Quantitative characterization of viscoelastic behavior in tissue-mimicking phantoms and ex vivo animal tissues.

Viscoelasticity of soft tissue is often related to pathology, and therefore, has become an important diagnostic indicator in the clinical assessment of suspect tissue. Surgeons, particularly within head and neck subsites, typically use palpation techniques for intra-operative tumor detection. This detection method, however, is highly subjective and often fails to detect small or deep abnormalities. Vibroacoustography (VA) and similar methods have previously been used to distinguish tissue with high-contrast, but a firm understanding of the main contrast mechanism has yet to be verified. The contributions of tissue mechanical properties in VA images have been difficult to verify given the limited literature on viscoelastic properties of various normal and diseased tissue. This paper aims to investigate viscoelasticity theory and present a detailed description of viscoelastic experimental results obtained in tissue-mimicking phantoms (TMPs) and ex vivo tissues to verify the main contrast mechanism in VA and similar imaging modalities. A spherical-tip micro-indentation technique was employed with the Hertzian model to acquire absolute, quantitative, point measurements of the elastic modulus (E), long term shear modulus (η), and time constant (τ) in homogeneous TMPs and ex vivo tissue in rat liver and porcine liver and gallbladder. Viscoelastic differences observed between porcine liver and gallbladder tissue suggest that imaging modalities which utilize the mechanical properties of tissue as a primary contrast mechanism can potentially be used to quantitatively differentiate between proximate organs in a clinical setting. These results may facilitate more accurate tissue modeling and add information not currently available to the field of systems characterization and biomedical research

Simulation of hyperelastic materials in real-time using Deep Learning

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm. We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. Our method is applied to two benchmark examples: a cantilever beam and an L-shape subject to moving punctual loads. A comparison between our method and proper orthogonal decomposition (POD) is done through the paper. The results show that U-Mesh can perform very fast simulations on various geometries, mesh resolutions and number of input forces with very small errors

Identification of the Elastic Modulus of an Organ Model Using Reactive Force and Ultrasound Image

制度:新 ; 報告番号:甲3418号 ; 学位の種類:博士(工学) ; 授与年月日:2011/7/28 ; 早大学位記番号:新574

A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training

Robotic patients show great potential to improve medical palpation training as they can provide feedback that cannot be obtained in a real patient. Providing information about internal organs deformation can significantly enhance palpation training by giving medical trainees visual insight based on their finger behaviours. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, thus able to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real-time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANN) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 hrs to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that the ANN has a 92.6% accuracy. We also show that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has potential to be used as a training simulator for trainees to hone their palpation skills

Robotic simulators for tissue examination training with multimodal sensory feedback

Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators

In-vitro Strain and Modulus Measurements in Porcine Cervical Lymph Nodes

Cervical lymph nodes are common sites of metastatic involvement in head and neck cancers. These lymph nodes are superficially located and palpation is a common practice for assessing nodal hardness and staging cancer which is, however, too subjective and with limited accuracy. In this study, the mechanical properties of pig lymph node tissues were investigated using ultrasound elastography and indentation test. Lymph nodes were excised from fresh pork pieces and embedded in an agar-gelatin phantom for strain imaging by elastography. A strain ratio reflecting the strain contrast of lymph node over agar-gelatin phantom was used to assess the elasticity of the lymph node. A cutting device was then custom-designed to slice the phantom into uniform slices for indentation test. The measurements revealed that there were significant differences in both the strain ratio and Young’s modulus between the peripheral and middle regions of the lymph nodes (both p < 0.05); however, the results appeared contradictory. Correlation between the results of the two measurements (modulus ratio vs. inversed strain ratio) showed their association was moderate for both the peripheral and middle regions (R2 = 0.437 and 0.424 respectively). As the tests were only performed on normal lymph nodes, comparison in stiffness between healthy and abnormal lymph nodes could not be made. Future studies should be conducted to quantify the stiffness change in abnormal lymph nodes

Elastography: modality-specific approaches, clinical applications, and research horizons

