Antonios Armaou was born in Athens, Greece, in 1972. He received the Diploma in Chemical Engineering degree from the National Technical University of Athens, Greece, in 1996, and the Ph.D. degree in Chemical Engineering from the University of California at Los Angeles, in 2001. From 2001 to 2002, he held a postdoctoral research position at Princeton University, after which he joined the faculty of the Chemical Engineering department at Pennsylvania State University where he currently is an associate professor and a faculty member to the Operations Research graduate program.

His theoretical research focuses on the development of computationally efficient model reduction, optimization and control methodologies for nonlinear distributed parameter and multiscale process systems. Applications in the areas of advanced materials and semiconductor processing and nanofabrication, as well as biomedical systems (i.e., viral infections and tumor growth) compliment his theoretical interests. He has co-authored more than thirty journal publications, forty five refereed conference proceedings papers and one book on the control and optimization of multiscale process systems.

Dr. Armaou has received the O. Hugo Schuck Best Paper Award given by the American Automatic Control Council in 2000. In 2006 he received the Research Initiation Award (PRF 44300-G9) by the American Chemical Society-Petroleum Research Fund and the CAREER Award (CBET 06-44519) by the National Science Foundation. He has been an Associate Editor to the Conference Editorial Board of the I.E.E.E. Control Systems Society since 2005. He is currently an IEEE and AIChE senior member.

Title: Evaluating Treatment Strategies for Viral Infections and Tumors Using Multiscale Simulation

Abstract: Advances in the current computing capabilities have made it possible to use methods for the systems field to investigate complex processes that were previously computationally intractable. Diverse fields benefit from these advances such as the biomedical field. Examples include the identification of drug targets, the design of new drugs and development of efficient medication strategies.

Recent results in optimization algorithms have, in principle, made it possible to design complex processes that are optimal with respect to certain criteria. An underlying requirement is that a computationally tractable mathematical model capable of describing the dynamic evolution of the process with sufficient detail is available. Treatment of HIV infection is an important challenge in today’s society. Current drugs merely prolong the life expectancy and enhance the quality of life of the patients. An important shortcoming of these drugs is their toxicity and the serious side effects they cause, for instance liver failure. Also, HIV has a significantly high rate of mutation which can lead to production of resistant viruses to drugs which results in reduction of drugs effectiveness. Consequently, quantification of drug toxicity effects, emergence of resistant populations, the probability of infection clearance and the computation of dosage strategies that are optimal with respect to quality of life is of vital importance. Similarly, cancer progression depends on the intricate interplay between biological processes that span the multiple temporal and spatial scales. This complexity urges the development of means to understand the orchestration of these processes for designing better therapeutics. Modeling and Simulation provides a sound approach to integrate information produced by experimental and clinical studies, to generate insights and test “what if” questions that may lead to new hypothesis.

Mathematical models that can capture key aspects of biological processes are usually a combination of macroscopic/continuum models that describe the process behavior at tissue or system level and microscopic/atomistic simulations that describe processes at the cellular or sub-cellular levels. These models are referred to as multiscale process systems and their solution requires very high computational resources. Therefore, computationally-efficient techniques for the computation of high-performance operation and control policies should utilize highly-accurate, yet computationally tractable approximations. Motivated by this, the broad objective of our research is to resolve the fundamental issues associated with computing optimal operation and control policies for complex process systems. In this talk, I will present (a) the development of computationally-efficient algorithms for simulation of such processes and (b) the development of nonlinear, low-order, approximate optimization formulations that can deal with the issues of nonlinearity, model uncertainty and computational constraints. I will finally present applications of the presented methods towards (a) the identification of optimal medication schedules that maximize the probability of HIV infection clearance during the primary stage and (b) simulation of solid tumor progression as the collective behavior of individual cancer cells whose fate is determined by intracellular signaling pathways (i.e., MAPK pathway) that are governed by the temporal-spatial distribution of key biochemical cues (i.e., growth factors and nutrients).

