Estimation of External Forces on the Basis of Local Displacement Observations of an Elastic Body

Sakata, Ryohei; Nakao, Megumi; Matsuda, Tetsuya

doi:10.14326/abe.6.21

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…However, application of the model-based approach has been restricted because the entire shape of organs cannot be obtained in many clinical situations such as ultrasound and intraoperative imaging. Recent study reports external force can be estimated using locally observed displacements of the deformed state [7]. To the best of our knowledge, without our preliminary study [5], no report has appeared investigating elasticity reconstruction that includes unobservable areas of elastic bodies.…”

Section: Introductionmentioning

confidence: 93%

Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields

Nakao¹,

Morita²,

Matsuda³

2019

Preprint

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This paper introduces an elasticity reconstruction method based on local displacement observations of elastic bodies. Sparse reconstruction theory is applied to formulate the underdetermined inverse problems of elasticity reconstruction including unobserved areas. An online local clustering scheme called a superelement is proposed to reduce the number of dimensions of the optimization parameters. Alternating the optimization of element boundaries and elasticity parameters enables the elasticity distribution to be estimated with a higher spatial resolution. The simulation experiments show that elasticity distribution is reconstructed based on observations of approximately 10% of the total body. The estimation error was improved when considering the sparseness of the elasticity distribution.

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Section: Introductionmentioning

confidence: 93%

Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields

Nakao¹,

Morita²,

Matsuda³

2019

Preprint

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“…In order to estimate the shape of the elastic object, matching algorithms based on shape descriptors [24], a method using weighted patch iterative closest point of the mesh [25], and a method using laser range scanner data to obtain information about the surface of the moving tissue [26] have been proposed. In another study, the external force applied to an object was estimated using partial observation, to overcome limited visibility at the time of surgical treatment [27]. In their work, it is necessary to assume that the properties related to the shape of the object are invariant before and after the deformation.…”

Section: Related Workmentioning

confidence: 99%

Deformation estimation of an elastic object by partial observation using a neural network

Yamamoto,

Nakao,

Ohzeki

et al. 2017

Preprint

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Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery. The aim of this study is to estimate the deformation of an entire three-dimensional elastic object using displacement information of very few observation points. A learning approach with a neural network was introduced to estimate the entire deformation of an object. We applied our method to two elastic objects; a rectangular parallelepiped model, and a human liver model reconstructed from computed tomography data. The average estimation error for the human liver model was 0.041 mm when the object was deformed up to 66.4 mm, from only around 3 % observations. These results indicate that the deformation of an entire elastic object can be estimated with an acceptable level of error from limited observations by applying a trained neural network to a new deformation.

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“…The models used in medical virtual reality organ simulators [8,9] require information about the elasticity of biological tissues. If such data could be obtained during surgery without additional hardware or time/cost burdens, various areas of biomed-ical research and intraoperative support systems [10,11] would bene t.…”

Section: Introductionmentioning

confidence: 99%

Model-based Estimation of Elastic Moduli by Local Displacement Observation of an Elastic Body

Morita

Nakao

Matsuda

2018

ABE

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As the pathological characteristics of living tissue generally correlate with tissue stiffness, techniques such as ultrasound elastography play an important role in the medical eld. One issue with elastography is the limited area of measurement. To determine the spatial distribution of elastic moduli, model-based estimation methods have been developed. However, these methods can only be applied to the observable area, and expanding the estimation area remains problematic. This paper introduces a method to estimate the spatial distribution of elastic moduli over the entire elastic body from locally observed deformation patterns. The reconstruction of elasticity from multiple deformation patterns is formulated as a minimization problem based on the sparseness of the gradient of tissue elasticity. Based on simulation experiments investigating the performance of the proposed method, we con rm that spatial variations in elasticity are effectively estimated and the area of elasticity reconstruction is extended. (40) ry, Graduate School of Informatics, Kyoto University. His research interests include biomedical engineering, computer assisted surgery, virtual reality, and medical image computing. Tetsuya MATSUDA Tetsuya MATSUDA (M 04) received the M.D. degree

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Estimation of External Forces on the Basis of Local Displacement Observations of an Elastic Body

Cited by 5 publications

References 16 publications

Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields

Sparse Elasticity Reconstruction and Clustering using Local Displacement Fields

Deformation estimation of an elastic object by partial observation using a neural network

Model-based Estimation of Elastic Moduli by Local Displacement Observation of an Elastic Body

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