Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

The 1992 4th NASA SERC Symposium on VLSI Design

Papers from the fourth annual NASA Symposium on VLSI Design, co-sponsored by the IEEE, are presented. Each year this symposium is organized by the NASA Space Engineering Research Center (SERC) at the University of Idaho and is held in conjunction with a quarterly meeting of the NASA Data System Technology Working Group (DSTWG). One task of the DSTWG is to develop new electronic technologies that will meet next generation electronic data system needs. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The NASA SERC is proud to offer, at its fourth symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories, the electronics industry, and universities. These speakers share insights into next generation advances that will serve as a basis for future VLSI design

Architectural Support for Medical Imaging

Advancements in medical imaging research are continuously providing doctors with better diagnostic information, removing the need for unnecessary surgeries and increasing accuracy in predicting life-threatening conditions. However, newly developed techniques are currently limited by the capabilities of existing computer hardware, restricting them to expensive, custom-designed machines that only the largest hospital systems can afford or even worse, precluding them entirely. Many of these issues are due to existing hardware being ill-suited for these types of algorithms and not designed with medical imaging in mind. In this thesis we discuss our efforts to motivate and democratize architectural support for advanced medical imaging tasks with MIRAQLE, a medical image reconstruction benchmark suite. In particular, MIRAQLE focuses on advanced image reconstruction techniques for 3D ultrasound, low-dose X-ray CT, and dynamic MRI. For each imaging modality we provide a detailed background and parallel implementations to enable future hardware development. In addition to providing baseline algorithms for these workloads, we also develop a unique analysis tool that provides image quality feedback for each simulation. This allows hardware designers to explore acceptable image quality trade-offs in algorithm-hardware co-design, potentially allowing for even more efficient solutions than hardware innovations alone could provide. We also motivate the need for such tools by discussing Sonic Millip3De, our low-power, highly parallel hardware for 3D ultrasound. Using Sonic Millip3De, we illustrate the orders-of-magnitude power efficiency improvement that better medical imaging hardware can provide, especially when developed with a hardware-software co-design. We also show validation of the design using a scaled-down FPGA proof-of-concept and discuss our further refinement of the hardware to support a wider range of applications and produce higher frame rates. Overall, with this thesis we hope to enable application specific hardware support for the critical medical imaging tasks in MIRAQLE to make them practical for wide clinical use.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137105/1/rsamp_1.pd

Collective Communications and Computation Mechanisms on the RF Channel for Organic Printed Smart Labels and Resource-limited IoT Nodes

Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) are seen as enabler technologies for realizing the Internet of Things (IoT). Organic and printed Electronics (OE) has the potential to provide low cost and all-printable smart RFID labels in high volumes. With regard to WSN, power harvesting techniques and resource-efficient communications are promising key technologies to create sustainable and for the environment friendly sensing devices. However, the implementation of OE smart labels is only allowing printable devices of ultra-low hardware complexity, that cannot employ standard RFID communications. And, the deployment of current WSN technology is far away from offering battery-free and low-cost sensing technology. To this end, the steady growth of IoT is increasing the demand for more network capacity and computational power. With respect to wireless communications research, the state-of-the-art employs superimposed radio transmission in form of physical layer network coding and computation over the MAC to increase information flow and computational power, but lacks on practicability and robustness so far. With regard to these research challenges we developed in particular two approaches, i.e., code-based Collective Communications for dense sensing environments, and time-based Collective Communications (CC) for resource-limited WSNs. In respect to the code-based CC approach we exploit the principle of superimposed radio transmission to acquire highly scalable and robust communications obtaining with it at the same time as well minimalistic smart RFID labels, that can be manufactured in high volume with present-day OE. The implementation of our code-based CC relies on collaborative and simultaneous transmission of randomly drawn burst sequences encoding the data. Based on the framework of hyper-dimensional computing, statistical laws and the superposition principle of radio waves we obtained the communication of so called ensemble information, meaning the concurrent bulk reading of sensed values, ranges, quality rating, identifiers (IDs), and so on. With 21 transducers on a small-scale reader platform we tested the performance of our approach successfully proving the scalability and reliability. To this end, we implemented our code-based CC mechanism into an all-printable passive RFID label down to the logic gate level, indicating a circuit complexity of about 500 transistors. In respect to time-based CC approach we utilize the superimposed radio transmission to obtain resource-limited WSNs, that can be deployed in wide areas for establishing, e.g., smart environments. In our application scenario for resource-limited WSN, we utilize the superimposed radio transmission to calculate functions of interest, i.e., to accomplish data processing directly on the radio channel. To prove our concept in a case study, we created a WSN with 15 simple nodes measuring the environmental mean temperature. Based on our analysis about the wireless computation error we were able to minimize the stochastic error arbitrarily, and to remove the systematic error completely

The 1982 NASA/ASEE Summer Faculty Fellowship Program

A NASA/ASEE Summer Faculty Fellowship Research Program was conducted to further the professional knowledge of qualified engineering and science faculty members, to stimulate an exchange of ideas between participants and NASA, to enrich and refresh the research and teaching activities of participants' institutions, and to contribute to the research objectives of the NASA Centers

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f