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Prof. Francis J. Doyle III is the Associate Dean for Research in the College of Engineering at UC, Santa Barbara and he is the Associate Director of the Army Institute for Collaborative Biotechnologies. He holds the Duncan and Suzanne Mellichamp Chair in Process Control in the Department of Chemical Engineering, as well as appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering Program. He received his B.S.E. from Princeton (1985), C.P.G.S. from Cambridge (1986), and Ph.D. from Caltech (1991), all in Chemical Engineering. Prior to his appointment at UCSB, he has held faculty appointments at Purdue University and the University of Delaware, and held visiting positions at DuPont, Weyerhaeuser, and Stuttgart University. He is the recipient of several research awards (including the NSF National Young Investigator, ONR Young Investigator, Humboldt Research Fellowship) as well as teaching awards (including the Purdue Potter Award, and the ASEE Ray Fahien Award). He is currently the editor-in-chief of the IEEE Transactions on Control Systems Technology, and holds Associate Editor positions with the Journal of Process Control, the SIAM Journal on Applied Dynamical Systems, and Royal Society’s Interface. In 2005, he was awarded the Computing in Chemical Engineering Award from the American Institute of Chemical Engineers for his innovative work in systems biology. His research interests are in systems biology, network science, modeling and analysis of circadian rhythms, drug delivery for diabetes, model-based control, and control of particulate processes.

Title: The Role of Process Systems Engineering in the Quest for the Artificial Pancreas

Abstract: Type 1 diabetes mellitus is a disease characterized by complete pancreatic beta-cell insufficiency. The only treatment is with subcutaneous or intravenous insulin injections, traditionally administered in an open-loop manner. Patients attempt to mimic normal physiology in order to prevent the complications of hyper- and hypoglycemia (elevated glucose levels, and low glucose levels, respectively). Even minor glucose elevations increase the risk of complications (retinopathy, nephropathy, and peripheral vascular disease). In recent years, sensors and pumps have become available that show great promise for a closed-loop artificial pnacreas -- however the crucial missing component is the algorithm to connect the devices. In order to normalize the glucose levels of insulin dependent, type 1 diabetic patients, the algorithms for the development of an artificial pancreatic islet need to exploit all the measured variables that the normal islet insulin secretion utilizes and quickly increase or decrease the insulin secretory. Our group has been working on model-based control algorithms for pump control over the last 15 years; with clinical evaluations over the last 6 years in collaboration with the Sansum Diabetes Research Institute. Our investigations have addressed the critical algorithmic elements of: model identification, disturbance estimation, model predictive controller design, event detection, monitoring and alarming, and optimization solution. In this talk, we present our most recent computational and clinical results in pursuit of the artificial beta cell. Our novel contributions include the model formulation, meal detection and estimation schemes, efficient programming formulation, and the use of insulin-on-board constraints to ensure safety.

Talk slides (PDF)     Frank Doyle's website     back to top

Prof. Emad S. Ebbini Received his B.Sc. in EE/communications in 1985 from the University of Jordan, and his M.S. and Ph.D. in EE from the University of Illinois at Urbana-Champaign in 1987 and 1990. From 1990 until 1998, he was on the faculty of the EECS department at the University of Michigan Ann Arbor. Since 1998, he has been with the ECE department at the University of Minnesota, where he is currently a Professor. In 1993, he received the NSF Young Investigator Award for his work on new ultrasound phased arrays for imaging and therapy. He was a member of AdCom for the IEEE Ultrasonics, Ferroelectrics, and Frequency Control (1994 – 97). He was the Guest Editor for a special issue on therapeutic ultrasound in the IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (November 1996). He was an Associate Editor of the IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (1997 – 2002). He is currently an Associate Editor of the IEEE Transactions on Biomedical Engineering. He served as the Guest Editor for a combined special issue on therapeutic ultrasound for the IEEE Transactions on Biomedical Engineering and the IEEE Transactions on Biomedical Engineering Letters (January 2010).

Dr. Ebbini is a member of the Standing Technical Program Committee for the IEEE Ultrasonics Symposium and a member of the Board of the International Society for Therapeutic Ultrasound. His research interests are in signal and array processing with applications to biomedical ultrasonics and medical devices.

