Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

3D measuring tool for estimating femoroacetabular impingement.

Osteoarthritis of the hip is commonly caused by the repetitive contact between abnormal skeletal prominences between the anterosuperior femoral head-neck junction and the rim of the acetabular socket. Current methods for estimating femoroacetabular impingement by analyzing the sphericity of the femoral head require manual measurements which are both inaccurate and open to interpretation. In this research we provide a prototype software tool for improving this estimation.

VCath: a tablet-based neurosurgery training tool.

VCath is a neurosurgery training tool for the catheterization of the lateral ventricle that has been designed for use on tablet devices. We believe this is the first use of a tablet (iPad) for this purpose and demonstrates future utility for this approach, particularly for Objective Structured Clinical Exams (OSCEs). This paper outlines the implementation and use of VCath.

A directed particle system for optimised visualization of blood flow in complex networks.

This paper introduces a novel technique for the visualization of blood (or other fluid) flowing through a complex 3D network of vessels. The Directed Particle System (DPS) approach is loosely based on the computer graphics concept of flocking agents. It has been developed and optimised to provide effective real time visualization and qualitative simulation of fluid flow. There are many potential applications of DPS, and one example - a decision support tool for coronary collateralization - is discussed.

A method to compute respiration parameters for patient-based simulators.

We propose a method to automatically tune a patient-based virtual environment training simulator for abdominal needle insertion. The key attributes to be customized in our framework are the elasticity of soft-tissues and the respiratory model parameters. The estimation is based on two 3D Computed Tomography (CT) scans of the same patient at two different time steps. Results are presented on four patients and show that our new method leads to better results than our previous studies with manually tuned parameters.

Immersive Virtual Reality for the Cognitive Rehabilitation of Stroke Survivors.

We present the results of a double-blind phase 2b randomized control trial that used a custom built virtual reality environment for the cognitive rehabilitation of stroke survivors. A stroke causes damage to the brain and problem solving, memory and task sequencing are commonly affected. The brain can recover to some extent, however, and stroke patients have to relearn how to carry out activities of daily living. We have created an application called VIRTUE to enable such activities to be practiced using immersive virtual reality. Gamification techniques enhance the motivation of patients such as by making the level of difficulty of a task increase over time. The design and implementation of VIRTUE is described together with the results of the trial conducted within the Stroke Unit of a large hospital. We report on the safety and acceptability of VIRTUE. We have also observed particular benefits of VR treatment for stroke survivors that experienced more severe cognitive impairment, and an encouraging reduction in time spent in the hospital for all patients that received the VR treatment.

Realistic visualization of living brain tissue.

This paper presents an advanced method of visualizing the surface appearance of living brain tissue. We have been granted access to the operating theatre during neurosurgical procedures to obtain colour data via calibrated photography of exposed brain tissue. The specular reflectivity of the brain's surface is approximated by analyzing a gelatine layer applied to animal flesh. This provides data for a bidirectional reflectance distribution function (BRDF) that is then used the rendering process. Rendering is achieved in realtime by utilizing the GPU, and includes support for ambient occlusion, advanced texturing, sub surface scattering and specularity. Our goal is to investigate whether realistic visualizations of living anatomy can be produced and so provide added value to anatomy education.

Modification of commercial force feedback hardware for needle insertion simulation.

A SensAble Omni force feedback device has been modified to increase the face validity of a needle insertion simulation. The new end effector uses a real needle hub and shortened needle shaft in place of the Omni's pre-fitted pen shaped end effector. This modification facilitates correct procedural training through the simulation of co-located visual and haptic cues in an augmented reality approach to simulation. The development of the new end effector is described and a pictorial guide to its manufacture and the fitting process is provided. Initial results from face validation studies bode well for the fidelity of this low cost device.

Quantizing the void: extending Web3D for space-filling haptic meshes.

In this paper we summarize the progress of the Web3D scene graph model, and associated standards, specifically Extensible 3D (X3D) in the domain of medical simulation. Historically, the Web3D nodesets have focused on the representation and rendering of point, line or surface geometry. More recently, significant progress in X3D Volume rendering has been made available through the co-operative DICOM work item, n-Dimensional Presentation States. However, here we outline the need for a standard for simulation meshes and review several related approaches. As a result, we propose preliminary requirements for a simulation mesh standard and provide several use case scenarios of how Web3D and haptic technologies can aid the fulfillment of these requirements. We conclude with an X3D proposal to describe simulation meshes for soft (deformable) bodies.

