The study of gastric motility is important to gain an understanding of disease and nutrition. Many studies focused on gastric motility require the use of synthetic tracers to reconstruct the motion of content. Our study instead exploits the properties of a specific magnetic resonance imaging (MRI) acquisition protocol, combined with multi-objective optimised clustering to map the motion of peas in a human stomach. We chose Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimise the starting positions for a modified k-means to create optimum clusters. We compared our optimisation approach with a pure random search (PRS) that took an equal amount of processing time. Since we have no ground truth available, we have created alternative methods of evaluation: if each pea’s velocity is within an expected range, and if each pea’s motion is correlated with neighbouring peas. We found that the optimised version was an improvement over the PRS for both the range accuracy objective (56.25

Scan planning for X-ray CT systems can be difficult due to the large number of elements affecting scan quality. The use of X-ray simulation can answer feasibility questions, however existing methods are focused on experts who are familiar with XCT and programming knowledge. WebCT is a user-centric application for performing virtual XCT scans with the validated X-ray simulator gVirtualXray. Focused on accessibility, the interface allows changing all scanning parameters; from tube characteristics to detector energy response, while allowing full-scale simulation and reconstruction in minutes. WebCT is available as a free, open-source application, giving full control over a virtual lab-CT or synchrotron system. Configurations can be saved, shared, or even imported from many popular XCT dataset formats. We demonstrate in this paper the use of WebCT as a scan planning tool, using a simple CAD mockup to select filtration based on transmission before scanning.

Many studies focused on gastric motility require the use of synthetic tracers to map the motion of content. Our study instead takes advantage of an unusual MRI acquisition protocol, combined with multi-objective optimised clustering to map the motion of food (peas, a natural 'tracer') in a human stomach. We chose NSGA-II to optimise the starting positions for a modified k-means to create optimum clusters. We compared our optimisation approach with a purely random approach that took an equal amount of processing time. Since we have no ground truth available, we have created alternative measures to evaluate our solutions: if the resulting pea velocities are within an expected range, and if each pea's motion is correlated with neighbouring peas. We found that the optimised version has a significant improvement over the purely random search. Furthermore, we found many interesting food motion behaviours, such as correlated pea motion and more complex motion dynamics such as collision. Overall we found that the combined optimisation and clustering approach produced interesting findings relating to food dynamics in a human stomach.

gVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT.

Background and objectives: This study provides a quantitative comparison of images created using gVirtu-alXray (gVXR) to both Monte Carlo (MC) and real images of clinically realistic phantoms. gVirtualXray is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes. Methods: Images are generated with gVirtualXray and compared with a corresponding ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. When real images are involved, the simulations are used in an image registration framework so that the two images are aligned. Results: The mean absolute percentage error (MAPE) between the images simulated with gVirtualXray and MC is 3.12%, the zero-mean normalised cross-correlation (ZNCC) is 99.96% and the structural similarity index (SSIM) is 0.99. The run-time is 10 days for MC and 23 ms with gVirtualXray. Images simulated using surface models segmented from a CT scan of the Lungman chest phantom were similar to (i) DRRs computed from the CT volume and (ii) an actual digital radiograph. CT slices reconstructed from images simulated with gVirtualXray were comparable to the corresponding slices of the original CT volume. Conclusions: When scattering can be ignored, accurate images that would take days using MC can be generated in milliseconds with gVirtualXray. This speed of execution enables the use of repetitive simu-lations with varying parameters, e.g. to generate training data for a deep-learning algorithm, and to min-imise the objective function of an optimisation problem in image registration. The use of surface models enables the combination of X-ray simulation with real-time soft-tissue deformation and character anima-tion, which can be deployed in virtual reality applications. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes. We produced four Jupyter Notebooks to compare images simulated with gVXR and ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. Image registration was deployed in two Notebooks to align the simulated image on the corresponding ground truth image. We demonstrated that accurate images can be generated in milliseconds with gVirtualXray when scattering can be ignored.