Title: realtime control of multiple-focus phased array heating patterns based on noninvasive ultrasound thermography

Abstract: High intensity focused ultrasound (HIFU) is a promising modality for the treatment of cancer and other tissue abnormalities. It has proven to be effective in the treatment of prostate cancer, uterine fibroids, and other tumors in accessible organs. Phased array applicators offer unparalleled level of spatial and temporal control over the heating pattern, including simultaneous heating of at multiple-focus locations. This has many potential advantages in thermal therapy, including reduction in treatment time, improved localization of therapeutic effects to the target volume, compensating for heterogeneous blood perfusion, etc. Modern phased array drivers are capable of dynamic control of heating patterns using a variety of man-machine interfaces with millisecond resolution. Realtime temperature control algorithms with spatial and temporal resolutions matching those of the drivers are needed to realize the full potential of phased array technology in thermal therapy. Furthermore, to preserve the noninvasive nature of the treatment, the algorithm must utilize a noninvasive method for measuring temperature change within the treatment volume.

We have developed a noninvasive method for imaging temperature change using 2D diagnostic ultrasound. A realtime implementation of the method was recently completed on a Sonix RP scanner (Ultrasonix, Vancouver, BC, Canada). The temperature imaging system was integrated with a 64-channel phased array driver allowing for the selection of multiple temperature control points within the treatment volume (as seen on the B-mode realtime images). Control signals are issued to the driver to maintain the desired temperature levels at these control points by realtime synthesis of multiple-focus patterns. An optimal PID control algorithm is implemented simultaneously on all selected control points. Thermocouple measurements were performed in the vicinity of the control points to verify the performance of the temperature control algorithm. Tests were performed in tissue-mimicking phantoms and in freshly excised porcine liver tissue samples. The realtime operation of the 2D noninvasive temperature measurement, control algorithm, and inverse field computation for the synthesis of the array field patterns were performed on a high-performance GPU platform. Temperature profiles measured using our noninvasive 2D imaging method were in agreement with direct measurements performed using thermocouples. The PID controller was able to control the tissue temperature to within 0.2oC from the set point with sub-second response time at multiple points. The algorithm was stable and voltage-current profiles from the array driver were smooth under a variety of conditions, indicating robust operation. The operation of the algorithm closely mimics its envisioned implementation in a clinical setting, i.e. user (physician) control through man-machine interface in true realtime.

In this talk, we give a description of an ultrasound phased array system for image-guided thermal therapy applications with illustrative examples of clinically-relevant multiple-focus heating patterns. We also describe our multi-point (multiple-focus) control algorithm with emphasis on special requirements for multiple-focus heating patterns. In particular, we describe our implementation of anti-windup compensation and dynamic power reallocation among different focal points upon reaching each set point. Examples of long-exposure (used in hyperthermia) and short-exposure (used in ablative therapy) multiple-focus patterns will be given and their clinical applications will be discussed.

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Levi Hargrove received his B. Sc. and PhD in Electrical Engineering from the University of New Brunswick in Fredericton, New Brunswick, Canada in 2003 and 2008 respectively. Dr. Hargrove's research focuses on developing clinically robust pattern recognition myoelectric control systems for both upper and lower limb amputees. Dr. Hargrove joined Todd Kuiken's the Neural Engineering Center for Artificial Limbs at the Rehabilitation Institute of Chicago in 2008 to lead a US Army Telemedicine and Advanced Technology Center (TATRC) sponsored research project to develop a neural interface for powered lower limb prosthetics. In addition to being an Assistant Research Professor in the Department of Physical Medicine and Rehabilitation at Northwestern University's Feinberg School of Medicine, Levi is a member of the IEEE and the Association of Professional Engineers and Geoscientist's of New Brunswick.

Title: Towards clinically viable neural control of powered arms and legs

Abstract: Current powered prostheses do not meet the functional requirements of patients with high-level amputation who need to perform multi-joint coordinated motions. The surface electromyographic (EMG) signal has been used as a neural control source for many years and it provides acceptable control in simple one degree of freedom systems. As the level of amputation increases, the functional requirements to be restored are greater but there are fewer muscle sites from which the EMG signals can be measured. Targeted muscle reinnervation is a surgical technique which re-routes residual nerves to muscle which is no longer biomechanically functional as a result an amputation. This provides a natural and intuitive mechanism for accessing neural information and we have successfully used the resulting neural signals to provide improved prosthesis control to our patients. This talk will highlight clinical experiences resulting from targeted muscle reinnervation with pattern recognition decoder algorithms for upper limb amputees. Additionally, preliminary results for non-weight bearing independent control of powered leg systems will be presented.