Manual palpation has been used for centuries to provide a relative indication of tissue health and disease. Engineers have sought to make these assessments increasingly quantitative and accessible within daily clinical practice. Since many of the developed techniques involve image-based quantification of tissue deformation in response to an applied force (i.e., "elastography”), such approaches fall squarely within the domain of the radiologist. While commercial elastography analysis software is becoming increasingly available for clinical use, the internal workings of these packages often remain a "black box,” with limited guidance on how to usefully apply the methods toward a meaningful diagnosis. The purpose of the present review article is to introduce some important approaches to elastography that have been developed for the most widely used clinical imaging modalities (e.g., ultrasound, MRI), to provide a basic sense of the underlying physical principles, and to discuss both current and potential (musculoskeletal) applications. The article also seeks to provide a perspective on emerging approaches that are rapidly developing in the research laboratory (e.g., optical coherence tomography, fibered confocal microscopy), and which may eventually gain a clinical foothol

Soft volume simulation using a deformable surface model

The aim of the research is to contribute to the modelling of deformable objects, such as soft tissues in medical simulation. Interactive simulation for medical training is a concept undergoing rapid growth as the underlying technologies support the increasingly more realstic and functional training environments. The prominent issues in the deployment of such environments centre on a fine balance between the accuracy of the deformable model and real-time interactivity. Acknowledging the importance of interacting with non-rigid materials such as the palpation of a breast for breast assessment, this thesis has explored the physics-based modelling techniques for both volume and surface approach. This thesis identified that the surface approach based on the mass spring system (MSS) has the benefits of rapid prototyping, reduced mesh complexity, computational efficiency and the support for large material deformation compared to the continuum approach. However, accuracy relative to real material properties is often over looked in the configuration of the resulting model. This thesis has investigated the potential and the feasibility of surface modelling for simulating soft objects regardless of the design of the mesh topology and the non-existence of internal volume discretisation. The assumptions of the material parameters such as elasticity, homogeneity and incompressibility allow a reduced set of material values to be implemented in order to establish the association with the surface configuration. A framework for a deformable surface model was generated in accordance with the issues of the estimation of properties and volume behaviour corresponding to the material parameters. The novel extension to the surface MSS enables the tensile properties of the material to be integrated into an enhanced configuration despite its lack of volume information. The benefits of the reduced complexity of a surface model are now correlated with the improved accuracy in the estimation of properties and volume behaviour. Despite the irregularity of the underlying mesh topology and the absence of volume, the model reflected the original material values and preserved volume with minimal deviations. Global deformation effect which is essential to emulate the run time behaviour of a real soft material upon interaction, such as the palpation of a generic breast, was also demonstrated, thus indicating the potential of this novel technique in the application of soft tissue simulation

VISIO-HAPTIC DEFORMABLE MODEL FOR HAPTIC DOMINANT PALPATION SIMULATOR

Vision and haptic are two most important modalities in a medical simulation. While visual cues assist one to see his actions when performing a medical procedure, haptic cues enable feeling the object being manipulated during the interaction. Despite their importance in a computer simulation, the combination of both modalities has not been adequately assessed, especially that in a haptic dominant environment. Thus, resulting in poor emphasis in resource allocation management in terms of effort spent in rendering the two modalities for simulators with realistic real-time interactions. Addressing this problem requires an investigation on whether a single modality (haptic) or a combination of both visual and haptic could be better for learning skills in a haptic dominant environment such as in a palpation simulator. However, before such an investigation could take place one main technical implementation issue in visio-haptic rendering needs to be addresse

Patient specific modeling of palpation-based prostate cancer diagnosis: effects of pelvic cavity anatomy and intrabladder pressure.

Computational modeling has become a successful tool for scientific advances including understanding the behavior of biological and biomedical systems as well as improving clinical practice. In most cases, only general models are used without taking into account patient-specific features. However, patient specificity has proven to be crucial in guiding clinical practice because of disastrous consequences that can arise should the model be inaccurate. This paper proposes a framework for the computational modeling applied to the example of the male pelvic cavity for the purpose of prostate cancer diagnostics using palpation. The effects of patient specific structural features on palpation response are studied in three selected patients with very different pathophysiological conditions whose pelvic cavities are reconstructed from MRI scans. In particular, the role of intrabladder pressure in the outcome of digital rectal examination is investigated with the objective of providing guidelines to practitioners to enhance the effectiveness of diagnosis. Furthermore, the presence of the pelvic bone in the model is assessed to determine the pathophysiological conditions in which it has to be modeled. The conclusions and suggestions of this work have potential use not only in clinical practice and also for biomechanical modeling where structural patient-specificity needs to be considered. © 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd