Use of a 3D perfusion bioreactor with osteoblasts and osteoblast/endothelial cell co-cultures to improve tissue-engineered bone

The delivery of oxygen, nutrients, and the removal of waste are essential for cellular survival. Culture systems for 3D bone tissue engineering have addressed this issue by utilizing perfusion flow bioreactors that stimulate osteogenic activity through the delivery of oxygen and nutrients by low-shear fluid flow. It is also well established that bone responds to mechanical stimulation, but may desensitize under continuous loading. While perfusion flow and mechanical stimulation are used to increase cellular survival in vitro, 3D tissue-engineered constructs face additional limitations upon in vivo implantation. As it requires significant amounts of time for vascular infiltration by the host, implants are subject to an increased risk of necrosis. One solution is to introduce tissue-engineered bone that has been pre-vascularized through the co-culture of osteoblasts and endothelial cells on 3D constructs. It is unclear from previous studies: 1) how 3D bone tissue constructs will respond to partitioned mechanical stimulation, 2) how gene expression compares in 2D and in 3D, 3) how co-cultures will affect osteoblast activity, and 4) how perfusion flow will affect co-cultures of osteoblasts and endothelial cells. We have used an integrated approach to address these questions by utilizing mechanical stimulation, perfusion flow, and a co-culture technique to increase the success of 3D bone tissue engineering. We measured gene expression of several osteogenic and angiogenic genes in both 2D and 3D (static culture and mechanical stimulation), as well as in 3D cultures subjected to perfusion flow, mechanical stimulation and partitioned mechanical stimulation. Finally, we co-cultured osteoblasts and endothelial cells on 3D scaffolds and subjected them to long-term incubation in either static culture or under perfusion flow to determine changes in gene expression as well as histological measures of osteogenic and angiogenic activity. We discovered that 2D and 3D osteoblast cultures react differently to shear stress, and that partitioning mechanical stimulation does not affect gene expression in our model. Furthermore, our results suggest that perfusion flow may rescue 3D tissue-engineered constructs from hypoxic-like conditions by reducing hypoxia-specific gene expression and increasing histological indices of both osteogenic and angiogenic activity. Future research to elucidate the mechanisms behind these results may contribute to a more mature bone-like structure that integrates more quickly into host tissue, increasing the potential of bone tissue engineering.

POSE ESTIMATION AND 3D RECONSTRUCTION USING SENSOR FUSION

A camera maps 3-dimensional (3D) world space to a 2-dimensional (2D) image space. In the process it loses the depth information, i.e., the distance from the camera focal point to the imaged objects. It is impossible to recover this information from a single image. However, by using two or more images from different viewing angles this information can be recovered, which in turn can be used to obtain the pose (position and orientation) of the camera. Using this pose, a 3D reconstruction of imaged objects in the world can be computed. Numerous algorithms have been proposed and implemented to solve the above problem; these algorithms are commonly called Structure from Motion (SfM). State-of-the-art SfM techniques have been shown to give promising results. However, unlike a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) which directly give the position and orientation respectively, the camera system estimates it after implementing SfM as mentioned above. This makes the pose obtained from a camera highly sensitive to the images captured and other effects, such as low lighting conditions, poor focus or improper viewing angles. In some applications, for example, an Unmanned Aerial Vehicle (UAV) inspecting a bridge or a robot mapping an environment using Simultaneous Localization and Mapping (SLAM), it is often difficult to capture images with ideal conditions. This report examines the use of SfM methods in such applications and the role of combining multiple sensors, viz., sensor fusion, to achieve more accurate and usable position and reconstruction information. This project investigates the role of sensor fusion in accurately estimating the pose of a camera for the application of 3D reconstruction of a scene. The first set of experiments is conducted in a motion capture room. These results are assumed as ground truth in order to evaluate the strengths and weaknesses of each sensor and to map their coordinate systems. Then a number of scenarios are targeted where SfM fails. The pose estimates obtained from SfM are replaced by those obtained from other sensors and the 3D reconstruction is completed. Quantitative and qualitative comparisons are made between the 3D reconstruction obtained by using only a camera versus that obtained by using the camera along with a LIDAR and/or an IMU. Additionally, the project also works towards the performance issue faced while handling large data sets of high-resolution images by implementing the system on the Superior high performance computing cluster at Michigan Technological University.