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Dr. Kazerooni holds a Doctorate in Mechanical Engineering from MIT and is currently a Professor in the Mechanical Engineering Department at the University of California, Berkeley. Dr. Kazerooni is the director of the Berkeley Robotics and Human Engineering Laboratory. He has published over 180 articles on Robotics, Control Sciences, Artificial Locomotion, Assist Devices and Mechatronics. He is the holder of twenty pertinent patents where most of them have been licensed. Dr. Kazerooni has served in a variety of leadership roles in the robotics community; served as associated editor of two journals: ASME Journal of Dynamics Systems and Control and IEEE Transaction on Mechatronics. Dr. Kazerooni was the recipient of the outstanding ASME Investigator Award, Discover Magazine Technological Innovation Award, and the McKnight-Land Grant Professorship. His research was recognized as the most innovative technology of the year in New York Times Magazine; December 2004. Dr. Kazerooni is also the founder and CTO of Berkeley Bionics which designs and manufactures lower extremity exoskeletons to augment human strength and endurance during locomotion.

Title: Lower Extremity Exoskeleton Systems for Medical Applications

Abstract: Berkeley Robotics and Human Engineering Laboratory at UC, Berkeley is the birthplace of the exoskeleton systems being adopted by Lockheed Martin. During the last 20 years, this laboratory has been devoted to uncovering all basic issues associated with the control, design and power of exoskeleton systems. The adoption of exoskeletons by Lockheed Martin for DOD applications is just a beginning of a much larger bionics field especially in the medical field. Patients who have difficulty walking often use wheelchairs for mobility. It is a common and well-respected opinion in the field that postponing the use of wheelchairs retards the onset of other types of secondary disabilities and diseases. The ramifications of long-term wheelchair use are secondary injuries including: hip, knee, and ankle contractures; heterotopic ossification of lower extremity joints; frequent urinary tract infection; spasticity; and reduced heart and circulatory function. The objective of our research is to develop smart, powered exoskeleton orthotic systems to be used for individuals with otherwise limited mobility. These exoskeletons are powered and allow their wearers to walk upright without the energetic drain associated with existing orthotic devices. These smart exoskeletons will replace wheelchairs and enable many individuals who cannot walk due to neurological disorders, muscular disorders or aging to walk again.

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Dr. Koopman started his career as a pediatrician in 1969, then joined the epidemic intelligence service and became acting state epidemiologist in the State of Washington and then a smallpox eradicator in northern India. That was followed by an MPH in epidemiology and several years of work in Latin America before joining the faculty at the University of Michigan in 1978. From 1984-6 he set up an epidemiology program in Mexico under the auspices of the US CDC and thereafter changed careers from a field epidemiologist to a mathematical modeler and theoretician. His theoretical focus has been on how dynamic system analysis which takes contact patterns into account can be used to make robust inferences that serve Public Health. His currently funded work includes 1) the analysis of modes of transmission for influenza and methicillin resistant Staph aureus (MRSA) in hospitals, 2) developing methods to use genetic sequences of infectious agents to analyze their transmission systems, 3) determining how much HIV transmission occurs before people become HIV positive and the aspects of contact patterns that increase that, and 4) why polio is not being eradicated in northern India.

Title: Complex Dynamics of Polio Transmission and Immunity and the Failure of High Vaccination Rates to Eliminate Transmission in Northern India

Abstract: The polio eradication program thought that it could be successful without a detailed analysis of the dynamics involved. Empirical observations based on a mental model of how vaccination worked at the population level were thought to be enough to guide the program to success. The smallpox eradication program provided a successful model in this regard. The grand successes in the initial years of the eradication effort reinforced this idea. Vaccinating only children under age 5 proved successful in most of the world. Then problems emerged in northern India as very high levels of vaccination failed to eliminate transmission. This failure was analyzed with standard risk based epidemiologic analyses that indicated something was interfering with vaccine effectiveness in northern India. The inference was made that this was other enteric infections. The inferred solution was to increase vaccination rates of children under 5 even further. But that inference is based on a flawed understanding of poliovirus transmission dynamics and poliovirus immunity dynamics. Our analysis indicates that most likely it is going to take vaccination of older individuals with waned immunity in order to bring poliovirus transmission to an end. A new generation of epidemiologists who understand how to analyze dynamic systems is needed to face this and a series of other issues where we have great opportunities to control infection transmission but an inadequate understanding of the underlying systems.

