The pre-scientific concept of a "soul": A neurophenomenological hypothesis about its origin.

In this contribution I will argue that our traditional, folk-phenomenological concept of a "soul� may have its origins in accurate and truthful first-person reports about the experiential content of a specific neurophenomenological state-class. This class of phenomenal states is called the "Out-of-body experience� (OBE hereafter), and I will offer a detailed description in section 3 of this paper. The relevant type of conscious experience seems to possess a culturally invariant cluster of functional and phenomenal core properties: it is a specific kind of conscious experience, which can in principle be undergone by every human being. I propose that it probably is one of the most central semantic roots of our everyday, folk-phenomenological idea of what a soul actually is

Computed Tomography-Derived 3D Modeling to Guide Sizing and Planning of Transcatheter Mitral Valve Interventions

A plethora of catheter-based strategies have been developed to treat mitral valve disease. Evolving 3-dimensional (3D) multidetector computed tomography (MDCT) technology can accurately reconstruct the mitral valve by means of 3-dimensional computational modeling (3DCM) to allow virtual implantation of catheter-based devices. 3D printing complements computational modeling and offers implanting physician teams the opportunity to evaluate devices in life-size replicas of patient-specific cardiac anatomy. MDCT-derived 3D computational and 3D-printed modeling provides unprecedented insights to facilitate hands-on procedural planning, device training, and retrospective procedural evaluation. This overview summarizes current concepts and provides insight into the application of MDCT-derived 3DCM and 3D printing for the planning of transcatheter mitral valve replacement and closure of paravalvular leaks. Additionally, future directions in the development of 3DCM will be discussed

Policy needs and options for a common approach towards modelling and simulation of human physiology and diseases with a focus on the virtual physiological human.

Life is the result of an intricate systemic interaction between many processes occurring at radically different spatial and temporal scales. Every day, worldwide biomedical research and clinical practice produce a huge amount of information on such processes. However, this information being highly fragmented, its integration is largely left to the human actors who find this task increasingly and ever more demanding in a context where the information available continues to increase exponentially. Investments in the Virtual Physiological Human (VPH) research are largely motivated by the need for integration in healthcare. As all health information becomes digital, the complexity of health care will continue to evolve, translating into an ever increasing pressure which will result from a growing demand in parallel to limited budgets. Hence, the best way to achieve the dream of personalised, preventive, and participative medicine at sustainable costs will be through the integration of all available data, information and knowledge

A Rapid and Computationally Inexpensive Method to Virtually Implant Current and Next-Generation Stents into Subject-Specific Computational Fluid Dynamics Models

Computational modeling is often used to quantify hemodynamic alterations induced by stenting, but frequently uses simplified device or vascular representations. Based on a series of Boolean operations, we developed an efficient and robust method for assessing the influence of current and next-generation stents on local hemodynamics and vascular biomechanics quantified by computational fluid dynamics. Stent designs were parameterized to allow easy control over design features including the number, width and circumferential or longitudinal spacing of struts, as well as the implantation diameter and overall length. The approach allowed stents to be automatically regenerated for rapid analysis of the contribution of design features to resulting hemodynamic alterations. The applicability of the method was demonstrated with patient-specific models of a stented coronary artery bifurcation and basilar trunk aneurysm constructed from medical imaging data. In the coronary bifurcation, we analyzed the hemodynamic difference between closed-cell and open-cell stent geometries. We investigated the impact of decreased strut size in stents with a constant porosity for increasing flow stasis within the stented basilar aneurysm model. These examples demonstrate the current method can be used to investigate differences in stent performance in complex vascular beds for a variety of stenting procedures and clinical scenarios

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Image-guided port placement for minimally invasive cardiac surgery

Minimally invasive surgery is becoming popular for a number of interventions. Use of robotic surgical systems in coronary artery bypass intervention offers many benefits to patients, but is however limited by remaining challenges in port placement. Choosing the entry ports for the robotic tools has a large impact on the outcome of the surgery, and can be assisted by pre-operative planning and intra-operative guidance techniques. In this thesis, pre-operative 3D computed tomography (CT) imaging is used to plan minimally invasive robotic coronary artery bypass (MIRCAB) surgery. From a patient database, port placement optimization routines are implemented and validated. Computed port placement configurations approximated past expert chosen configurations with an error of 13.7 ±5.1 mm. Following optimization, statistical classification was used to assess patient candidacy for MIRCAB. Various pattern recognition techniques were used to predict MIRCAB success, and could be used in the future to reduce conversion rates to conventional open-chest surgery. Gaussian, Parzen window, and nearest neighbour classifiers all proved able to detect ‘candidate’ and ‘non-candidate’ MIRCAB patients. Intra-operative registration and laser projection of port placements was validated on a phantom and then evaluated in four patient cases. An image-guided laser projection system was developed to map port placement plans from pre-operative 3D images. Port placement mappings on the phantom setup were accurate with an error of 2.4 ± 0.4 mm. In the patient cases, projections remained within 1 cm of computed port positions. Misregistered port placement mappings in human trials were due mainly to the rigid-body registration assumption and can be improved by non-rigid techniques. Overall, this work presents an integrated approach for: 1) pre-operative port placement planning and classification of incoming MIRCAB patients; and 2) intra-operative guidance of port placement. Effective translation of these techniques to the clinic will enable MIRCAB as a more efficacious and accessible procedure

INTERFACE DESIGN FOR A VIRTUAL REALITY-ENHANCED IMAGE-GUIDED SURGERY PLATFORM USING SURGEON-CONTROLLED VIEWING TECHNIQUES

Initiative has been taken to develop a VR-guided cardiac interface that will display and deliver information without affecting the surgeons’ natural workflow while yielding better accuracy and task completion time than the existing setup. This paper discusses the design process, the development of comparable user interface prototypes as well as an evaluation methodology that can measure user performance and workload for each of the suggested display concepts. User-based studies and expert recommendations are used in conjunction to es­ tablish design guidelines for our VR-guided surgical platform. As a result, a better understanding of autonomous view control, depth display, and use of virtual context, is attained. In addition, three proposed interfaces have been developed to allow a surgeon to control the view of the virtual environment intra-operatively. Comparative evaluation of the three implemented interface prototypes in a simulated surgical task scenario, revealed performance advantages for stereoscopic and monoscopic biplanar display conditions, as well as the differences between three types of control modalities. One particular interface prototype demonstrated significant improvement in task performance. Design recommendations are made for this interface as well as the others as we prepare for prospective development iterations

Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB