University of Macau / Faculty of Science and Technology / Department of Electrical and Computer Engineering

Phase-contrast magnetic resonance imaging (PC-MRI) is a powerful tool for measuring blood flow and has a wide range of cardiovascular applications. Simulation of PC-MRI from numerical flow data would be useful for addressing the data quality of PC-MRI measurements and to study and understand different artifacts. It would also make it possible to optimize imaging parameters prior to the PC-MRI measurements and to evaluate different methods for measuring wall shear stress. Based on previous studies a PC-MRI simulation tool was developed. An Eulerian-Lagrangian approach was used to solve the problem. Computational fluid dynamics (CFD) data calculated on a fix structured mesh (Eulerian point of view) were used as input. From the CFD data spin particle trajectories were computed. The magnetization of the spin particle is then evaluated as the particle travels along its trajectory (Lagrangian point of view). The simulated PC-MRI data were evaluated by comparison with PC-MRI measurements on an in vitro phantom. Results indicate that the PC-MRI simulation tool functions well. However, further development is required to include some of the artifacts. Decreasing the computation time will make more accurate and powerful simulations possible. Several suggestions for improvements are presented in this report.

Because of their compatibility and precise results bio-optical methods based on measurements of the optical tissue properties gain importance in non-invasive medical therapy and diagnostic. For development and standardization of medical devices optical phantoms are suitable. The present report handles the creation and evaluation of solid tissue phantoms, made up of Agar, Vasolipid and ink utilizing different mixture ratios. After cutting the models in slices of 0.2 to 1.1 mm thickness the absorption- and scattering coefficient were measured using a collimated laser beam setup. As result of the study a formula for the preparation of solid optical tissue phantoms with desired optical properties was found, that is valid for models containing 1.12 % Agar.

<p>Phase-contrast magnetic resonance imaging (PC-MRI) is a powerful tool for measuring blood flow and has a wide range of cardiovascular applications. Simulation of PC-MRI from numerical flow data would be useful for addressing the data quality of PC-MRI measurements and to study and understand different artifacts. It would also make it possible to optimize imaging parameters prior to the PC-MRI measurements and to evaluate different methods for measuring wall shear stress.</p><p>Based on previous studies a PC-MRI simulation tool was developed. An Eulerian-Lagrangian approach was used to solve the problem. Computational fluid dynamics (CFD) data calculated on a fix structured mesh (Eulerian point of view) were used as input. From the CFD data spin particle trajectories were computed. The magnetization of the spin particle is then evaluated as the particle travels along its trajectory (Lagrangian point of view).</p><p>The simulated PC-MRI data were evaluated by comparison with PC-MRI measurements on an in vitro phantom. Results indicate that the PC-MRI simulation tool functions well. However, further development is required to include some of the artifacts. Decreasing the computation time will make more accurate and powerful simulations possible. Several suggestions for improvements are presented in this report.</p>

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Yousef Haik, Florida State University, College of Engineering, Dept. of Mechanical Engineering. Title and description from dissertation home page (viewed June 16, 2004). Includes bibliographical references.

In this thesis, we develop and test a technology for computer-assisted assessments of newborn brain maturity from sleep electroencephalogram (EEG). Brain maturation of newborns is reflected in rapid development of EEG patterns over a number of weeks after conception. Observing the maturational patterns, experts can assess newborn’s EEG maturity with an accuracy ±2 weeks of newborn’s stated age. A mismatch between the EEG patterns and newborn’s physiological age alerts clinicians about possible neurological problems. Analysis of newborn EEG requires specialised skills to recognise the maturity-related waveforms and patterns and interpret them in the context of newborns age and behavioural state. It is highly desirable to make the results of maturity assessment most accurate and reliable. However, the expert analysis is limited in capability to estimate the uncertainty in assessments. To enable experts quantitatively evaluate risks of brain dysmaturity for each case, we employ the Bayesian model averaging methodology. This methodology, in theory, provides the most accurate assessments along with the estimates of uncertainty, enabling experts to take into account the full information about the risk of decision making. Such information is particularly important when assessing the EEG signals which are highly variable and corrupted by artefacts. The use of decision tree models within the Bayesian averaging enables interpreting the results as a set of rules and finding the EEG features which make the most important contribution to assessments. The developed technology was tested on approximately 1,000 EEG recordings of newborns aged 36 to 45 weeks post conception, and the accuracy of assessments was comparable to that achieved by EEG experts. In addition, it was shown that the Bayesian assessment can be used to quantitatively evaluate the risk of brain dysmaturity for each EEG recording.

The first chapter of this dissertation explores how the regulatory approval process affects innovation incentives in medical technologies. While prior studies of medical innovation under regulation have found an early mover regulatory advantage for drugs, I find the opposite to be true for medical devices. Using detailed data on over three decades of high-risk medical device approval times in the United States, I show pioneer entrants spend approximately 34 percent (7.2 months) longer in the approval process than the first follow-on innovator. Back-of-the-envelope calculations suggest that the opportunity cost of capital of a delay of this length is upwards of 7 percent of the total cost of bringing a new device to market. I consider how different types of regulatory uncertainty affect approval times and find that a product's technological novelty is largely unrelated to time spent under review. In contrast, uncertainty about application content and format appears to play a large role: when objective guidelines for evaluation are published, approval times quicken for subsequent entrants. Finally, I consider how the regulatory process affects firms&rsquo; market entry strategies and find that financially constrained firms are less likely to enter new device markets as pioneers.