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Jr-Shin Li received his BS and MS degrees from National Taiwan University, and his PhD degree in Applied Mathematics from Harvard University in 2006. He is currently an Assistant Professor in Electrical and Systems Engineering with joint appointment in the Division of Biology and Biomedical Sciences at Washington University in St. Louis. His research interests are in areas of control theory, optimization, and computational mathematics with applications ranging from quantum mechanics and neuroscience to bioinformatics. He is a recipient of the NSF Career Award in 2007 as well as the AFOSR Young Investigator Award in 2009.

Title: Ensemble Control and Computation: From Quantum Mechanics to Neuroscience

Abstract: Many applications in control of biological and quantum mechanical systems involve manipulating a family of dynamical systems with the same open-loop control signal since the state feedback is either difficult or impossible to obtain. Typical applications include the design of pulse sequences in Nuclear Magnetic Resonance (NMR) spectroscopy and imaging (MRI) and the development of external stimuli in neurological treatment such as Parkinson's disease and epilepsy. These control designs motivate a new class of control problems called Ensemble Control. In this talk, I will introduce the notion of ensemble controllability and present a unified computational method based on pseudospectral approximations for solving optimal control problems arising from the area of ensemble control. The optimal pulse sequences for protein NMR spectroscopy and MRI derived by our analytical and numerical methods will be presented.

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Ann Rundell is an assistant professor in the Weldon School of Biomedical Engineering at Purdue University. She received her BS in Electrical Engineering from the University of Pennsylvania. Prior to graduate school, Ann worked for three years at Artel, Inc. in Windham, Maine as an engineer designing small portable photometric instrumentation systems for the clinical and environmental marketplaces. Eventually Ann returned to school to earn her MS and PhD degrees from the School of Electrical and Computer Engineering at Purdue University. Her graduate research was on modeling and control of the immune system. Upon completion of her PhD she worked at MIT Lincoln Laboratory as a member of the Technical Staff for three years prior to joining academia as a faculty member. Her research interests apply systems and control theory to control cellular and physiological processes for developing and designing diagnostics and therapeutics. She has co-authored more than 20 peer reviewed articles, is a senior member in IEEE, serves as a Section Editor for the Encyclopedia of Systems Biology, and recently received the NSF CAREER award.

Title: Quantitative Experiment Design for Highly Uncertain Biological Systems

Abstract: Many biological systems are highly uncertain thus their descriptive mathematical models have structures that are not fully defined by underlying physical and chemical principles and have parameters that are not well constrained by existing data. Experiments to resolve the biological system behaviors and their associated mathematical model are expensive, so it is vital to design experiments that will be nearly optimal among available experiments in terms of constraining the model structure and parameters the most. Our sequential experiment design approach addresses these issues by using sparse grid interpolation to identify multiple areas of parameter space that are consistent with available data and clustering these identified parameters based on simulated model response and the limits of experimental measurement. By analyzing the expected experimental variance and the variance due to different model responses, we choose a measurement to provide maximal discrimination among currently acceptable solutions. This experiment design criterion is similar to the Hunter-Reiner criterion since it looks for the largest difference in predicted dynamics, but it also avoids design points with large expected measurement error as recommended by Buzzi-Ferraris & Forzatti. This approach further differs from other experiment design methods in that it simultaneously addresses both parameter- and structural- based uncertainty, is applicable to some ill-posed problems where the number of uncertain parameters exceeds the amount of data, places very few requirements on the model type, available data, and a priori parameter estimates, and is performed over the global uncertain parameter space. We illustrate this approach on models of the mitogen-activated protein kinase cascade, one with 3 uncertain parameters and one with 18 uncertain parameters. The results show that system dynamics are highly uncertain with an initial set of limited experimental data. Nevertheless, the algorithm requires only three additional experimental data points to simultaneously discriminate between possible model structures and acceptable parameter values. This sparse grid-based experiment design process provides a systematic and computationally efficient exploration over the entire uncertain parameter space of potential model structures to resolve the uncertainty in the nonlinear systems biology model dynamics.