A model for flexible tools used in minimally invasive medical virtual environments.

Within the limits of current technology, many applications of a virtual environment will trade-off accuracy for speed. This is not an acceptable compromise in a medical training application where both are essential. Efficient algorithms must therefore be developed. The purpose of this project is the development and validation of a novel physics-based real time tool manipulation model, which is easy to integrate into any medical virtual environment that requires support for the insertion of long flexible tools into complex geometries. This encompasses medical specialities such as vascular interventional radiology, endoscopy, and laparoscopy, where training, prototyping of new instruments/tools and mission rehearsal can all be facilitated by using an immersive medical virtual environment. Our model recognises and uses accurately patient specific data and adapts to the geometrical complexity of the vessel in real time.

VRIA: A Web-Based Framework for Creating Immersive Analytics Experiences.

We present VRIA, a Web-based framework for creating Immersive Analytics (IA) experiences in Virtual Reality. VRIA is built upon WebVR, A-Frame, React and D3.js, and offers a visualization creation workflow which enables users, of different levels of expertise, to rapidly develop Immersive Analytics experiences for the Web. The use of these open-standards Web-based technologies allows us to implement VR experiences in a browser and offers strong synergies with popular visualization libraries, through the HTML Document Object Model (DOM). This makes VRIA ubiquitous and platform-independent. Moreover, by using WebVR's progressive enhancement, the experiences VRIA creates are accessible on a plethora of devices. We elaborate on our motivation for focusing on open-standards Web technologies, present the VRIA creation workflow and detail the underlying mechanics of our framework. We also report on techniques and optimizations necessary for implementing Immersive Analytics experiences on the Web, discuss scalability implications of our framework, and present a series of use case applications to demonstrate the various features of VRIA. Finally, we discuss current limitations of our framework, the lessons learned from its development, and outline further extensions.

De-smokeGCN: Generative Cooperative Networks for Joint Surgical Smoke Detection and Removal.

Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this article, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images.

Cost effective ultrasound imaging training mentor for use in developing countries.

This paper reports on a low cost system for training ultrasound imaging techniques. The need for such training is particularly acute in developing countries where typically ultrasound scanners remain idle due to the lack of experienced sonographers. The system described below is aimed at a PC platform but uses interface components from the Nintendo Wii games console. The training software is being designed to support a variety of patient case studies, and also supports remote tutoring over the internet.

Developing an immersive ultrasound guided needle puncture simulator.

We present an integrated system for training ultrasound guided needle puncture. Our aim is to provide a cost effective and validated training tool that uses actual patient data to enable interventional radiology trainees to learn how to carry out image-guided needle puncture. The input data required is a computed tomography scan of the patient that is used to create the patient specific models. Force measurements have been made on real tissue and the resulting data is incorporated into the simulator. Respiration and soft tissue deformations are also carried out to further improve the fidelity of the simulator.

Efficient soft tissue deformation using charged particles.

As the power of personal computing increases, there is a greater demand for high fidelity virtual environments for medical training simulators. However, it is still a challenge to provide realistic soft tissue deformation especially when haptic feedback is also required. This paper presents a new approach to soft tissue deformation using a novel Charged Particle method to efficiently model both the structure and haptic properties of anatomy in real time.

An Information-Theoretic Approach to the Cost-benefit Analysis of Visualization in Virtual Environments.

Visualization and virtual environments (VEs) have been two interconnected parallel strands in visual computing for decades. Some VEs have been purposely developed for visualization applications, while many visualization applications are exemplary showcases in general-purpose VEs. Because of the development and operation costs of VEs, the majority of visualization applications in practice have yet to benefit from the capacity of VEs. In this paper, we examine this status quo from an information-theoretic perspective. Our objectives are to conduct cost-benefit analysis on typical VE systems (including augmented and mixed reality, theater-based systems, and large powerwalls), to explain why some visualization applications benefit more from VEs than others, and to sketch out pathways for the future development of visualization applications in VEs. We support our theoretical propositions and analysis using theories and discoveries in the literature of cognitive sciences and the practical evidence reported in the literatures of visualization and VEs.

SLAM-based dense surface reconstruction in monocular Minimally Invasive Surgery and its application to Augmented Reality.