Today we can see how companies are making significant investments in various methods and tools to access and utilize the users’ knowledge for the purpose of innovation efforts. What many highly innovative companies try to accomplish with these investments is to develop and establish sustained producer-user ecosystems. The aim of these ecosystems is often to encourage users to collaborate with each other and with the producer in order to generate innovations related to the offering of the producer. However, although these ecosystems are proven to create new innovation opportunities for companies, it has been shown that a close collaboration with many users brings about a number of challenges for companies. For example, it can be costly and time-consuming to establish and utilize large numbers of user relationships and it can be difficult to align the innovation interests of established producers with those of the users. Moreover, the fact that many innovating users have relationships to one another can contribute to conflicts of interests and established producers may have to balance stability and change within the ecosystem. Another challenge in the utilization of user relationships is that it is not only dependent on the direct interaction with users but also the internal dissemination and utilization of information related to the users’ needs. This dissemination can be problematic since it is costly and difficult to forward timely and reliable information about the users’ needs. Consequently, the aim of this thesis is to better understand how companies that are operating within sustained producer-user ecosystems can leverage user relationships for the purpose of innovation. The research forming the foundation for this thesis was carried out within two established medical technology companies that successfully had developed innovations within this type of ecosystems. Quantitative and qualitative data was collectedand a number of different analyses were conducted. The results reveal that these ecosystems can be understood as a system where direct and indirect user experiences are distributed among employees and users. This in turn implies that employees utilization of user relationships can be understood as a function of the extent to which employees knows and values the pertinence of their own as well as others direct and indirect user experiences as well as accessibility and cost of seeking user-information from other people. The results demonstrate that when companies experience high costs related to the acquisition of user experiences they may benefit from employing users to occupy boundary-spanning roles. However, the thesis reveals how the utilization of such boundary-spanning roles brings with it a number of organizational challenges. Moreover, an important aspect of success tends to be the utilization of relationships to so-called transformational users. These users experience problems with established producers' current products before the majority of users, they adopt new technologies earlier than their peers, and they cooperate with established producers for the purpose of transformation of a product field in order to obtain or maintain a central position within the ecosystem. Furthermore, the results reveal how established producers and users are jointly engaged in value creation through various collaborations. However, the results also indicate that the utilization of user relationships for innovation within these types of ecosystems is, to a large extent, a matter of managing tensions emerging within and around these collaborations. On a general level, this thesis points to the potential benefits of considering producer-user ecosystems as a comprehensive perspective, which may explain how companies gain and sustain a competitive edge, rather than one out of many approaches that companies can apply in order to leverage user relationships for innovation. / <p>QC 20140918</p>

Thesis (D.P.H.)--University of Hawaii at Manoa, 1994. / Includes bibliographical references (leaves 119-129). / Microfiche. / x, 129 leaves, bound 29 cm

In parallel with the developments in imaging modalities, image-guided surgery (IGS) can now provide the surgeon with high quality three-dimensional images depicting human anatomy. Although IGS is now in widely use in neurosurgery, there remain some limitations that must be overcome before it can be employed in more general minimally invasive procedures. In this thesis, we have developed several contributions to the field of medical image registration and brain tissue deformation modeling. From the methodology point of view, medical image registration algorithms can be classified into feature-based and intensity-based methods. One of the challenges faced by feature-based registration would be to determine which specific type of feature is desired for a given task and imaging type. For this reason, a point set registration using points and curves feature is proposed, which has the accuracy of registration based on points and the robustness of registration based on lines or curves. We have also tackled the problem on rigid registration of multimodal images using intensity-based similarity measures. Mutual information (MI) has emerged in recent years as a popular similarity metric and widely being recognized in the field of medical image registration. Unfortunately, it ignores the spatial information contained in the images such as edges and corners that might be useful in the image registration. We introduce a new similarity metric, called Adaptive Mutual Information (AMI) measure which incorporates the gradient spatial information. Salient pixels in the regions with high gradient value will contribute more in the estimation of mutual information of image pairs being registered. Experimental results showed that our proposed method improves registration accuracy and it is more robust to noise images which have large deviation from the reference image. Along with this direction, we further improve the technique to simultaneously use all information obtained from multiple features. Using multiple spatial features, the proposed algorithm is less sensitive to the effect of noise and some inherent variations, giving more accurate registration. Brain shift is a complex phenomenon and there are many different reasons causing brain deformation. We have investigated the pattern of brain deformation with respect to location and magnitude and to consider the implications of this pattern for correcting brain deformation in IGS systems. A computational finite element analysis was carried out to analyze the deformation and stress tensor experienced by the brain tissue during surgical operations. Finally, we have developed a prototype visualization display and navigation platform for interpretation of IGS. The system is based upon Qt (cross-platform GUI toolkit) and it integrates VTK (an object-oriented visualization library) as the rendering kernel. Based on the construction of a visualization software platform, we have laid a foundation on the future research to be extended to implement brain tissue deformation into the system.