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Sridevi V. Sarma (M’04) received the B.S. degree in electrical engineering from Cornell University, Ithaca NY, in 1994; and an M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in, Cambridge MA, in 1997 and 2006, respectively. She was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge, from 2006-2009. She is now an assistant professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University, Baltimore MD. Her research interests include modeling, estimation and control of neural systems. She is a recipient of the GE faculty for the future scholarship, a National Science Foundation graduate research fellow, a L’Oreal For Women in Science fellow, and a recipient of the Burroughs Wellcome Fund Careers at the Scientific Interface Award.

Title: To Cue or Not To Cue: Reframing the Question for Parkinson's

Abstract: Cues are believed to modulate motor function in Parkinson’s disease (PD) through activation of corticostriatal pathways that suppresses pathological basal ganglia activity such as 10-30 Hz beta oscillations. We set out to evaluate the association between external cues and downstream neuromodulatory effects in basal ganglia in PD patients. We compared the effects of anticipated and unanticipated cues upon behavior and subthalamic nucleus (STN) neurophysiology in seven PD patients executing a center-out task. In anticipated trials, patients knew the “go cue” would appear at the start of the trial, and moved in response to the visual cue. In unanticipated trials the “go cue” appeared 50% of the time, and were designated visually-guided when presented and self-initiated otherwise. At the start of each unanticipated trial, patients could not anticipate the cue and therefore could not plan for a specific type of movement.

We analyzed spiking activity in the STN by constructing point process models from spike trains generated by each neuron . A point process model describes the spiking propensity of a given neuron as a function of extrinsic factors (eg. cues, movement), intrinsic factors (eg. neuron’s spiking history, local activity), and time. Using our models, we then computed the percentage of STN neurons that exhibit, in a statistically significant sense, bursting, 10-30 Hz beta oscillations, and directional tuning for each trial type.

We found that during anticipated (+cue) trials and self-initiated trials (-cue), neuromodulation occurred: beta oscillations decreased while directional tuning increased in the STN, with decreased reaction and movement times. During visually-guided (+cue) trials, the beta oscillations and directional tuning in the STN did not modulate and behavior times increased. These results suggest that external cues may not be critical to motor facilitation in PD. Rather the activation of previously formulated motor plans, via either external or internal cues, may be the mechanism in “cue-related” motor modulation. Interestingly, physiological modulation occurred earlier during the anticipated (+cue) trials versus self-initiated trials, supporting this viewpoint.

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Milan Sonka received his Ph.D. degree in 1983 from the Czech Technical University in Prague, Czech Republic. He is Professor and Chair of the Department of Electrical & Computer Engineering, Professor of Ophthalmology & Visual Sciences, and Radiation Oncology at the University of Iowa, Co-director of Iowa Institute for Biomedical Imaging, IEEE Fellow, and AIMBE Fellow. His research interests include medical imaging and knowledge-based image analysis with emphasis on cardiovascular, pulmonary, orthopedic, and ophthalmic image analysis. He is the first author of 3 editions of Image Processing, Analysis and Machine Vision book (1993, 1998, 2008) and co-authored or co-edited 18 books/proceedings. He has published more than 80 journal papers and over 340 other publications. He is Editor in Chief of the IEEE Transactions on Medical Imaging, member of the Editorial Board of the Medical Image Analysis journals. Dr. Sonka is co-founder of VIDA Diagnostics, a start-up company developing novel image-analysis tools for comprehensive quantitative assessment of pulmonary morphology and function and co-founder of Medical Imaging Applications, LLC, a company that develops and markets cardiovascular ultrasound image analysis research and clinical-care software.

