Bummo Ahn

Measurement and characterization of soft tissue behavior with surface deformation and force response under large deformations

Soft tissue characterization with the inverse finite element method (FEM) optimization algorithm plays an important role in developing a physical model for medical simulations. However, tissue characterization that takes into account comprehensive boundary conditions for large deformations remains a challenge due to computational complexities and a lack of experimental data. In this study, soft tissue experiments on porcine livers were performed to measure the surface deformation and force response of soft tissues resulting from indentation loading depending on various indentation depths and two different tip shapes. Measurements were carried out with a three-dimensional (3D) optical system and a force transducer. Using the surface deformation and force response results, we estimated the maximum radius of influence, which can be utilized to determine the minimal required soft tissue model size for the FEM simulation. Considering the influence of the boundary conditions, the model was designed and integrated into an inverse FEM optimization algorithm to estimate the model parameters. The mechanical behavior of large deformations was characterized with FE modeling via hyperelastic and linear viscoelastic models.

An efficient soft tissue characterization algorithm from in vivo indentation experiments for medical simulation.

Realistic virtual reality surgical training simulators require an accurate biomechanical model of in vivo soft tissue behaviour. One of the challenges in modelling is to characterize soft tissue properties incorporating the experimental measurements of organ behaviour.

Robotic palpation and mechanical property characterization for abnormal tissue localization

Palpation is an intuitive examination procedure in which the kinesthetic and tactile sensations of the physician are used. Although it has been widely used to detect and localize diseased tissues in many clinical fields, the procedure is subjective and dependent on the experience of the individual physician. Palpation results and biomechanics-based mechanical property characterization are possible solutions that can enable the acquisition of objective and quantitative information on abnormal tissue localization during diagnosis and surgery. This paper presents an integrated approach for robotic palpation combined with biomechanical soft tissue characterization. In particular, we propose a new palpation method that is inspired by the actual finger motions that occur during palpation procedures. To validate the proposed method, robotic palpation experiments on silicone soft tissue phantoms with embedded hard inclusions were performed and the force responses of the phantoms were measured using a robotic palpation system. Furthermore, we carried out a numerical analysis, simulating the experiments and estimating the objective and quantitative properties of the tissues. The results indicate that the proposed approach can differentiate diseased tissue from normal tissue and can characterize the mechanical information of diseased tissue, which means that this method can be applied as a means of abnormality localization to diagnose prostate cancers.

Robotic Palpation-Based Mechanical Property Mapping for Diagnosis of Prostate Cancer

PURPOSE The aim of this study was to estimate the mechanical properties (elasticity) of normal and cancer prostate tissues and to develop a tissue elasticity map for the diagnosis and localization of prostate cancer. MATERIALS AND METHODS A total of 735 sites from 35 radical prostatectomy specimens were used in the experiments using a robotic palpation system, and the elasticities of the specimens were estimated by a tissue characterization algorithm. The estimated elasticities from 21 regions were separated into normal and cancer tissues using the pathological information, and a tissue elasticity map was developed using numerical functions and a nonlinear surface-fitting method. RESULTS The mean elastic moduli of the normal and cancer tissues were 15.25 ± 5.88 and 28.80 ± 11.20 kPa, respectively. The base region had the highest elasticity, followed by the medial and apex regions. These results demonstrated the ability to separate the cancer tissue from the normal tissue based on its elastic modulus. The tissue elasticity mapping was carried out using the estimated elasticity and nonlinear surface fitting. The proposed map showed the elasticity and was used to estimate the elastic modulus of the prostate at any given region. CONCLUSION Tissue elasticity may be an important indicator of prostate cancer because the pathologic changes alter the tissue properties, including cell integrity and intercellular matrix. This work provides quantitative and objective information for the diagnosis of prostate cancer. In addition, these results may have implications for the localization of prostate cancers.

Graphic and haptic modelling of the oesophagus for VR-based medical simulation.

Medical simulators with vision and haptic feedback have been applied to many medical procedures in recent years, due to their safe and repetitive nature for training. Among the many technical components of the simulators, realistic and interactive organ modelling stands out as a key issue for judging the fidelity of the simulation. This paper describes the modelling of an oesophagus for a real‐time laparoscopic surgical simulator.

