Soft tissue deformation estimation by spatio-temporal Kalman filter finite element method

Yarahmadian, M; Zhong, Yongmin; Gu, Chengfan; Shin, Jaehyun

doi:10.3233/thc-174640

Cited by 3 publications

(3 citation statements)

References 14 publications

(9 reference statements)

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“…This paper significantly improves and extends the authors' previous work reported in [29] with substantial further studies and development. It uses the Wilson-θ integration, which is unconditionally stable, to address the issue of conditional stability involved in the Newmark integration for time domain discretization.…”

Section: Introductionsupporting

confidence: 86%

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Kalman Filter Finite Element Method for Real-Time Soft Tissue Modeling

et al. 2020

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Soft tissue modelling plays a significant role in surgery simulation as well as surgical procedure planning and training. However, it is a challenging research task to satisfy both physical realism and realtime simulation for soft tissue deformation. The finite element method (FEM) is a representative strategy for modelling of soft tissue deformation with highly physical realism. However, it suffers from expensive computations, unable to meet the requirement of real-time simulation. This paper proposes a novel method by combining FEM with the Kalman filter for real-time and accurate modelling of soft tissue deformation. The novelty of this method is that soft tissue deformation is formulated as a filtering identification process to online estimate soft tissue deformation from local measurement of displacement. To construct the discrete system state equation for filtering estimation, soft tissue deformation is discretised based on elastic theory in the space domain by FEM and is further discretised in the time domain by using the Wilson-θ implicit integration to solve the dynamic equilibrium equation of FEM deformation modelling. Subsequently, a Kalman filter is developed for online estimation and analysis of soft tissue deformation according to local measurement of displacement. Interactive tool-tissue interaction with haptic feedback is also achieved for surgery simulation. The presented method significantly improves the computational performance of the traditional FEM, but still maintains a similar level of accuracy. It not only achieves the real-time performance, but also exhibits the similar deformation behaviours as the traditional FEM and enables the use of large time steps to improve the simulation efficiency.

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Section: Introductionsupporting

confidence: 86%

“…So far, there has been very limited research focusing on using Kalman filter for soft tissue deformation, and the combination of KF and FEM for modelling of soft tissue deformation is still a challenge task. Just recently, Yarahmadian et al studied a Kalman filter based on FEM for modelling of soft tissue deformation [29].…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter Finite Element Method for Real-Time Soft Tissue Modeling

et al. 2020

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“…Thus, there has been limited research on using the nonlinear H-C model for soft tissue characterization. Although finite element model (FEM) [ 10 ] provides an accurate description of the force interaction between surgical tool and soft tissues using continuum mechanics, it is difficult to achieve the real-time performance due to expensive computations [ 3 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization

Shin

Zhong

Oetomo

et al. 2018

Sensors

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This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error.

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‘Grey-Box-Processing’: a novel validation method for use in vehicle safety applications

Soot

Dlugosch

Fritsch

et al. 2022

Engineering with Computers

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The ‘Grey-Box-Processing’ method, presented in this article, allows for the integration of simulated and experimental data sets with the overall objective of a comprehensive validation of simulation methods and models. This integration leads to so-called hybrid data sets. They allow for a spatially and temporally resolved identification and quantitative assessment of deviations between experimental observations and results of corresponding finite element simulations in the field of vehicle safety. This is achieved by the iterative generation of a synthetic, dynamic solution corridor in the finite element domain, which is deduced from experimental observations and restricts the freedom of movement of a virtually analyzed structure. The hybrid data sets thus contain physically based information about the interaction (e.g. acting forces) between the solution corridor and the virtually analyzed structure. An additional result of the ‘Grey-Box-Processing’ is the complemented three-dimensional reconstruction of the incomplete experimental observations (e.g. two-dimensional X-ray movies). The extensive data sets can be used not only for the assessment of the similarity between experiment and simulation, but also for the efficient derivation of improvement measures in order to increase the predictive power of the used model or method if necessary. In this study, the approach is presented in detail. Simulation-based investigations are conducted using generic test setups as well as realistic pedestrian safety test cases. These investigations show the general applicability of the method as well as the significant informative value and interpretability of generated hybrid data sets.

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Soft tissue deformation estimation by spatio-temporal Kalman filter finite element method

Cited by 3 publications

References 14 publications

Kalman Filter Finite Element Method for Real-Time Soft Tissue Modeling

Kalman Filter Finite Element Method for Real-Time Soft Tissue Modeling

Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization

‘Grey-Box-Processing’: a novel validation method for use in vehicle safety applications

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