Search

• Dynamic modelling of soft tissue deformation based on the dynamic model decomposition. • No prior knowledge of the system governing equation is required. • Construct a reduced-order model by extracting the essential features of the full model. This paper studies a new approach to dynamic modelling of soft tissue nonlinear deformation behavior based on dynamic model decomposition. This approach derives the deformation relationship between two continuous time points from correlated system snapshots. Subsequently, it constructs a reduced-order system by extracting most relevant information via thin-singular value decomposition to capture dynamic characteristics of the full-order deformation system. Unlike the existing methods which mainly rely on the establishment of complex constitutive equations for governing soft tissue deformation, the proposed method conducts tissue deformation directly from measured samples without involving constitutive governing equations. Simulation results show that the proposed method not only achieves real-time performance, but also sustains similar accuracy as the nonlinear finite element method. [ABSTRACT FROM AUTHOR]

Recent studies have suggested that irregular pulsation of intracranial aneurysm during the cardiac cycle may be potentially associated with aneurysm rupture risk. However, there is a lack of quantification method for irregular pulsations. This study aims to quantify irregular pulsations by the displacement and strain distribution of the intracranial aneurysm surface during the cardiac cycle using four-dimensional CT angiographic image data. Four-dimensional CT angiography was performed in 8 patients. The image data of a cardiac cycle was divided into approximately 20 phases, and irregular pulsations were detected in four intracranial aneurysms by visual observation, and then the displacement and strain of the intracranial aneurysm was quantified using coherent point drift and finite element method. The displacement and strain were compared between aneurysms with irregular and normal pulsations in two different ways (total and stepwise). The stepwise first principal strain was significantly higher in aneurysms with irregular than normal pulsations (0.20 ± 0.01 vs 0.16 ± 0.02 , p = 0.033). It was found that the irregular pulsations in intracranial aneurysms usually occur during the consecutive ascending or descending phase of volume changes during the cardiac cycle. In addition, no statistically significant difference was found in the aneurysm volume changes over the cardiac cycle between the two groups. Our method can successfully quantify the displacement and strain changes in the intracranial aneurysm during the cardiac cycle, which may be proven to be a useful tool to quantify intracranial aneurysm deformability and aid in aneurysm rupture risk assessment. [ABSTRACT FROM AUTHOR]

• A new constrained FEM (finite element method) for accurate and runtime modeling of soft tissue. • Modeling the soft tissue deformation with unknown loading input. • The proposed method significantly promotes the FEM computational performance. This paper presents a new constrained FEM (finite element method) for accurate and runtime modeling of soft tissue's mechanical deformation from measurement of local surface displacement. This method turns the modeling problem of tissue deformation with unknown loading input into a constrained estimation problem to online estimate physical tissue deformation. Based on linear FEM discretization, a constrained estimation algorithm is developed to optimally estimate unknown loading input and online calculate deformation from measurement of local surface displacement. Results of simulation and experimentation exhibit that the proposed method significantly promotes the FEM computational performance but still inherits the FEM advantage of physical accuracy. [ABSTRACT FROM AUTHOR]

Realistic modelling of human soft tissue is very important in medical applications. This paper proposes a novel method by dynamically incorporating soft tissue characterisation in the process of soft tissue modelling to increase the modelling fidelity. This method defines nonlinear tissue deformation with unknown mechanical properties as a problem of nonlinear filtering identification to dynamically identify mechanical properties and further estimate nonlinear deformation behaviour of soft tissue. It combines maximum likelihood theory, nonlinear filtering and nonlinear finite element method (NFEM) for modelling of nonlinear tissue deformation behaviour based on dynamic identification of homogeneous tissue properties. On the basis of hyperelasticity, a nonlinear state-space equation is established by discretizing tissue deformation through NFEM for dynamic filtering. A maximum likelihood algorithm is also established to dynamically identify tissue mechanical properties during the deformation process. Upon above, a maximum likelihood-based extended Kalman filter is further developed for dynamically estimating tissue nonlinear deformation based on dynamic identification of tissue mechanical properties. Simulation and experimental analyses reveal that the proposed method not only overcomes the NFEM limitation of expensive computations, but also absorbs the NFEM merit of high accuracy for modelling of homogeneous tissue deformation. Further, the proposed method also effectively identifies tissue mechanical properties during the deformation modelling process. [ABSTRACT FROM AUTHOR]

• This paper formulates mechanical deformation of biological tissues as nonlinear filtering for dynamic estimation of mechanical deformation behaviours of biological tissues; • Tissue mechanical deformation is discretised in the spatial and temporal domains by the nonlinear finite element method (NFEM) and central difference integration scheme, respectively, to construct the nonlinear state-space equations for dynamic filtering; • An extended Kalman filter (EKF) is developed to dynamically estimate mechanical deformations of biological tissues • The proposed EKF-NFEM conquers the NFEM limitation of expensive computations and still preserves a reliable level of accuracy as the NFEM. Background and Objective : Soft tissue modelling is crucial to surgery simulation. This paper introduces an innovative approach to realistic simulation of nonlinear deformation behaviours of biological soft tissues in real time. Methods : This approach combines the traditional nonlinear finite-element method (NFEM) and nonlinear Kalman filtering to address both physical fidelity and real-time performance for soft tissue modelling. It defines tissue mechanical deformation as a nonlinear filtering process for dynamic estimation of nonlinear deformation behaviours of biological tissues. Tissue mechanical deformation is discretized in space using NFEM in accordance with nonlinear elastic theory and in time using the central difference scheme to establish the nonlinear state-space models for dynamic filtering. Results : An extended Kalman filter is established to dynamically estimate nonlinear mechanical deformation of biological tissues. Interactive deformation of biological soft tissues with haptic feedback is accomplished as well for surgery simulation. Conclusions : The proposed approach conquers the NFEM limitation of step computation but without trading off the modelling accuracy. It not only has a similar level of accuracy as NFEM, but also meets the real-time requirement for soft tissue modelling. [ABSTRACT FROM AUTHOR]