An Efficient Soft Tissue Characterization Method for Haptic Rendering of Soft Tissue Deformation in Medical Simulation

The modeling of soft tissue behavior is essential in haptic rendering for virtual reality based medical simulators, which provide a safe and objective medium for training medical personnel. This paper presents a measuring device, modeling and effective characterization method of soft tissues material properties. Experiments were conducted on five porcine livers using an indentation device and ramp-and-hold trajectory. The soft tissue was assumed to be a continuous, incompressible, homogeneous and isotropic solid and a nonlinear constitutive model based on the observation of the force-displacement behavior of soft tissues. The constitutive model was fitted to the experimental data using the Levenberg- Marquardt optimization algorithm combined with a three-dimensional nonlinear finite element simulation. Scripting software was used to automatically characterize the mechanical properties and to repeat the FE simulation until the simulated and experimental responses matched.

Robotic system for hybrid diagnosis of prostate cancer: Design and experimentation

Medical robotic system is a reliable method to provide a precise and safe treatment to the patients, especially in the area of urology. The prostate cancers are firmer than normal tissues. Therefore, physicians can detect the prostate cancer with a palpation. In this paper, we developed a robotic system to detect prostate cancers using robotic palpation to find the suspected area of the tissue as well as to localize the area of the needle biopsy. To validate the performance of the developed system, the experiment on soft tissue phantoms was carried out. The results indicate that the system can identify the differences of tissue phantoms within and without inclusions. In addition, the bigger inclusions and the closer inclusions from the surface show larger value of the force response. These results indicate that the developed robotic system can be used to find hard materials such as tumor and malignant tissue in the prostate.

Robotic system with sweeping palpation and needle biopsy for prostate cancer diagnosis

Early diagnosis of prostate cancers can be of major benefit to patients because, combined with proper treatments, it can increase the survival rate of patients.

New approach for abnormal tissue localization with robotic palpation and mechanical property characterization

Robotic palpation is of major interest as a medical technique that could replace subjective palpation and tactile sensation by yielding precisely controlled palpation to tissues and quantitative tactile feedback acquisition. Palpation results and biomechanics based mechanical property characterization are possible solutions that could enable the acquisition of objective and quantitative information on abnormal tissue localization during diagnosis and surgery. This paper presents an integrated approach for robotic palpation and mechanical property characterization. To validate the proposed methods, robotic palpation experiments on silicone soft-tissue phantoms with embedded hard inclusions were performed using a robotic palpation system, and the force responses of the phantoms were measured. Furthermore, we carried out a numerical analysis simulating the experiments and estimating the objective and quantitative properties of the tissues.

Palpation device for the identification of kidney and bladder cancer: a pilot study.

Purpose To determine the ability of a novel palpation device to differentiate between benign and malignant tissues of the kidney and bladder by measuring tissue elasticity. Materials and Methods A novel palpation device was developed, mainly composed of a micromotor, a linear position sensor, a force transducer, and a hemisphere tip and cylindrical body probe. Motion calibration as well as performance validation was done. The tissue elasticity of both benign and malignant tissues of the kidney and bladder was measured using this device. A single investigator performed the ex-vivo palpation experiment in twelve kidneys and four bladder specimens. Malignant tissues were made available from partial nephrectomy specimens and radical cystectomy specimens. Palpations for benign renal parenchyma tissue were carried out on nephroureterectomy specimens while non-involved areas in the radical cystectomy specimens were used for benign bladder samples. Elastic modulus (Youngs modulus) of tissues was estimated using the Hertz-Sneddon equation from the experimental results. These were then compared using a t-test for independent samples. Results Renal cell carcinoma tissues appear to be softer than normal kidney tissues, whereas tissues from urothelial carcinoma of the bladder appear to be harder than normal bladder tissues. The results from renal cell carcinoma differed significantly from those of normal kidney tissues (p=0.002), as did urothelial carcinoma of the bladder from normal bladder tissues (p=0.003). Conclusion Our novel palpation device can potentially differentiate between malignant and benign kidney and bladder tissues. Further studies are necessary to verify our results and define its true clinical utility.