Many studies aim to create fully automatic classification pipelines where human input is minimal or non-existent. However, for certain classification problems, where data is scarce or difficult to interpret, utilising the human in the loop (HITL) can improve classification performance. Furthermore, combining traditional classification methods with a state-of-the-art convolutional neural network (CNN) may achieve good performance. Our study therefore aims to provide an ensemble method that outperforms a CNN and traditional classifiers, and to create an iterative HITL pipeline to automatically detect peas from magnetic resonance imaging (MRI) images. Our ensemble contains one CNN (AlexNet), and two traditional classifiers that take radiomic data (Quadratic Discriminant Analysis (QDA) and a support vector machine (SVM)). At each iteration, the humans in the loop correct the results of the previous classification. This data is then added to our training and testing data to improve the pea detection at the next iteration. In most cases our ensemble performs better than the CNN or traditional classifiers alone but is not a perfect trend and in some of the cases the CNN performs marginally better. Overall, we found that the addition of HITL improves performance compared to without.

The COVID-19 pandemic has been a period where time-series of disease statistics, such as the number of cases or vaccinations, have been intensively used by public health professionals to estimate how their region compares to others and estimate what future could look like at home. Conventional visualizations are often limited in terms of advanced comparative features and in supporting forecasting systematically. This paper presents a visual analytics approach to support data-driven prediction based on a search-analyze-predict process comprising a multi-metric, multi-criteria time-series search method and a data-driven prediction technique. These are supported by a visualization framework for the comprehensive comparison of multiple time-series. We inform the design of our approach by getting iterative feedback from public health experts globally, and evaluate it both quantitatively and qualitatively.

Particle Swarm Optimisation (PSO) and Evolutionary Algorithms (EAs) differ in various ways, in particular with respect to information sharing and diversity management, making their scopes of applications very diverse. Combining the advantages of both approaches is very attractive and has been successfully achieved through hybridisation. Another possible improvement, notably for addressing scalability issues, is cooperation. It has first been developed for co-evolution in EA techniques and it is now used in PSO. However, until now, attempts to make PSO cooperate have been based on multi-population schemes almost exclusively. The focus of this paper is set on single-population schemes, or fine-grained cooperation. By analogy with an evolutionary scheme that has long been proved effective, the fly algorithm (FA), we design and compare a cooperative PSO (coPSO), and a PSO-flavoured fly algorithm. Experiments run on a benchmark, the Lamp problem, show that fine-grained cooperation based on marginal fitness evaluations and steady-state schemes outperforms classical techniques when the dimension of the problem increases. These preliminary results highlight interesting future directions of research on fine-grained cooperation schemes, by combining features of PSO and FA.

