Regensburg Center of Biomedical Engineering - RCBE
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The gold standard for the analysis of human kinematics and kinetics is a camera-based motion capture system in combination with force measurement platforms. Alternatively, inertial measurement units can be utilized to obtain human kinematics, while ground reaction forces are computed from full body dynamics. This setup represents a system independent from the spatial confinement of a gait laboratory. The aim of this study is the comparison of the two methods by the investigation of lower limb kinematics and the resulting joint reaction forces within the ankle-, knee- and hip joints. For this purpose, human motion during gait was captured simultaneously by both measurement techniques. 13 trials from 8 different test subjects were evaluated in total. IMU data was processed with a quaternion based Kalman Filter. The data sets were implemented into a musculoskeletal simulation program in order to drive a virtual human body model. Each sensor was aligned to the gravitational and magnetic field vectors of the earth. The angles of flexions, extensions and rotations were analyzed to determine kinematic differences. Joint reaction forces defined kinetic dissimilarities. The overall kinematic differences of both models yielded root mean square errors of 7.62°, 6.02°, 4.95°, 2.79°, 2.38° and 3.56° for ankle flexion, subtalar eversion, knee flexion, hip external rotation, hip abduction and hip flexion, respectively. The proximo-distal differences in force peaks between the models yielded overall for the ankle, 57.33 %Bodyweight(BW) ± 46.86 %BW (16.66 %(Maximum peak to peak) ± 13.62 %) for the knee 37.09 %BW ± 29.33 %BW (17.65 % ± 15.44 %) and 32.03 %BW ± 24.33 %BW (15.6 % ± 12.54 %) for the hip. The overall outcome of this work investigated an approach independent of the common setup of the gait laboratory, thus enabling a cheaper and more flexible technology as an alternative. However, kinematic and thus kinetic differences remain rather large. Future work aims to improve the contact criterion for the calculation of the ground reaction forces and the implementation of a full-body calibration algorithm for the IMU system in order to counteract magnetic field disturbances.
Common Schanz screw systems can be used to stabilize pelvic ring fractures. In order to accommodate for different patient’s requirements, implants can be placed in cranio-caudal direction into the os ilium (T1), or into the supraacetabular bone canal, and thus, in dorso-ventral direction (T2). Whereas both techniques are currently used, no data of the biomechanical behavior is available up to this date. The aim of this study is to analyze, whether T2 shows biomechanical advantages with respect to tissue and implant stresses due to the enlarged bone-implant interface. Forces acting on the pelvis were analyzed using motion capture data of a gait cycle obtained by the utilization of a musculoskeletal simulation program. A three dimensional finite element (FE) model of the pelvis with grayscale-based material properties was generated. The muscle and joint reaction forces at toe-off were applied to the FE model and instable pelvis fractures were implemented. The osteosynthesis systems were positioned within the model in order to enable the comparison between the two different surgical techniques. Stresses and displacements were analyzed for bone tissue, fracture zone and implant.
T2 lead to approx. 30% larger displacements in the fracture zone. Von-Mises stresses were larger for T2 in the implant (80 MPa vs. 227 MPa), whereas T1 leads to larger stresses in the bone tissue (200 MPa vs. 140 MPa).
Both implantation techniques showed a good biomechanical behavior. Differences could be found with respect to tissue strains and deformations in the fracture zone. If bone quality or fracture healing are of concern, T2 or T1 should be used, respectively. However, both techniques seem to be applicable for cases with no special requirements. Further analyses aim to investigate the behavior under cyclic loading.
Simulation of Fluid-Structure Interaction between injection medium and balloon catheter using ICFD
(2017)
Arteriosclerosis is a major health issue worldwide. While it is commonly treated by the implantation of an balloon-expandable stent, micro injuries may occur during stent deployment, and induce in-stent restenosis, whose consequence can be fatal. Studying this undesirable phenomenon is usually limited as experimental data is hard to obtain on ethical ground. Numerical simulation are performed to better understand this problem. To construct a more realistic simulation of a balloon-expandable stent, a partitioned strongly-coupled FSI simulation of the balloon deployment was set up using the ICFD solver of LS-DYNA, - a quite innovative approach. The complex balloon configuration as well as the interaction of the injection medium and the balloon structure was considered. The balloon structure consisting of shell elements was obtained from preliminary balloon folding and pleating simulations. The balloon consists of a flexible thin walled polyamide. The injection fluid is implemented using volume elements. Balloon deployment was initiated by a pressure boundary condition inducing a volume flow into the balloon. The initial feasibility analysis showed promising result including a continuous balloon deployment and a reasonable development of the fluid pressure and velocity field. However, applying this FSI approach to a more complex balloon structure led to a non convergent solution. The non-convergence could be mainly reduced to mechanical factors including the low wall thickness of the balloon (< 0.05 mm) and the flexibility of the polyamide. Further, the ICFD solver shows less accuracy concerning the FSI conditions when dealing with thin flexible structures as well as enclosed volumes. A shell thickness of 0.06 mm is believed to result in a convergent solution.
Work places and conditions strains the human body, both psychologically and biomechanically. In order to analyse working conditions and in the following to improve them, detailed knowledge about the effect of the different stressors on the body is needed. This manuscript discusses methods on how to evaluate biomechanical and mental loading and its effect on the musculoskeletal system. A possible workflow for the analysis is presented.
The development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.
The Impact of Semi-Automated Segmentation and 3D Analysis on Testing New Osteosynthesis Material
(2017)
A new protocol for testing osteosynthesis material postoperatively combining semi-automated segmentation and 3D analysis of surface meshes is proposed. By various steps of transformation and measuring, objective data can be collected. In this study the specifications of a locking plate used for mediocarpal arthrodesis of the wrist were examined. The results show, that union of the lunate, triquetrum, hamate and capitate was achieved and that the plate is comparable to coexisting arthrodesis systems. Additionally, it was shown, that the complications detected correlate to the clinical outcome. In synopsis, this protocol is considered beneficial and should be taken into account in further studies.
Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barrett's esophagus (BE) is a common type of reflux that is not straight forward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barrett'sesophagus, with promising results and outperforming the well known Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-wordsthat is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735(SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy.
We propose an automatic approach for early detection of adenocarcinoma in the esophagus. High-definition endoscopic images (50 cancer, 50 Barrett) are partitioned into a dataset containing approximately equal amounts of patches showing cancerous and non-cancerous regions. A deep convolutional neural network is adapted to the data using a transfer learning approach. The final classification of an image is determined by at least one patch, for which the probability being a cancer patch exceeds a given threshold. The model was evaluated with leave one patient out cross-validation. With sensitivity and specificity of 0.94 and 0.88, respectively, our findings improve recently published results on the same image data base considerably. Furthermore, the visualization of the class probabilities of each individual patch indicates, that our approach might be extensible to the segmentation domain.