Articular human joint modelling

Copyright @ Cambridge University Press 2009.The work reported in this paper encapsulates the theories and algorithms developed to drive the core analysis modules of the software which has been developed to model a musculoskeletal structure of anatomic joints. Due to local bone surface and contact geometry based joint kinematics, newly developed algorithms make the proposed modeller different from currently available modellers. There are many modellers that are capable of modelling gross human body motion. Nevertheless, none of the available modellers offer complete elements of joint modelling. It appears that joint modelling is an extension of their core analysis capability, which, in every case, appears to be musculoskeletal motion dynamics. It is felt that an analysis framework that is focused on human joints would have significant benefit and potential to be used in many orthopaedic applications. The local mobility of joints has a significant influence in human motion analysis, in understanding of joint loading, tissue behaviour and contact forces. However, in order to develop a bone surface based joint modeller, there are a number of major problems, from tissue idealizations to surface geometry discretization and non-linear motion analysis. This paper presents the following: (a) The physical deformation of biological tissues as linear or non-linear viscoelastic deformation, based on spring-dashpot elements. (b) The linear dynamic multibody modelling, where the linear formulation is established for small motions and is particularly useful for calculating the equilibrium position of the joint. This model can also be used for finding small motion behaviour or loading under static conditions. It also has the potential of quantifying the joint laxity. (c) The non-linear dynamic multibody modelling, where a non-matrix and algorithmic formulation is presented. The approach allows handling complex material and geometrical nonlinearity easily. (d) Shortest path algorithms for calculating soft tissue line of action geometries. The developed algorithms are based on calculating minimum ‘surface mass’ and ‘surface covariance’. An improved version of the ‘surface covariance’ algorithm is described as ‘residual covariance’. The resulting path is used to establish the direction of forces and moments acting on joints. This information is needed for linear or non-linear treatment of the joint motion. (e) The final contribution of the paper is the treatment of the collision. In the virtual world, the difficulty in analysing bodies in motion arises due to body interpenetrations. The collision algorithm proposed in the paper involves finding the shortest projected ray from one body to the other. The projection of the body is determined by the resultant forces acting on it due to soft tissue connections under tension. This enables the calculation of collision condition of non-convex objects accurately. After the initial collision detection, the analysis involves attaching special springs (stiffness only normal to the surfaces) at the ‘potentially colliding points’ and motion of bodies is recalculated. The collision algorithm incorporates the rotation as well as translation. The algorithm continues until the joint equilibrium is achieved. Finally, the results obtained based on the software are compared with experimental results obtained using cadaveric joints

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

Enhancement of Surgical Training Practice with the Spring Tensor Heuristic Model

The enhancement of surgical simulation tools is an important research study, to assist in the assessment and feedback of medical training practice. In this research, the Spring Tensor Model (STEM) has been used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of the surgical trainer as part of the laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated infrastructure. Combining sensor network framework paradigms to a surgical knowledge-based construct demonstrates how STEMcan enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training

In vitro simulation of calcific aortic valve disease in three-dimensional bioprinted models

