Endoscopic Tactile Capsule for Non-Polypoid Colorectal Tumour Detection

An endoscopic tactile robotic capsule, embedding miniaturized MEMS force sensors, is presented. The capsule is conceived to provide automatic palpation of non-polypoid colorectal tumours during colonoscopy, since it is characterized by high degree of dysplasia, higher invasiveness and lower detection rates with respect to polyps. A first test was performed employing a silicone phantom that embedded inclusions with variable hardness and curvature. A hardness-based classification was implemented, demonstrating detection robustness to curvature variation. By comparing a set of supervised classification algorithms, a weighted 3-nearest neighbor classifier was selected. A bias force normalization model was introduced in order to make different acquisition sets consistent. Parameters of this model were chosen through a particle swarm optimization method. Additionally, an ex-vivo test was performed to assess the capsule detection performance when magnetically-driven along a colonic tissue. Lumps were identified as voltage peaks with a prominence depending on the total magnetic force applied to the capsule. Accuracy of 94 % in hardness classification was achieved, while a 100 % accuracy is obtained for the lump detection within a tolerance of 5 mm from the central path described by the capsule. In real application scenario, we foresee our device aiding physicians to detect tumorous tissues

A Framework for Tumor Localization in Robot-Assisted Minimally Invasive Surgery

Manual palpation of tissue is frequently used in open surgery, e.g., for localization of tumors and buried vessels and for tissue characterization. The overall objective of this work is to explore how tissue palpation can be performed in Robot-Assisted Minimally Invasive Surgery (RAMIS) using laparoscopic instruments conventionally used in RAMIS. This thesis presents a framework where a surgical tool is moved teleoperatively in a manner analogous to the repetitive pressing motion of a finger during manual palpation. We interpret the changes in parameters due to this motion such as the applied force and the resulting indentation depth to accurately determine the variation in tissue stiffness. This approach requires the sensorization of the laparoscopic tool for force sensing. In our work, we have used a da Vinci needle driver which has been sensorized in our lab at CSTAR for force sensing using Fiber Bragg Grating (FBG). A computer vision algorithm has been developed for 3D surgical tool-tip tracking using the da Vinci \u27s stereo endoscope. This enables us to measure changes in surface indentation resulting from pressing the needle driver on the tissue. The proposed palpation framework is based on the hypothesis that the indentation depth is inversely proportional to the tissue stiffness when a constant pressing force is applied. This was validated in a telemanipulated setup using the da Vinci surgical system with a phantom in which artificial tumors were embedded to represent areas of different stiffnesses. The region with high stiffness representing tumor and region with low stiffness representing healthy tissue showed an average indentation depth change of 5.19 mm and 10.09 mm respectively while maintaining a maximum force of 8N during robot-assisted palpation. These indentation depth variations were then distinguished using the k-means clustering algorithm to classify groups of low and high stiffnesses. The results were presented in a colour-coded map. The unique feature of this framework is its use of a conventional laparoscopic tool and minimal re-design of the existing da Vinci surgical setup. Additional work includes a vision-based algorithm for tracking the motion of the tissue surface such as that of the lung resulting from respiratory and cardiac motion. The extracted motion information was analyzed to characterize the lung tissue stiffness based on the lateral strain variations as the surface inflates and deflates

Conditioned haptic perception for 3D localization of nodules in soft tissue palpation with a variable stiffness probe

This paper provides a solution for fast haptic information gain during soft tissue palpation using a Variable Lever Mechanism (VLM) probe. More specifically, we investigate the impact of stiffness variation of the probe to condition likelihood functions of the kinesthetic force and tactile sensors measurements during a palpation task for two sweeping directions. Using knowledge obtained from past probing trials or Finite Element (FE) simulations, we implemented this likelihood conditioning in an autonomous palpation control strategy. Based on a recursive Bayesian inferencing framework, this new control strategy adapts the sweeping direction and the stiffness of the probe to detect abnormal stiff inclusions in soft tissues. This original control strategy for compliant palpation probes shows a sub-millimeter accuracy for the 3D localization of the nodules in a soft tissue phantom as well as a 100% reliability detecting the existence of nodules in a soft phantom

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

前立腺癌スクリーニングのためのロボット触診システムの開発

Tohoku University田中真美課

Development of a Harmonic Motion Imaging guided Focused Ultrasound system for breast tumor characterization and treatment monitoring