Open AccessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article Dykes Jason, Abdul-Rahman Alfie, Archambault Daniel, Bach Benjamin, Borgo Rita, Chen Min, Enright Jessica, Fang Hui, Firat Elif E., Freeman Euan, Gönen Tuna, Harris Claire, Jianu Radu, John Nigel W., Khan Saiful, Lahiff Andrew, Laramee Robert S., Matthews Louise, Mohr Sibylle, Nguyen Phong H., Rahat Alma A. M., Reeve Richard, Ritsos Panagiotis D., Roberts Jonathan C., Slingsby Aidan, Swallow Ben, Torsney-Weir Thomas, Turkay Cagatay, Turner Robert, Vidal Franck P., Wang Qiru, Wood Jo and Xu Kai 2022Correction to: 'Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations' (2022) by Dykes et al.Phil. Trans. R. Soc. A.38020220296http://doi.org/10.1098/rsta.2022.0296SectionOpen AccessCorrectionCorrection to: 'Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations' (2022) by Dykes et al. Jason Dykes Jason Dykes Google Scholar Find this author on PubMed Search for more papers by this author , Alfie Abdul-Rahman Alfie Abdul-Rahman Google Scholar Find this author on PubMed Search for more papers by this author , Daniel Archambault Daniel Archambault Google Scholar Find this author on PubMed Search for more papers by this author , Benjamin Bach Benjamin Bach Google Scholar Find this author on PubMed Search for more papers by this author , Rita Borgo Rita Borgo Google Scholar Find this author on PubMed Search for more papers by this author , Min Chen Min Chen Google Scholar Find this author on PubMed Search for more papers by this author , Jessica Enright Jessica Enright Google Scholar Find this author on PubMed Search for more papers by this author , Hui Fang Hui Fang Google Scholar Find this author on PubMed Search for more papers by this author , Elif E. Firat Elif E. Firat Google Scholar Find this author on PubMed Search for more papers by this author , Euan Freeman Euan Freeman Google Scholar Find this author on PubMed Search for more papers by this author , Tuna Gönen Tuna Gönen Google Scholar Find this author on PubMed Search for more papers by this author , Claire Harris Claire Harris Google Scholar Find this author on PubMed Search for more papers by this author , Radu Jianu Radu Jianu Google Scholar Find this author on PubMed Search for more papers by this author , Nigel W. John Nigel W. John Google Scholar Find this author on PubMed Search for more papers by this author , Saiful Khan Saiful Khan Google Scholar Find this author on PubMed Search for more papers by this author , Andrew Lahiff Andrew Lahiff Google Scholar Find this author on PubMed Search for more papers by this author , Robert S. Laramee Robert S. Laramee Google Scholar Find this author on PubMed Search for more papers by this author , Louise Matthews Louise Matthews Google Scholar Find this author on PubMed Search for more papers by this author , Sibylle Mohr Sibylle Mohr Google Scholar Find this author on PubMed Search for more papers by this author , Phong H. Nguyen Phong H. Nguyen Google Scholar Find this author on PubMed Search for more papers by this author , Alma A. M. Rahat Alma A. M. Rahat Google Scholar Find this author on PubMed Search for more papers by this author , Richard Reeve Richard Reeve Google Scholar Find this author on PubMed Search for more papers by this author , Panagiotis D. Ritsos Panagiotis D. Ritsos Google Scholar Find this author on PubMed Search for more papers by this author , Jonathan C. Roberts Jonathan C. Roberts Google Scholar Find this author on PubMed Search for more papers by this author , Aidan Slingsby Aidan Slingsby Google Scholar Find this author on PubMed Search for more papers by this author , Ben Swallow Ben Swallow Google Scholar Find this author on PubMed Search for more papers by this author , Thomas Torsney-Weir Thomas Torsney-Weir Google Scholar Find this author on PubMed Search for more papers by this author , Cagatay Turkay Cagatay Turkay Google Scholar Find this author on PubMed Search for more papers by this author , Robert Turner Robert Turner Google Scholar Find this author on PubMed Search for more papers by this author , Franck P. Vidal Franck P. Vidal Google Scholar Find this author on PubMed Search for more papers by this author , Qiru Wang Qiru Wang Google Scholar Find this author on PubMed Search for more papers by this author , Jo Wood Jo Wood Google Scholar Find this author on PubMed Search for more papers by this author and Kai Xu Kai Xu Google Scholar Find this author on