BACKGROUND: Calcific aortic valve disease (CAVD) is the most prevalent heart valve disease in the developed world, claiming almost 17,000 deaths annually in the United States. The lack of noninvasive therapeutics to slow or halt the disease warrants the need for further understanding of the pathobiological mechanisms of CAVD. A tri-laminar structure of aortic valve determines the biomechanical properties of its leaflets. Valvular endothelial cells (VECs) and interstitial cells (VICs) are responsible for valve structural integrity. Traditional two-dimensional culture conditions spontaneously activate the pathological differentiation of VICs making in vitro studies challenging. A monolayered three-dimensional (3D) hydrogel platform was recently developed as a novel in vitro culture system to study the phenotypic changes of VICs leading to microcalcification (early stages of calcification). This system, however, did not fully recapitulate the microenvironment of native valve tissues because of the lack of individual layer representations and endothelial coverage. Bioprinting technology, which allows precise and integrated positioning of cells, matrix, and biomolecules, may provide an innovative approach toward building a more biologically relevant 3D culture platform. OBJECTIVE: This study aims to lay the groundwork for building a multilayered 3D-bioprinted culture platform to study CAVD by first validating the use of bioprinting in monolayered cell-laden 3D hydrogel constructs. METHODS: Human VICs were isolated from patients undergoing valve replacement surgeries at Brigham and Women’s Hospital (Boston, MA) according to Institutional Review Board (IRB) protocols. VICs were expanded in culture medium containing growth factors for up to 6 passages and then encapsulated in hydrogels using 3D bioprinting technology. After encapsulation, VIC-laden 3D constructs were cultured in either normal or osteogenic conditions for 21 days. Microcalcification, cell proliferation, and cell apoptosis were evaluated using fluorescent staining and confocal microscopy. Results were compared with results from VIC-laden hydrogels made manually. RESULTS: An increase in microcalcification was observed throughout bioprinted VIC-laden hydrogel constructs cultured in osteogenic conditions for 21 days, whereas normal conditions developed negligible calcification signals. Cell proliferation and apoptosis were not significantly different between normal and osteogenic groups in bioprinted hydrogels. Cell-free hydrogels did not exhibit any microcalcification. Overall, bioprinted hydrogels showed less nonspecific background staining than handmade hydrogels, thus providing a better means for quantitative assessments of 3D culture platforms. CONCLUSION: Based on bioprinting technology, an improved monolayered cell-laden hydrogel platform was successfully established as a first step toward building an in vitro multilayered disease model for studying the pathobiological mechanisms of CAVD. The results in this study were consistent with current literature that proposes calcification as a cell-dependent, apoptotic-independent, and proliferation-independent pathway.2019-07-13T00:00:00

Measurement, modelling, and closed-loop control of crystal shape distribution: Literature review and future perspectives

Crystal morphology is known to be of great importance to the end-use properties of crystal products, and to affect down-stream processing such as filtration and drying. However, it has been previously regarded as too challenging to achieve automatic closed-loop control. Previous work has focused on controlling the crystal size distribution, where the size of a crystal is often defined as the diameter of a sphere that has the same volume as the crystal. This paper reviews the new advances in morphological population balance models for modelling and simulating the crystal shape distribution (CShD), measuring and estimating crystal facet growth kinetics, and two- and three-dimensional imaging for on-line characterisation of the crystal morphology and CShD. A framework is presented that integrates the various components to achieve the ultimate objective of model-based closed-loop control of the CShD. The knowledge gaps and challenges that require further research are also identified

Augmented Reality-Assisted Craniotomy for Parasagittal and Convexity En Plaque Meningiomas and Custom-Made Cranio-Plasty: A Preliminary Laboratory Report

Background: This report discusses the utility of a wearable augmented reality platform in neurosurgery for parasagittal and convexity en plaque meningiomas with bone flap removal and custom-made cranioplasty. Methods: A real patient with en plaque cranial vault meningioma with diffuse and extensive dural involvement, extracranial extension into the calvarium, and homogeneous contrast enhancement on gadolinium-enhanced T1-weighted MRI, was selected for this case study. A patient-specific manikin was designed starting with the segmentation of the patient’s preoperative MRI images to simulate a craniotomy procedure. Surgical planning was performed according to the segmented anatomy, and customized bone flaps were designed accordingly. During the surgical simulation stage, the VOSTARS head-mounted display was used to accurately display the planned craniotomy trajectory over the manikin skull. The precision of the craniotomy was assessed based on the evaluation of previously prepared custom-made bone flaps. Results: A bone flap with a radius 0.5 mm smaller than the radius of an ideal craniotomy fitted perfectly over the performed craniotomy, demonstrating an error of less than ±1 mm in the task execution. The results of this laboratory-based experiment suggest that the proposed augmented reality platform helps in simulating convexity en plaque meningioma resection and custom-made cranioplasty, as carefully planned in the preoperative phase. Conclusions: Augmented reality head-mounted displays have the potential to be a useful adjunct in tumor surgical resection, cranial vault lesion craniotomy and also skull base surgery, but more study with large series is needed

Enhancement of surgical training practice with the spring tensor heuristic model

The enhancement of surgical simulation tools is an important research study, to assist in the assessment and feedback of medical training practice. In this research, the Spring Tensor Model (STEM) has been used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of the surgical trainer as part of the laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated infrastructure. Combining sensor network framework paradigms to a surgical knowledge-based construct demonstrates how STEM can enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training