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Castel Guelfo di Bologna, Italy

Liu B.,University of Bedfordshire | Telea A.C.,University of Groningen | Roerdink J.B.T.M.,University of Groningen | Clapworthy G.J.,University of Bedfordshire | And 6 more authors.
Computers and Graphics (Pergamon) | Year: 2014

Centerline extraction is important in a variety of visualization applications including shape analysis, geometry processing, and virtual endoscopy. Centerlines allow accurate measurements of length along winding tubular structures, assist automatic virtual navigation, and provide a path-planning system to control the movement and orientation of a virtual camera. However, efficiently computing centerlines with the desired accuracy has been a major challenge. Existing centerline methods are either not fast enough or not accurate enough for interactive application to complex 3D shapes. Some methods based on distance mapping are accurate, but these are sequential algorithms which have limited performance when running on the CPU. To our knowledge, there is no accurate parallel centerline algorithm that can take advantage of modern many-core parallel computing resources, such as GPUs, to perform automatic centerline extraction from large data volumes at interactive speed and with high accuracy. In this paper, we present a new parallel centerline extraction algorithm suitable for implementation on a GPU to produce highly accurate, 26-connected, one-voxel-thick centerlines at interactive speed. The resulting centerlines are as accurate as those produced by a state-of-the-art sequential CPU method [40], while being computed hundreds of times faster. Applications to fly through path planning and virtual endoscopy are discussed. Experimental results demonstrating centeredness, robustness and efficiency are presented. © 2014 Elsevier Ltd. Source


Chen H.,University of Oxford | Selimovic A.,University of Oxford | Thompson H.,University of Oxford | Chiarini A.,SCS srl | And 3 more authors.
Annals of Biomedical Engineering | Year: 2013

We propose a novel method to reconstruct the hypothetical geometry of the healthy vasculature prior to intracranial aneurysm (IA) formation: a Frenet frame is calculated along the skeletonization of the arterial geometry; upstream and downstream boundaries of the aneurysmal segment are expressed in terms of the local Frenet frame basis vectors; the hypothetical healthy geometry is then reconstructed by propagating a closed curve along the skeleton using the local Frenet frames so that the upstream boundary is smoothly morphed into the downstream boundary. This methodology takes into account the tortuosity of the arterial vasculature and requires minimal user subjectivity. The method is applied to 22 clinical cases depicting IAs. Computational fluid dynamic simulations of the vasculature without IA are performed and the haemodynamic stimuli in the location of IA formation are examined. We observe that locally elevated wall shear stress (WSS) and gradient oscillatory number (GON) are highly correlated (20/22 for WSS and 19/22 for GON) with regions susceptible to sidewall IA formation whilst haemodynamic indices associated with the oscillation of the WSS vectors have much lower correlations. © 2013 Biomedical Engineering Society. Source


Zhao X.,University of Bedfordshire | Liu E.,University of Bedfordshire | Clapworthy G.J.,University of Bedfordshire | Viceconti M.,Laboratorio Of Tecnologia Medica | Testi D.,SCS srl
Computer Methods and Programs in Biomedicine | Year: 2012

Carefully collected, high-quality data are crucial in biomedical visualization, and it is important that the user community has ready access to both this data and the high-performance computing resources needed by the complex, computational algorithms that will process it. Biological researchers generally require data, tools and algorithms from multiple providers to achieve their goals. This paper illustrates our response to the problems that result from this. The Living Human Digital Library (LHDL) project presented in this paper has taken advantage of Web Services to build a biomedical digital library infrastructure that allows clinicians and researchers not only to preserve, trace and share data resources, but also to collaborate at the data-processing level. © 2010 Elsevier Ireland Ltd. Source


Valente G.,Rizzoli Orthopaedic Institute | Pitto L.,Rizzoli Orthopaedic Institute | Testi D.,SCS srl | Seth A.,Stanford University | And 4 more authors.
PLoS ONE | Year: 2014

Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur. © 2014 Valente et al. Source


Petrolo L.,Rizzoli Orthopaedic Institute | Petrolo L.,SCS srl | Testi D.,SCS srl | Taddei F.,Rizzoli Orthopaedic Institute | Viceconti M.,Rizzoli Orthopaedic Institute
Computer Methods and Programs in Biomedicine | Year: 2010

The aim of this study is to evaluate the performance of a non-conventional input and output device (virtual reality) in a total hip replacement surgical planner. A test was performed asking five users to position a cup in a defined position. Every user performed the task using three different hardware configurations: (I) conventional mouse and monitor, (II) mouse and auto-stereoscopic monitor, and (III) 12-DOF tracker (haptic device) and auto-stereoscopic monitor. The results were evaluated in terms of root mean square error of the obtained position with respect to the target one and in terms of learning curve. The results showed that the examined VR technology does not show a sufficient positioning accuracy to be considered for clinical assessment. © 2009 Elsevier Ireland Ltd. All rights reserved. Source

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