PubMed Search for more papers by this author Jason Dykes Jason Dykes Google Scholar Find this author on PubMed , Alfie Abdul-Rahman Alfie Abdul-Rahman Google Scholar Find this author on PubMed , Daniel Archambault Daniel Archambault Google Scholar Find this author on PubMed , Benjamin Bach Benjamin Bach Google Scholar Find this author on PubMed , Rita Borgo Rita Borgo Google Scholar Find this author on PubMed , Min Chen Min Chen Google Scholar Find this author on PubMed , Jessica Enright Jessica Enright Google Scholar Find this author on PubMed , Hui Fang Hui Fang Google Scholar Find this author on PubMed , Elif E. Firat Elif E. Firat Google Scholar Find this author on PubMed , Euan Freeman Euan Freeman Google Scholar Find this author on PubMed , Tuna Gönen Tuna Gönen Google Scholar Find this author on PubMed , Claire Harris Claire Harris Google Scholar Find this author on PubMed , Radu Jianu Radu Jianu Google Scholar Find this author on PubMed , Nigel W. John Nigel W. John Google Scholar Find this author on PubMed , Saiful Khan Saiful Khan Google Scholar Find this author on PubMed , Andrew Lahiff Andrew Lahiff Google Scholar Find this author on PubMed , Robert S. Laramee Robert S. Laramee Google Scholar Find this author on PubMed , Louise Matthews Louise Matthews Google Scholar Find this author on PubMed , Sibylle Mohr Sibylle Mohr Google Scholar Find this author on PubMed , Phong H. Nguyen Phong H. Nguyen Google Scholar Find this author on PubMed , Alma A. M. Rahat Alma A. M. Rahat Google Scholar Find this author on PubMed , Richard Reeve Richard Reeve Google Scholar Find this author on PubMed , Panagiotis D. Ritsos Panagiotis D. Ritsos Google Scholar Find this author on PubMed , Jonathan C. Roberts Jonathan C. Roberts Google Scholar Find this author on PubMed , Aidan Slingsby Aidan Slingsby Google Scholar Find this author on PubMed , Ben Swallow Ben Swallow Google Scholar Find this author on PubMed , Thomas Torsney-Weir Thomas Torsney-Weir Google Scholar Find this author on PubMed , Cagatay Turkay Cagatay Turkay Google Scholar Find this author on PubMed , Robert Turner Robert Turner Google Scholar Find this author on PubMed , Franck P. Vidal Franck P. Vidal Google Scholar Find this author on PubMed , Qiru Wang Qiru Wang Google Scholar Find this author on PubMed , Jo Wood Jo Wood Google Scholar Find this author on PubMed and Kai Xu Kai Xu Google Scholar Find this author on PubMed Published:12 September 2022https://doi.org/10.1098/rsta.2022.0296This article corrects the followingResearch ArticleVisualization for epidemiological modelling: challenges, solutions, reflections and recommendationshttps://doi.org/10.1098/rsta.2021.0299 Jason Dykes, Alfie Abdul-Rahman, Daniel Archambault, Benjamin Bach, Rita Borgo, Min Chen, Jessica Enright, Hui Fang, Elif E. Firat, Euan Freeman, Tuna Gönen, Claire Harris, Radu Jianu, Nigel W. John, Saiful Khan, Andrew Lahiff, Robert S. Laramee, Louise Matthews, Sibylle Mohr, Phong H. Nguyen, Alma A. M. Rahat, Richard Reeve, Panagiotis D. Ritsos, Jonathan C. Roberts, Aidan Slingsby, Ben Swallow, Thomas Torsney-Weir, Cagatay Turkay, Robert Turner, Franck P. Vidal, Qiru Wang, Jo Wood and Kai Xu volume 380issue 2233Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences15 August 2022 Phil. Trans. R. Soc. A 380, 20210299. (Published online 15 August 2022). (https://doi.org/10.1098/rsta.2021.0299) In the original version of this article, references 113–120, 123–140 and 143 were incorrectly numbered. This has been corrected on the publisher's website. Previous Article VIEW FULL TEXT DOWNLOAD PDF FiguresRelatedReferencesDetailsRelated articlesVisualization for epidemiological modelling: challenges, solutions, reflections and recommendations15 August 2022Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences This Issue31 October 2022Volume 380Issue 2235Theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks' compiled and edited by Luke G. Bennetts, Cecilia M. Bitz, Daniel L. Feltham, Alison L. Kohout and Michael H. Meylan Article InformationDOI:https://doi.org/10.1098/rsta.2022.0296PubMed:36088934Published by:Royal SocietyPrint ISSN:1364-503XOnline ISSN:1471-2962History: Manuscript received23/08/2022Manuscript accepted23/08/2022Published online12/09/2022Published in print31/10/2022 License:© 2022 The Authors.Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. Citations and impact Subjectscomputer modelling and simulationhuman-computer interaction

