haptic interface and a vibro-tactile CyberGlove serial haptic interface. The haptic interface is a prototype for real time feedback control on robotic integration of smart censors. This approach is easy to apply on various types of sensors, such as acoustic wave sensors, ultrasound array, etc., which will enhance the ability to communicate between the operators and the virtual environment. The flexiforce sensor and tactile sensor based haptic interface in force feedback systems will improve traditional robot visual sensing systems by a virtual feel.

Interpersonal bias based on weight and race is widespread in the clinical setting; it is crucial to investigate how emerging genomics technologies will interact with and influence such biases in the future. The current study uses a virtual reality (VR) simulation to investigate the influence of apparent patient race and provision of genomic information on medical students' implicit and explicit bias toward a virtual patient with obesity. Eighty-four third- and fourth-year medical students (64% female, 42% White) were randomized to interact with a simulated virtual patient who appeared as Black versus White, and to receive genomic risk information for the patient versus a control report. We assessed biased behavior during the simulated encounter and self-reported attitudes toward the virtual patient. Medical student participants tended to express more negative attitudes toward the White virtual patient than the Black virtual patient (both of whom had obesity) when genomic information was absent from the encounter. When genomic risk information was provided, this more often mitigated bias for the White virtual patient, whereas negative attitudes and bias against the Black virtual patient either remained consistent or increased. These patterns underscore the complexity of intersectional identities in clinical settings. Provision of genomic risk information was enough of a contextual shift to alter attitudes and behavior. This research leverages VR simulation to provide an early look at how emerging genomic technologies may differentially influence bias and stereotyping in clinical encounters.

Intrabody communication (IBC) is a promising approach for realizing information transmission among various wearable or implantable devices within bodyNET. The existing IBC studies are limited to the local or abstract geometric channel model, which is limited to providing efficient channel characteristics for the various transmitting scenarios. The aim of this article is to reconstruct a whole-body model with anatomical features and combine the field-circuit model for investigating the human channel characteristics. A visible human data (VHD) is established by segmentation and 3-D reconstruction of layered tissues. The transmission attenuation was simulated and analyzed through the finite element method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In vivo</i> experiments and phantom experiments were implemented to validate the simulation results. For the on-body communication, the transmission attenuation of the short distance was similar to the low-pass filter’s amplitude-frequency characteristics. For the implantable communication, the consistency coefficient of the attenuation between in-body to on-body and on-body to in-body was higher than 98%, indicating that the body channel is bidirectional. This work provides a repeatable and verified VHD-based whole-body model for the human channel attenuation investigation. It is an essential step for the further development of IBC, such as the transceiver design and the multinodes’ protocol for bodyNET.

Current methods for estimating and reporting radiation dose from CT examinations are largely patient-generic; the body size and hence dose variation from patient to patient is not reflected. Furthermore, the current protocol designs rely on dose as a surrogate for the risk of cancer incidence, neglecting the strong dependence of risk on age and gender. The purpose of this study was to develop a method for estimating patient-specific radiation dose and cancer risk from CT examinations. The study included two patients (a 5-week-old female patient and a 12-year-old male patient), who underwent 64-slice CT examinations (LightSpeed VCT, GE Healthcare) of the chest, abdomen, and pelvis at our institution in 2006. For each patient, a nonuniform rational B-spine (NURBS) based full-body computer model was created based on the patient's clinical CT data. Large organs and structures inside the image volume were individually segmented and modeled. Other organs were created by transforming an existing adult male or female full-body computer model (developed from visible human data) to match the framework defined by the segmented organs, referencing the organ volume and anthropometry data in ICRP Publication 89. A Monte Carlo program previously developed and validated for dose simulation on the LightSpeed VCT scanner was used to estimate patient-specific organ dose, from which effective dose and risks of cancer incidence were derived. Patient-specific organ dose and effective dose were compared with patient-generic CT dose quantities in current clinical use: the volume-weighted CT dose index (CTDIvol) and the effective dose derived from the dose-length product (DLP). The effective dose for the CT examination of the newborn patient (5.7 mSv) was higher but comparable to that for the CT examination of the teenager patient (4.9 mSv) due to the size-based clinical CT protocols at our institution, which employ lower scan techniques for smaller patients. However, the overall risk of cancer incidence attributable to the CT examination was much higher for the newborn (2.4 in 1000) than for the teenager (0.7 in 1000). For the two pediatric-aged patients in our study, CTDIvol underestimated dose to large organs in the scan coverage by 30%-48%. The effective dose derived from DLP using published conversion coefficients differed from that calculated using patient-specific organ dose values by -57% to 13%, when the tissue weighting factors of ICRP 60 were used, and by -63% to 28%, when the tissue weighting factors of ICRP 103 were used. It is possible to estimate patient-specific radiation dose and cancer risk from CT examinations by combining a validated Monte Carlo program with patient-specific anatomical models that are derived from the patients' clinical CT data and supplemented by transformed models of reference adults. With the construction of a large library of patient-specific computer models encompassing patients of all ages and weight percentiles, dose and risk can be estimated for any patient prior to or after a CT examination. Such information may aid in decisions for image utilization and can further guide the design and optimization of CT technologies and scan protocols.