Dataset Information

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

ABSTRACT: Haptic information in robotic surgery can significantly improve clinical outcomes and help detect hard soft-tissue inclusions that indicate potential abnormalities. Visual representation of tissue stiffness information is a cost-effective technique. Meanwhile, direct force feedback, although considerably more expensive than visual representation, is an intuitive method of conveying information regarding tissue stiffness to surgeons. In this study, real-time visual stiffness feedback by sliding indentation palpation is proposed, validated, and compared with force feedback involving human subjects. In an experimental tele-manipulation environment, a dynamically updated color map depicting the stiffness of probed soft tissue is presented via a graphical interface. The force feedback is provided, aided by a master haptic device. The haptic device uses data acquired from an F/T sensor attached to the end-effector of a tele-manipulated robot. Hard nodule detection performance is evaluated for 2 modes (force feedback and visual stiffness feedback) of stiffness feedback on an artificial organ containing buried stiff nodules. From this artificial organ, a virtual-environment tissue model is generated based on sliding indentation measurements. Employing this virtual-environment tissue model, we compare the performance of human participants in distinguishing differently sized hard nodules by force feedback and visual stiffness feedback. Results indicate that the proposed distributed visual representation of tissue stiffness can be used effectively for hard nodule identification. The representation can also be used as a sufficient substitute for force feedback in tissue palpation.

SUBMITTER: Li M

PROVIDER: S-EPMC5383005 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Li Min M Konstantinova Jelizaveta J Xu Guanghua G He Bo B Aminzadeh Vahid V Xie Jun J Wurdemann Helge H Althoefer Kaspar K

PloS one 20170301 3

Haptic information in robotic surgery can significantly improve clinical outcomes and help detect hard soft-tissue inclusions that indicate potential abnormalities. Visual representation of tissue stiffness information is a cost-effective technique. Meanwhile, direct force feedback, although considerably more expensive than visual representation, is an intuitive method of conveying information regarding tissue stiffness to surgeons. In this study, real-time visual stiffness feedback by sliding i ...[more]

PMID: 28248996

Similar Datasets

Project description:BackgroundA calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol.MethodsThe approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing.ResultsThe study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting.ConclusionsThis is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

Project description:ObjectiveThe incidence of sub-centimeter pulmonary nodules has been increasing along with the use of low-dose computed tomography (LDCT) as a screening tool for early lung cancer detection. In our institution, pulmonary nodule computed tomography-guided localization (PNCL) is performed preoperatively with the laser angle guided assembly (LAGA), an angle reference device. This study aims to investigate the efficacy of postgraduate education in a phantom simulation of PNCL, with or without LAGA.Setting designThis prospective study was conducted in an academic hospital in Taiwan. Seven thoracic surgery residents and three experienced senior physicians were recruited to perform PNCL using a phantom simulation, with or without LAGA, for five nodules each and complete a questionnaire. Performance data were collected. χ2 tests, Mann-Whitney U test, univariate and multivariate linear regression were used for statistical analyses.ResultsThe confidence level increased from median 7[range 1, 9] to 8, range [6,9] (p = 0.001) before and after the simulation education course. The scores of enhanced PNCL ability and course satisfaction were as high as 8 [5,9], and 9 [7,9]. LAGA enabled broader puncture angles (with 27.5° [0°,80°]; without 14° [0°, 80°], p = 0.003), a lower puncture frequency (with 1 [1,4]; without 2 [1,5], p < 0.001), and a smaller angle deviation (with 3°[ 0°,8°]; without 5°[ 0°,19°], p = 0.002). Pleural depth in millimeters was associated with increased puncture frequency (0.019[0,010,0.028]) and procedure time (0.071'[ 0.018,0.123']. The PNCL-experienced physicians performed the procedure in less time (-2.854'[-4.646',1.061']. The traverse direction toward the mediastinum diminished the frequency (toward 1[ 1,3]; away 1 [1,5], p = 0.003) and time (toward 7.5'[2',18]'; away 9'[ 3',31'], p = 0.027). The learning curve did not improve procedure performance after ten PNCL simulation rounds.ConclusionsThe phantom PNCL simulation education course increased the confidence level, enhanced residents' skill acquisition, and promoted learning satisfaction. The angle reference device helped improve the outcomes of the puncture frequency and reduced angle deviation.

Project description:Dynamic simulations are necessary for understanding the mechanism of how biochemical networks generate robust properties to environmental stresses or genetic changes. Sensitivity analysis allows the linking of robustness to network structure. However, it yields only local properties regarding a particular choice of plausible parameter values, because it is hard to know the exact parameter values in vivo. Global and firm results are needed that do not depend on particular parameter values. We propose mathematical analysis for robustness (MAR) that consists of the novel evolutionary search that explores all possible solution vectors of kinetic parameters satisfying the target dynamics and robustness analysis. New criteria, parameter spectrum width and the variability of solution vectors for parameters, are introduced to determine whether the search is exhaustive. In robustness analysis, in addition to single parameter sensitivity analysis, robustness to multiple parameter perturbation is defined. Combining the sensitivity analysis and the robustness analysis to multiple parameter perturbation enables identifying critical reactions. Use of MAR clearly identified the critical reactions responsible for determining the circadian cycle in the Drosophila interlocked circadian clock model. In highly robust models, while the parameter vectors are greatly varied, the critical reactions with a high sensitivity are uniquely determined. Interestingly, not only the per-tim loop but also the dclk-cyc loop strongly affect the period of PER, although the dclk-cyc loop hardly changes its amplitude and it is not potentially influential. In conclusion, MAR is a powerful method to explore wide parameter space without human-biases and to link a robust property to network architectures without knowing the exact parameter values. MAR identifies the reactions critically responsible for determining the period and amplitude in the interlocked feedback model and suggests that the circadian clock intensively evolves or designs the kinetic parameters so that it creates a highly robust cycle.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets