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Electrical Engineering and Computer Sciences
Customized Hardware for Magnetic Resonance Imaging (MRI): Screen-Printed Receiver Coils Arrays
Magnetic Resonance Imaging (MRI) is widely used in clinical settings as a diagnostic tool that offers superior contrast when compared to other imaging techniques. However, the RF receive coils represent a limiting factor on signal to noise ratio (SNR) which translates into notoriously slow scans. Current receive coils are often anatomically unmatched for the human body, heavy, inflexible, restrictive, and poorly tolerated by many patients. We propose the use of printed electronics as a powerful new approach for the design and manufacturing of MRI receive arrays. Printing enables highly flexible, lightweight, and inexpensive devices that conform easily to a patient’s body, much like a tailor-made (bespoke) garment. These printed arrays will address the performance gap where lack of body proximity and conformity is the dominating factor in SNR. Prof. Arias and Prof. Lustig have shown that this technology can be adapted to different body types, presenting excellent in vivo results. In this project we will conduct an in-depth study of the characteristics of printed components, together with SNR characterization of coil arrays tuned to operate in 1.5T and 3T clinical scanners. Our goal is to mature the technology to demonstrate large-area printed multi-channel MRI coil arrays, integrated into clothing which will give MRI access to a much broader human population, specially children.
Ana Claudia Arias is Associate Professor in the Department of Electrical Engineering and Computer Sciences. Prior to UC Berkeley, she was the manager of the Printed Electronic Devices Area at PARC, a Xerox Company. She received her PhD in Physics from the University of Cambridge, UK. Her current research focuses on flexible printed devices and integration that leads to mechanically flexible electronic systems.
Employing Metabolite Protecting Groups for a Sustainable Indigo Dyeing Process
A major challenge for green chemistry is the maintenance of unstable intermediates under the mild conditions that make green chemistry green. One excellent example is the dyeing of blue jeans using indigo, a process that currently depends on hazardous reducing agents to generate and maintain an unstable, soluble intermediate. We propose to develop a biomimetic process that produces a natural indigo precursor by applying an easily-removed, easily-recycled protecting group during biosynthesis. This stable, soluble product eliminates the need for the chemical reducing agents, making the process cleaner and safer. Indigo dye solubility is the first of many green chemistry challenges that we believe will be addressable with this concept of biological protecting groups.
John Dueber is Assistant Professor in the Department of Bioengineering and a Principal Investigator with the Energy Biosciences Institute at UC Berkeley. His research employs protein engineering and synthetic biology approaches to gain designable control over biological systems. He earned his PhD at the University of California, San Francisco, in 2005 and joined the UC Berkeley faculty in 2010 after postdoctoral studies there as a QB3 Distinguished Fellow.
Molecular and Cell Biology
Jamming up Protein Turnover:
Image-Based Modeling for Acute Stroke Diagnosis
Diagnostic imaging, including CT and MRI, has transformed medicine by enabling noninvasive view of tissue inside the body. Using this technology to reliably diagnose cardiovascular problems is hindered by the fact that functional information about blood flow can be difficult to glean from medical images. Moreover, while diagnostic imaging may be useful to spot a problem, it does not provide the predictive power needed to test whether a given intervention may have anticipated benefit. The integration of diagnostic imaging with computational modeling can help address these two critical needs. In regards to ischemic stroke, we aim to develop a quantitative metric derived from image-based blood flow modeling at the time of diagnosis to risk-stratify the patient for a given treatment. The physician can then use this information, along with the patient's clinical data, to make a more informed and accurate treatment decision for that particular patient.
Shawn Shadden is Assistant Professor in the Department of Mechanical Engineering. His research focuses on the advancement of theoretical and computational methods to quantify complex fluid flow. Shawn received his PhD in Control and Dynamical Systems from the California Institute of Technology and his BS from the University of Texas, Austin in Aerospace Engineering.
Electrical Engineering and Computer Sciences
Quantitative Phase Microscopy
This project aims to develop new methods of quantitative phase imaging, for applications in biological microscopy and semiconductor lithography. Phase imaging is a useful contrast mechanism that allows transparent samples to be visualized and quantified in a label-free non-invasive manner. From phase images, we can recover accurate height maps of cells or microfabricated surfaces, with nanometer-level sensitivity and diffraction-limited lateral resolution. Our methods use only a few images captured at various focus settings of a microscope. Thus, they are particularly simple and easy to incorporate into existing microscopes without specialized hardware. We aim to develop software that can interface with existing microscope hardware for adding quantitative phase capabilities to any commercial microscope, with real-time computation and easy automation of focus tracking and image segmentation.
Laura Waller is Assistant Professor in the Department of Electrical Engineering and Computer Sciences. She heads the Computational Imaging Lab, which develops new methods for optical imaging, with optics and computational algorithms designed simultaneously. The specific focus is on measuring and controlling wave effects (such as phase, coherence or nonlinearity) in microscopes and cameras. Laura was a Postdoctoral Research Associate in Electrical Engineering and Lecturer of Physics at Princeton University from 2010-2012 and received her BS, MEng, and PhD degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in 2004, 2005, and 2010, respectively.