Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model

Li, Min; Konstantinova, Jelizaveta; Xu, Guanghua; Aminzadeh, Vahid; Xie, Jun; Würdemann, Helge A.; Althoefer, Kaspar

doi:10.1371/journal.pone.0172703

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…These latter samples reached a strain of 25%, which is outside the linear viscoelastic region. Nevertheless, comparable strains can be reached in tissue phantoms developed for palpation models, and in our case these tests allowed us to empirically model the relaxation of samples closer to their fracture point in order to gain some insight into the mechanisms leading to enhanced toughness at high strains.…”

Section: Methodsmentioning

confidence: 83%

Sodium Alginate Toughening of Gelatin Hydrogels

Samp

Iovanac

Nolte

2017

ACS Biomater. Sci. Eng.

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Gelatin is a popular material for the creation of tissue phantoms due to its ease-of-use, safety, low relative cost, and its amenability to tuning physical properties through the use of additives. One difficulty that arises when using gelatin, especially in low concentrations, is the brittleness of the material. In this paper, we show that small additions of another common biological polymer, sodium alginate, significantly increase the toughness of gelatin without changing the Young’s modulus or other low-strain stress relaxation properties of the material. Samples were characterized using ramp-hold stress relaxation tests. The experimental data from these tests were then fit to the Generalized Maxwell (GM) model, as well as two models based on a fractional calculus approach: the Kelvin–Voigt Fractional Derivative (KVFD) and Fractional Maxwell (FM) models. We found that for our samples, the fractional models provided better fits with fewer parameters, and at strains within the linear elastic region, the linear viscoelastic parameters of the alginate/gelatin and pure gelatin samples were essentially indistinguishable. When the same ramp-hold stress relaxation experiments were run at high strains outside of the linear elastic region, we observed a shift in stress relaxation to shorter time scales with increasing sodium alginate addition, which may be associated with an increase in fluidity within the gelatin matrix. This leads us to believe that sodium alginate acts to enhance the viscosity within the fluidic region of the gelatin matrix, providing additional energy dissipation without raising the modulus of the material. These results are applicable to anyone desiring independent control of the Young’s modulus and toughness in preparing tissue phantoms, and suggest that sodium alginate should be added to low-modulus gelatin for use in biological and medical testing applications.

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Section: Methodsmentioning

confidence: 83%

Sodium Alginate Toughening of Gelatin Hydrogels

Samp

Iovanac

Nolte

2017

ACS Biomater. Sci. Eng.

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“…3). The silicone phantom simulating abdominal tissue was made using a standard Ecoflex 00-10 mixture of 1:1 (Part A : B) [16]. A TIMESETL piezo-electric contact microphone was attached to the surface of the silicone phantom.…”

Section: A Experiments 1: Hard Nodule Depth Detectionmentioning

confidence: 99%

Soft Tissue Characterisation Using a Novel Robotic Medical Percussion Device With Acoustic Analysis and Neural Networks

Qiu

Tan

Thompson

et al. 2022

IEEE Robot. Autom. Lett.

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Medical percussion is a common manual examination procedure used by physicians to determine the state of underlying tissues from their acoustic responses. Although it has been used for centuries, there is a limited quantitative understanding of its dynamics, leading to subjectivity and a lack of detailed standardisation. This paper presents a novel compliant two-degree-of-freedom robotic device inspired by the human percussion action, and validates its performance in two tissue characterisation experiments. In Experiment 1, spectrotemporal analysis using 1-D Continuous Wavelet Transform (CWT) proved the potential of the device to identify hard nodules, mimicking lipomas, embedded in silicone phantoms representing a patient's abdominal region. In Experiment 2, Gaussian Mixture Modelling (GMM) and Neural Network (NN) predictive models were implemented to classify composite phantom tissues of varying density and thickness. The proposed device and methods showed up to 97.5% accuracy in the classification of phantoms, proving the potential of robotic solutions to standardise and improve the accuracy of percussion diagnostic procedures.

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“…Finite element method (FEM) simulation is recognized as a proper approach to extract objective and quantitative information of inhomogeneous abnormal tissue, but this method is highly time consuming and normally has been used to simulate the mechanical property of soft tissue deformation from a scientific research point of view [25,26]. The Young's or elastic modulus is recently widely used to evaluate the perceived stiffness of the soft tissue, which takes into accoun the tool-tissue interaction and the indentation tip size as used in [22,27]. To calculate the perceived stiffness of soft tissue, the interaction force must be measured.…”

Section: Introductionmentioning

confidence: 99%

Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation

2019

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Force sensing in robotic-assisted minimally invasive surgery (RMIS) is crucial for performing dedicated surgical procedures, such as bilateral teleoperation and palpation. Due to the bio-compatibility and sterilization requirements, a specially designed surgical tool/shaft is normally attached to the sensor while contacting the organ targets. Through this design, the measured force from the sensor usually contains uncertainties, such as noise, inertial force etc., and thus cannot reflect the actual interaction force with the tissue environment. Motivated to provide the authentic contact force between a robotic tool and soft tissue, we proposed a data-driven force compensation scheme without intricate modeling to reduce the effects of force measurement uncertainties. In this paper, a neural-network-based approach is utilized to automatically model the inertial force subject to noise during the robotic palpation procedure, then the exact contact force can be obtained through the force compensation method which cancels the noise and inertial force. Following this approach, the genuine interaction force during the palpation task can be achieved furthermore to improve the appraisal of the tumor surrounded by the soft tissue. Experiments are conducted with robotic-assisted palpation tasks on a silicone-based soft tissue phantom and the results verify the effectiveness of the suggested method.

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Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model

Cited by 14 publications

References 25 publications

Sodium Alginate Toughening of Gelatin Hydrogels

Sodium Alginate Toughening of Gelatin Hydrogels

Soft Tissue Characterisation Using a Novel Robotic Medical Percussion Device With Acoustic Analysis and Neural Networks

Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation

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