BACKGROUND AND OBJECTIVE: While Minimally Invasive Surgery (MIS) offers considerable benefits to patients, it also imposes big challenges on a surgeon's performance due to well-known issues and restrictions associated with the field of view (FOV), hand-eye misalignment and disorientation, as well as the lack of stereoscopic depth perception in monocular endoscopy. Augmented Reality (AR) technology can help to overcome these limitations by augmenting the real scene with annotations, labels, tumour measurements or even a 3D reconstruction of anatomy structures at the target surgical locations. However, previous research attempts of using AR technology in monocular MIS surgical scenes have been mainly focused on the information overlay without addressing correct spatial calibrations, which could lead to incorrect localization of annotations and labels, and inaccurate depth cues and tumour measurements. In this paper, we present a novel intra-operative dense surface reconstruction framework that is capable of providing geometry information from only monocular MIS videos for geometry-aware AR applications such as site measurements and depth cues. We address a number of compelling issues in augmenting a scene for a monocular MIS environment, such as drifting and inaccurate planar mapping. METHODS: A state-of-the-art Simultaneous Localization And Mapping (SLAM) algorithm used in robotics has been extended to deal with monocular MIS surgical scenes for reliable endoscopic camera tracking and salient point mapping. A robust global 3D surface reconstruction framework has been developed for building a dense surface using only unorganized sparse point clouds extracted from the SLAM. The 3D surface reconstruction framework employs the Moving Least Squares (MLS) smoothing algorithm and the Poisson surface reconstruction framework for real time processing of the point clouds data set. Finally, the 3D geometric information of the surgical scene allows better understanding and accurate placement AR augmentations based on a robust 3D calibration. RESULTS: We demonstrate the clinical relevance of our proposed system through two examples: (a) measurement of the surface; (b) depth cues in monocular endoscopy. The performance and accuracy evaluations of the proposed framework consist of two steps. First, we have created a computer-generated endoscopy simulation video to quantify the accuracy of the camera tracking by comparing the results of the video camera tracking with the recorded ground-truth camera trajectories. The accuracy of the surface reconstruction is assessed by evaluating the Root Mean Square Distance (RMSD) of surface vertices of the reconstructed mesh with that of the ground truth 3D models. An error of 1.24 mm for the camera trajectories has been obtained and the RMSD for surface reconstruction is 2.54 mm, which compare favourably with previous approaches. Second, in vivo laparoscopic videos are used to examine the quality of accurate AR based annotation and measurement, and the creation of depth cues. These results show the potential promise of our geometry-aware AR technology to be used in MIS surgical scenes. CONCLUSIONS: The results show that the new framework is robust and accurate in dealing with challenging situations such as the rapid endoscopy camera movements in monocular MIS scenes. Both camera tracking and surface reconstruction based on a sparse point cloud are effective and operated in real-time. This demonstrates the potential of our algorithm for accurate AR localization and depth augmentation with geometric cues and correct surface measurements in MIS with monocular endoscopes.

The Implementation and Validation of a Virtual Environment for Training Powered Wheelchair Manoeuvres.

Navigating a powered wheelchair and avoiding collisions is often a daunting task for new wheelchair users. It takes time and practice to gain the coordination needed to become a competent driver and this can be even more of a challenge for someone with a disability. We present a cost-effective virtual reality (VR) application that takes advantage of consumer level VR hardware. The system can be easily deployed in an assessment centre or for home use, and does not depend on a specialized high-end virtual environment such as a Powerwall or CAVE. This paper reviews previous work that has used virtual environments technology for training tasks, particularly wheelchair simulation. We then describe the implementation of our own system and the first validation study carried out using thirty three able bodied volunteers. The study results indicate that at a significance level of 5 percent then there is an improvement in driving skills from the use of our VR system. We thus have the potential to develop the competency of a wheelchair user whilst avoiding the risks inherent to training in the real world. However, the occurrence of cybersickness is a particular problem in this application that will need to be addressed.

Efficient modelling of soft tissue using particle systems.

The ever improving price-performance ratio of personal computers is making a significant contribution to widening the accessibility of training simulators for a wide variety of medical procedures. High fidelity solutions are becoming more and more sought after. However, the problem of providing realistic soft tissue deformation in real time remains, particularly if haptic interaction is also required. This paper presents a new approach for efficient soft tissue deformation using particle systems to model both structure and haptic properties of anatomy. We are applying this technique to a simulator for interventional radiology procedures, but it can easily be adapted for other medical domains.