Breast cancer is the most common cancer and the second leading cause of cancer death among women. About 1 in 8 U.S. women (about 12%) will develop invasive breast cancer over the course of their lifetime. Existing methods of early detection of breast cancer include mammography and palpation, either by patient self-examination or clinical breast exam. Palpation is the manual detection of differences in tissue stiffness between breast tumors and normal breast tissue. The success of palpation relies on the fact that the stiffness of breast tumors is often an order of magnitude greater than that of normal breast tissue, i.e., breast lesions feel ''hard'' or ''lumpy'' as compared to normal breast tissue. A mammogram is an x-ray that allows a qualified specialist to examine the breast tissue for any suspicious areas. Mammography is less likely to reveal breast tumors in women younger than 50 years with denser breast than in older women. When a suspicious site is detected in the breast through a breast self-exam or on a screening mammogram, the doctor may request an ultrasound of the breast tissue. A breast ultrasound can provide evidence about whether the lump is a solid mass, a cyst filled with fluid, or a combination of the two. An invasive needle biopsy is the only diagnostic procedure that can definitely determine if the suspicious area is cancerous. In the clinic, 80% of women who have a breast biopsy do not have breast cancer. Most women with breast cancer diagnosed will have some type of surgery to remove the tumor. Depending on the type of breast cancer and how advanced it is, the patient might need other types of treatment as well, such as chemotherapy and radiation therapy. Image-guided minimally-invasive treatment of localized breast tumor as an alternative to traditional breast surgery, such as high intensity focused ultrasound (HIFU) treatment, has become a subject of intensive research. HIFU applies extreme high temperatures to induce irreversible cell injury, tumor apoptosis and coagulative necrosis. Compared with conventional surgical procedures the main advantages of HIFU ablation lie in the fact that it is non-invasive, less scarring and less painful, allowing for shorter recovery time. HIFU can be guided by MRI (MRgFUS) or by conventional diagnostic ultrasound (USgFUS). Worldwide, thousands of patients with uterine fibroids, liver cancer, breast cancer, pancreatic cancer, bone tumors, and renal cancer have been treated by USgFUS. In this dissertation, the objective is to develop an integrated Harmonic Motion Imaging guided Focused Ultrasound (HMIgFUS) system as a clinical monitoring technique for breast HIFU with the added capability of detecting tumors for treatment planning, evaluation of tissue stiffness changes during HIFU ablation for treatment monitoring in real time, and assessment of thermal lesion sizes after treatment evaluation. A new HIFU treatment planning method was described that used oscillatory radiation force induced displacement amplitude variations to detect the HIFU focal spot before lesioning. Using this method, we were able to visualize the HMIgFUS focal region at variable depths. By comparing the estimated displacement profiles with lesion locations in pathology, we demonstrated the feasibility of using this HMI-based technique to localize the HIFU focal spot and predict lesion location during the planning phase. For HIFU monitoring, a HIFU lesion detection and ablation monitoring method was first developed using oscillatory radiation force induced displacement amplitude variations in real time. Using this method, the HMIgFUS focal region and lesion formation were visualized in real time at a feedback rate of 2.4 Hz. By comparing the estimated lesion size against gross pathology, the feasibility of using HMIgFUS to monitor treatment and lesion formation without interruption is demonstrated. In order to reduce the imaging time, it is shown in this dissertation that using the steered FUS beam, HMI can be used to image a 2.3 times larger ROI without requiring physical movement of the transducer. Using steering for HMI can be used to shorten the total imaging duration without requiring physical movement of the transducer. For the application of breast tumor, HMI and HMIgFUS were optimized and applied to ex vivo breast tissue. The results showed that HMI is experimentally capable of mapping and differentiating stiffness in normal and abnormal breast tissues. HMIgFUS can also successfully generate thermal lesions on normal and pathological breast tissues. HMI has also been applied to post-surgical breast mastectomy specimens to mimic the in vivo environment. In the end, the first HMI clinical system has been built with added capability of GUP-based parallel beamforming. A clinical trial has been approved at Columbia University to image breast tumor on patient. The HMI clinical system has shown to be able to map fibroadenoma mass on two patients with valid HMI displacement. The study in this dissertation may yield an early-detection technique for breast cancer without any age discrimination and thus, increase the survival rate

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