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 (Young's 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.ope

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

Haptic feedback from human tissues of various stiffness and homogeneity

This work presents methods for haptic modelling of soft and hard tissue with varying stiffness. The model provides visualization of deformation and calculates force feedback during simulated epidural needle insertion. A spring-mass-damper (SMD) network is configured from magnetic resonance image (MRI) slices of patient’s lumbar region to represent varying stiffness throughout tissue structure. Reaction force is calculated from the SMD network and a haptic device is configured to produce a needle insertion simulation. The user can feel the changing forces as the needle is inserted through tissue layers and ligaments. Methods for calculating the force feedback at various depths of needle insertion are presented. Voxelization is used to fill ligament surface meshes with spring mass damper assemblies for simulated needle insertion into soft and hard tissues. Modelled vertebrae cannot be pierced by the needle. Graphs were produced during simulated needle insertions to compare the applied force to haptic reaction force. Preliminary saline pressure measurements during Tuohy epidural needle insertion are also used as a basis for forces generated in the simulation

Using Visual Cues to Enhance Haptic Feedback for Palpation on Virtual Model of Soft Tissue

This paper explores methods that make use of visual cues aimed at generating actual haptic sensation to the user, namely pseudo-haptics. We propose a new pseudo-haptic feedback based method capable of conveying 3D haptic information and combining visual haptics with force feedback to enhance the user’s haptic experience. We focused on an application related to tumor identification during palpation and evaluated the proposed method in an experimental study where users interacted with a haptic device and graphical interface while exploring a virtual model of soft tissue, which represented stiffness distribution of a silicone phantom tissue with embedded hard inclusions. The performance of hard inclusion detection using force feedback only, pseudo-haptic feedback only, and the combination of the two feedbacks were compared with the direct hand touch. The combination method and direct hand touch had no significant difference in the detection results. Compared with the force feedback alone, our method increased the sensitivity by 5%, the positive predictive value by 4%, and decreased detection time by 48.7%. The proposed methodology has great potential for robot-assisted minimally invasive surgery and in all applications where remote haptic feedback is needed

An Experimental and Numerical Study on Tactile Neuroimaging: A Novel Minimally Invasive Technique for Intraoperative Brain Imaging

This is the peer reviewed version of the following article: Moslem Sadeghi-Goughari, Yanjun Qian, Soo Jeon, Sohrab Sadeghi and Hyock-Ju Kwon, “An Experimental and Numerical Study on Tactile Neuroimaging: A Novel Minimally Invasive Technique for Intraoperative Brain Imaging,” accepted to The International Journal of Medical Robotics and Computer Assisted Surgery which has been published in final form at: https://doi.org/10.1002/rcs.1893. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Background The success of tumor neurosurgery is highly dependent on the ability to accurately localize the operative target, which may be shifted during the operation. Performing an intraoperative brain imaging is crucial in minimally invasive neurosurgery to detect the effect of brain shift on the tumor’s location, and to maximize the efficiency of tumor resection. Method The major objective of this research is to introduce the tactile neuroimaging as a novel minimally invasive technique for intraoperative brain imaging. To investigate the feasibility of the proposed method, an experimental and numerical study was first performed on silicone phantoms mimicking the brain tissue with a tumor. Then the study was extended to a clinical model with the meningioma tumor. Results The stress distribution on the brain surface has high potential to intraoperatively localize the tumor. Conclusion Results suggest that tactile neuroimaging can be used to provide a non-invasive, and real-time intraoperative data on tumor’s features.Natural Sciences and Engineering Research Council || RGPIN/2015-05273, RGPIN/2015-04118, RGPAS/354703-201

Design and Nonlinear Control of a Haptic Glove for Virtual Palpation

This dissertation presents the design, kinematic analysis, and nonlinear control of a Haptic Glove for medical elastographic imaging virtual palpation. Of the 13 degrees of freedom present in the index finger, middle finger, and thumb of the hand, the design fixes 4, constrains 2 and controls 6 with pneumatic air cylinder actuators, allowing uncontrolled, but measured motion in the remaining 1 degree of freedom. Nearly linear bijective transfer functions between the actuator positions and joint angles are found in closed form for all 6 actuated joints. A nonlinear, sliding-mode controller that allows each actuator to be controlled by a single 5/3 proportional valve is designed and implemented. Test results for typical palpation motions are presented and discussed

A mechatronic platform for computer aided detection of nodules in anatomopathological analyses via stiffness and ultrasound measurements

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histological analyses. The proposed approach takes advantage of the property of cancer to alter the mechanical and acoustical properties of tissues, because of changes in stiffness and density. A force sensor and an ultrasound probe were combined to detect such alterations during force-regulated indentations. To explore the specimens, regardless of their orientation and shape, a scanned area of the test sample was defined using shape recognition applying optical background subtraction to the images captured by a camera. The motorized platform was validated using seven phantom tissues, simulating the mechanical and acoustical properties of ex-vivo diseased tissues, including stiffer nodules that can be encountered in pathological conditions during histological analyses. Results demonstrated the platform’s ability to automatically explore and identify the inclusions in the phantom. Overall, the system was able to correctly identify up to 90.3% of the inclusions by means of stiffness in combination with ultrasound measurements, paving pathways towards robotic palpation during intraoperative examinations

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi