Three-dimensional echocardiographic virtual endoscopy for the diagnosis of congenital heart disease in children

Virtual endoscopy (VE) is a new post-processing method that uses volumetric data sets to simulate the tracks of a “conventional” flexible endoscope. However, almost all studies of this method have involved virtual visualizations of the cardiovascular structures applied to computed tomography (CT) and magnetic resonance (MR) datasets. This paper introduces a novel visualization method called the “three-dimensional echocardiographic intracardiac endoscopic simulation system (3DE IESS)”, which uses 3D echocardiographic images in a virtual reality (VR) environment to diagnose congenital heart disease. The aim of this study was to analyze the feasibility of VE in the evaluation of congenital heart disease in children and its accuracy compared with 2DE. Three experienced pediatric cardiologists blinded to the patients’ diagnoses separately reviewed 40 two-dimensional echocardiographic (2DE) datasets and 40 corresponding VE datasets and judged whether abnormal intracardiac anatomy was present in terms of a five-point scale (1 = definitely absent; 2 = probably absent; 3 = cannot be determined; 4 = probably present; and 5 = definitely present). Compared with clinical diagnosis, the diagnostic accuracy of VE was 98.7% for ASD, 92.4% for VSD, 92.6% for TOF, and 94% for DORV, respectively. Diagnostic accuracy of VE was significantly higher than that of 2DE for TOF and DORV except for ASD and VSD. The receiver operating characteristic (ROC) curve for VE was closer to the optimal performance point than was the ROC curve for 2DE. The area under the ROC curve was 0.96 for VE and 0.93 for 2DE. Kappa values (range, 0.73–0.79) for VE and 2DE indicated substantial agreement. 3D echocardiographic VE can enhance our understanding of intracardiac structures and facilitate the evaluation of congenital heart disease

Position-based dynamics simulator of vessel deformations for path planning in robotic endovascular catheterization

A major challenge during autonomous navigation in endovascular interventions is the complexity of operating in a deformable but constrained workspace with an instrument. Simulation of deformations for it can provide a cost-effective training platform for path planning. Aim of this study is to develop a realistic, auto-adaptive, and visually plausible simulator to predict vessels’ global deformation induced by the robotic catheter’s contact and cyclic heartbeat motion. Based on a Position-based Dynamics (PBD) approach for vessel modeling, Particle Swarm Optimization (PSO) algorithm is employed for an auto-adaptive calibration of PBD deformation parameters and of the vessels movement due to a heartbeat. In-vitro experiments were conducted and compared with in-silico results. The end-user evaluation results were reported through quantitative performance metrics and a 5-Point Likert Scale questionnaire. Compared with literature, this simulator has an error of 0.23±0.13% for deformation and 0.30±0.85mm for the aortic root displacement. In-vitro experiments show an error of 1.35±1.38mm for deformation prediction. The end-user evaluation results show that novices are more accustomed to using joystick controllers, and cardiologists are more satisfied with the visual authenticity. The real-time and accurate performance of the simulator make this framework suitable for creating a dynamic environment for autonomous navigation of robotic catheters

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 246)

This bibliography lists 219 reports, articles and other documents introduced into the NASA scientific and technical information system in May 1983

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 172

This bibliography lists 132 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1977

A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery

Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training and assessment of new surgeons, that deviates from the “see one, do one, teach one” model introduced by William Halsted, so that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar, IEEXplore and known journals using the keywords, “endovascular surgery”, “surgical skills”, “endovascular skills”, “surgical training endovascular” and “catheter skills”. Focusing explicitly on endovascular surgical skills, we group related works into three categories based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated surgical assessment solutions