Granular jamming based controllable organ design for abdominal palpation

Medical manikins play an essential role in the training process of physicians. Currently, most available simulators for abdominal palpation training do not contain controllable organs for dynamic simulations. In this paper, we present a soft robotics controllable liver that can simulate various liver diseases and symptoms for effective and realistic palpation training. The tumors in the liver model are designed based on granular jamming with positive pressure, which converts the fluid-like impalpable particles to a solid-like tumor state by applying low positive pressure on the membrane. Through inflation, the tumor size, liver stiffness, and liver size can be controlled from normal liver state to various abnormalities including enlarged liver, cirrhotic liver, and multiple cancerous and malignant tumors. Mechanical tests have been conducted in the study to evaluate the liver design and the role of positive pressure granular jamming in tumor simulations

A Line Flow Granular Computing Approach for Economic Dispatch with Line Constraints

© 2017 IEEE. Line flow calculation plays a critically important role to guarantee the stable operation of power system in economic dispatch (ED) problems with line constraints. This paper presents a line flow granular computing approach for power flow calculation to assist the investigation on ED with line constraints, where the hierarchy method is adopted to divide the power network into multiple layers to reduce computational complexity. Each layer contains granules for granular computing, and the layer network is reduced by Ward equivalent retaining the PV nodes and boundary nodes of tie lines to decrease the data dimension. Then, Newton-Raphson method is applied further to calculate the power line flows within the layer. This approach is tested on IEEE 39-bus and 118-bus systems. The testing results show that the granular computing approach can solve the line flow problem in 9.2 s for the IEEE 118-bus system, while the conventional AC method needs 44.56 s. The maximum relative error of the granular computing approach in line flow tests is only 0.43%, which is quite small and acceptable. Therefore, the case studies demonstrate that the proposed granular computing approach is correct, effective, and can ensure the accuracy and efficiency of power line flow calculation

Soft Robot-Assisted Minimally Invasive Surgery and Interventions: Advances and Outlook

Since the emergence of soft robotics around two decades ago, research interest in the field has escalated at a pace. It is fuelled by the industry's appreciation of the wide range of soft materials available that can be used to create highly dexterous robots with adaptability characteristics far beyond that which can be achieved with rigid component devices. The ability, inherent in soft robots, to compliantly adapt to the environment, has significantly sparked interest from the surgical robotics community. This article provides an in-depth overview of recent progress and outlines the remaining challenges in the development of soft robotics for minimally invasive surgery

Liver Shape Analysis using Statistical Parametric Maps at Population Scale

Background: Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. Methods: Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. Results: We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. Conclusions: The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions

An open environment CT-US fusion for tissue segmentation during interventional guidance.

Therapeutic ultrasound (US) can be noninvasively focused to activate drugs, ablate tumors and deliver drugs beyond the blood brain barrier. However, well-controlled guidance of US therapy requires fusion with a navigational modality, such as magnetic resonance imaging (MRI) or X-ray computed tomography (CT). Here, we developed and validated tissue characterization using a fusion between US and CT. The performance of the CT/US fusion was quantified by the calibration error, target registration error and fiducial registration error. Met-1 tumors in the fat pads of 12 female FVB mice provided a model of developing breast cancer with which to evaluate CT-based tissue segmentation. Hounsfield units (HU) within the tumor and surrounding fat pad were quantified, validated with histology and segmented for parametric analysis (fat: -300 to 0 HU, protein-rich: 1 to 300 HU, and bone: HU>300). Our open source CT/US fusion system differentiated soft tissue, bone and fat with a spatial accuracy of ∼1 mm. Region of interest (ROI) analysis of the tumor and surrounding fat pad using a 1 mm(2) ROI resulted in mean HU of 68±44 within the tumor and -97±52 within the fat pad adjacent to the tumor (p<0.005). The tumor area measured by CT and histology was correlated (r(2) = 0.92), while the area designated as fat decreased with increasing tumor size (r(2) = 0.51). Analysis of CT and histology images of the tumor and surrounding fat pad revealed an average percentage of fat of 65.3% vs. 75.2%, 36.5% vs. 48.4%, and 31.6% vs. 38.5% for tumors <75 mm(3), 75-150 mm(3) and >150 mm(3), respectively. Further, CT mapped bone-soft tissue interfaces near the acoustic beam during real-time imaging. Combined CT/US is a feasible method for guiding interventions by tracking the acoustic focus within a pre-acquired CT image volume and characterizing tissues proximal to and surrounding the acoustic focus

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