Modeling Small Molecule Elution From a Hydrogel using a Microfluidic Technique

Drug release from a fluid-contacting biomaterial is simulated using a microfluidic device with channels defined by solute-loaded hydrogel. In order to mimic a drug delivery device, a solution of poly(ethylene glycol) diacrylate (PEG-DA), solute, and photoinitiator is cured inside a microfluidic device with a channel through the center ofthe hydrogel. As water is pumped through the channel, solute diffuses out of the hydrogel and into the water. Channel sizes within the devices range from 300 µm to 1000 µm to simulate vessels within the body. The properties of the PEG hydrogel were characterizedby the extent of crosslinking, the swelling ratio, and the mesh size of the gel. The structure of the hydrogel was related to the UV exposure dosage and the initial water and solute content in the PEG-DA solution.

Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and point distribution model

Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and a point distribution model is discussed. We present a 2D/3D reconstruction scheme combining statistical extrapolation and regularized shape deformation with an iterative image-to-model correspondence establishing algorithm, and show its application to reconstruct the surface of proximal femur. The image-to-model correspondence is established using a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformation to find a fraction of best matched 2D point pairs between features detected from the fluoroscopic images and those extracted from the 3D model. The obtained 2D point pairs are then used to set up a set of 3D point pairs such that we turn a 2D/3D reconstruction problem to a 3D/3D one. We designed and conducted experiments on 11 cadaveric femurs to validate the present reconstruction scheme. An average mean reconstruction error of 1.2 mm was found when two fluoroscopic images were used for each bone. It decreased to 1.0 mm when three fluoroscopic images were used.

Automatic extraction of femur contours from calibrated fluoroscopic images

Automatic identification and extraction of bone contours from X-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated X-ray images. The automatic initialization is solved by an estimation of Bayesian network algorithm to fit a multiple component geometrical model to the X-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the X-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Preliminary experiments on clinical data sets verified its validity

An integrated approach for reconstructing surface models of the proximal femur from sparse input data for surgical navigation

A patient-specific surface model of the proximal femur plays an important role in planning and supporting various computer-assisted surgical procedures including total hip replacement, hip resurfacing, and osteotomy of the proximal femur. The common approach to derive 3D models of the proximal femur is to use imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, the high logistic effort, the extra radiation (CT-imaging), and the large quantity of data to be acquired and processed make them less functional. In this paper, we present an integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific model of the proximal femur from intra-operatively available sparse data. Results of experiments performed on dry cadaveric bones using dozens of 3D points are presented, as well as experiments using a limited number of 2D X-ray images, which demonstrate promising accuracy of the present approach.

Mechanical modeling of Intraneural Ganglion Cyst

Intraneural Ganglion Cyst is a 200 year old mystery related to nerve injury which is yet to be solved. Current treatments for the above problem are relatively simple procedures related to removal of cystic contents from the nerve. However, these treatments may result into neuropathic pain and recurrence of the cyst. The articular theory proposed by Spinner et al., (Spinner et al. 2003) takes into consideration the neurological deficit in Common Peroneal Nerve (CPN) branch of the sciatic nerve and affirms that in addition to the above treatments, ligation of articular branch results into foolproof eradication of the deficit. Mechanical Modeling of the Affected Nerve Cross Section will reinforce the articular theory (Spinner et al. 2003). As the cyst propagates, it compresses the neighboring fascicles and the nerve cross section appears like a signet ring. Hence, in order to mechanically model the affected nerve cross section; computational methods capable of modeling excessively large deformations are required. Traditional FEM produces distorted elements while modeling such deformations, resulting into inaccuracies and premature termination of the analysis. The methods described in this Master’s Thesis are effective enough to be able to simulate such deformations. The results obtained from the model adequately resemble the MRI image obtained at the same location and shows an appearance of a signet ring. This Master’s Thesis describes the neurological deficit in brief followed by detail explanation of the advanced computational methods used to simulate this problem. Finally, qualitative results show the resemblance of mechanical model to MRI images of the Nerve Cross Section at the same location validating the capability of these methods to study this neurological deficit.

PARTICIPANT IMPACTS FROM A PARTICIPATORY WATER MODELING WORKSHOP IN THE SONORA RIVER BASIN, MEXICO

Much of the research in the field of participatory modeling (PM) has focused on the developed world. Few cases are focused on developing regions, and even fewer on Latin American developing countries. The work that has been done in Latin America has often involved water management, often specifically involving water users, and has not focused on the decision making stage of the policy cycle. Little work has been done to measure the effect PM may have on the perceptions and beliefs of decision makers. In fact, throughout the field of PM, very few attempts have been made to quantitatively measure changes in participant beliefs and perceptions following participation. Of the very few exceptions, none have attempted to measure the long-term change in perceptions and beliefs. This research fills that gap. As part of a participatory modeling project in Sonora, Mexico, a region with water quantity and quality problems, I measured the change in beliefs among participants about water models: ability to use and understand them, their usefulness, and their accuracy. I also measured changes in beliefs about climate change, and about water quantity problems, specifically the causes, solutions, and impacts. I also assessed participant satisfaction with the process and outputs from the participatory modeling workshops. Participants were from water agencies, academic institutions, NGOs, and independent consulting firms. Results indicated that participant comfort and self-efficacy with water models, their beliefs in the usefulness of water models, and their beliefs about the impact of water quantity problems changed significantly as a result of the workshops. I present my findings and discuss the results.