The presence of strong imaging artefacts in microtomographic X-ray data makes the CAD modelling process difficult to carry out. As an alternative to traditional image segmentation techniques, we propose to register the CAD models by deploying a realistic X-ray simulation on GPU in an optimisation framework. A user study was also conducted to compare the measurements made manually by a cohort of volunteers and those produced with our framework. Our implementation relies on open source software only. We numerically modelled the real experiment, taking into account geometrical properties as well as beam hardening, impulse response of the detector, phase contrast, and photon noise. Parameters of the overall model are then optimised so that X-ray projections of the registered the CAD models match the projections from an actual experiment. It appeared that manual measurements can be variable and subject to bias whereas our framework produced more reliable results. The features seen in the real CT image, including artefacts, were accurately replicated in the CT image reconstructed from the simulated data after registration: (i) linear attenuation coefficients are comparable for all the materials, (ii) geometrical properties are accurately recovered, and (iii) simulated images reproduce observed experimental artefacts. We showed that the choice of objective function is crucial to produce high fidelity results. We also demonstrated how to automatically produce CAD models as an optimisation problem, producing a high cross-correlation between the experimental CT slice and the simulated CT slice. These results pave the way towards the use of fast realistic simulation for accurate CAD modelling in tomographic X-ray data.

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'.

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.

PURPOSE:Traditional undergraduate radiographer training mixes academic lectures and clinical practice. Our goal is to bridge the current disconnection between theory and practice in a safe environment, avoiding the risk of radiation for both practitioners and patients. To this end, this research proposes a new software to teach diagnostic radiography using real-time interactive X-ray simulation and patient positioning.METHODS:The proposed medical simulator is composed of three main modules. A fast and accurate character animation technique is in charge of simulating the patient positioning phase and adapts their internal anatomy accordingly. gVirtualXRay is an open-source X-ray simulation library and generates the corresponding radiographs in real time. Finally, the courseware allows going through all the diagnostic radiology steps from the patient positioning and the machine configuration to the final image enhancing.RESULTS:A face and content validation study has been conducted; 18 radiology professionals were recruited to evaluate our software using a questionnaire. The results show that our tool is realistic in many ways (72% of the participants agreed that the simulations are visually realistic), useful (67%) and suitable (78%) for teaching X-ray radiography.CONCLUSIONS:The proposed tool allows simulating the most relevant steps of the projectional radiography procedure. The virtual patient posing system and X-ray simulation module execute at interactive rates. These features enable the lectures to show their students the results of good and bad practices in a classroom environment, avoiding radiation risk.

Radiographs of the hand are useful in diagnosing and staging diseases such as rheumatoid arthritis (RA) and other musculoskeletal diseases. Radiographs are projections of the 3D anatomy, with the useful information such as pose and pathology becoming lost in the process. We propose a 3D hand pose recovery method for radiographs of hands using a novel hybrid image registration method. Our pose recovery pipeline consists of aligning a simulated X-ray (digitally reconstructed radiograph) of an articulated phantom mesh model to a real hand radiograph using Covariance Matrix Adaptation Evolution Strategy. Early results demonstrate that our approach works well. Further inquiry is required to evaluate the applicability of our registration approach to other articulated musculoskeletal anatomy.

Distance learning engineering students (as well as those in face-to-face settings) should acquire a basic background in radiation-matter interaction physics (usually in the first semesters). Some students in this group may feel some degree of aversion towards these types of pure science-related subjects (mathematics, physics, chemistry, etc). In online learning scenarios, the average student is already an adult (37 years old or above) and may see no particular application of the aforementioned courses in their current or future professional life. Besides this, online institutions tend to lean too much on applet-based simulations. Although they may shed some light on the theory associated with the studied physical processes, these animated and interactive examples also seem to be ` stripped down' versions of the real events, and are felt to be disconnected from current scientific environments and engineering settings. For this reason, we describe a novel virtual lab approach to teach the basics of the low-energy interactions present in average x-ray settings. It combines real scientific simulation frameworks with modern computing techniques such as virtualization, cloud infrastructures, containers, networking and shared collaboration environments. It also fosters the use of hugely demanded development tools and programming languages and addresses the fundamentals of digital radiography and the linked electronic standards for image storage and transmission. With this mixed approach, blending scientific concepts, healthcare and state-of-the-art software solutions, our virtual labs have proven (over a period of five academic terms) to be both very attractive to and pedagogically successful (technically, and scientifically) for online engineering undergraduates. For the sake of completeness, we also propose a hands-on activity that mimics the geometrical peculiarities of x-ray rooms with the help of visible light and cheap materials.