Title: Multi-object Multi-surface Optimal Graph Segmentation

Abstract: Accurate and reliable image segmentation is of paramount importance in medical image analysis. With a widespread use of 3D/4D imaging modalities like MR, MDCT, ultrasound, or OCT in routine clinical practice, physicians are faced with ever-increasing amounts of image data to analyze and quantitative outcomes of such analyses are increasingly important. Yet, daily interpretation of clinical images is still typically performed visually and qualitatively, with quantitative analysis being an exception rather than the norm. Since performing organ/object segmentations in 3D or 4D is infeasible for a human observer in clinical setting due to the time constraints, quantitative and highly automated analysis methods must be developed. Utilizing contextual information of mutually related surfaces and objects is hypothesized to increase segmentation robustness.

A novel method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects will be presented. The reported method is an extension of our method for multi-surface graph based image segmentation that guarantees solution optimality and is directly applicable to n-D problems. While the method is generally applicable to a multitude of image segmentation problems, its utility and performance will be demonstrated on a knee-joint bone and cartilage segmentation task. The framework consists of the following main steps: 1) Shape model construction, 2) Pre-segmentation, 3) Cross-object surface mapping, 4) Multi-object, multi-surface graph construction, and 5) final segmentation and quantitative analysis. The functionality and performance of our method will be demonstrated in 3-D MR images of the knee joint originating from the NIH-supported Osteoarthritis Initiative Study. Additional applications of the methods to cardiovascular MR, pulmonary CT and ophthalmic OCT images will be presented demonstrating the broad applicability of the developed algorithmic concepts.

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Kai E Thomenius received his PhD degree from Rutgers University in Electrical Engineering and Physiology. He is currently a Chief Technologist in the Imaging Technologies Organization of GE Global Research in Niskayuna, NY. He is also an Adjunct Professor in the Electrical and Computer Science and Engineering department at Rensselaer Polytechnic Institute in Troy, NY. Prior to joining GE, he has worked for several medical ultrasound companies in senior R&D roles and holds about two dozen patents for medical ultrasound scanners. Dr. Thomenius' current activities focus on development of instrumentation and applications for newly evolving imagers such as ultrasound scanners and other forms of bioinstrumentation. He is a Fellow of the American Institute of Ultrasound in Medicine and has received the Coolidge Fellowship of the GE Global Research.

Title: New Applications for Medical Ultrasound Scanners

Abstract: Perhaps more than other imaging modalities, medical ultrasound, in its 40-year history, has shown an unusual amount of dynamism and adaptability in the face of the changing technological foundations of the instruments. Much of this is related to the basic architecture of these scanners; their operation is largely signal/image processing based and does not require substantial hardware such as gantries for its realization. Another associated benefit is the general trend of miniaturization in semiconductor electronics. In response, ultrasound scanners have steadily grown to smaller sizes without loss of performance and are now on the verge of reaching a dramatically larger clinical user base than previously possible.

This state of affairs introduces two related fields of research for medical ultrasound: new applications required by the broader clinical user base, and new clinical tasks given by the increasing role of computing in both the data acquisition and processing of the acoustic information. This discussion will review both of these areas from the point of view of the researcher with the goal of identifying key areas requiring additional investigation. In the area of new clinical research, the relatively low cost and high degree of portability of the scanners that are becoming available are opening up many new clinical research fronts. I will review several of these with discussions of some of their inherent challenges. On the technical side, the realization of software beamformation is opening up a whole new and very exciting front for systems development. The traditional approach of delay-and-sum beamformation is likely to recede in importance and be replaced by more image reconstruction type algorithms. Some of these opportunities will be discussed in greater detail.

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Mathukumalli Vidyasagar received the B.S., M.S. and Ph.D. degrees in electrical engineering from the University of Wisconsin in Madison, in 1965, 1967 and 1969 respectively. Between 1969 and 1989, he was a Professor of Electrical Engineering at various universities in the USA and Canada. This concluded with the University of Waterloo, ON, Canada, where he served between 1980 and 1989. In 1989 he returned to India as the Director of the newly created Centre for Artificial Intelligence and Robotics (CAIR) in Bangalore, under the Ministry of Defence, Government of India. Between 1989 and 2000, he built up CAIR into a leading research laboratory with about 40 scientists and a total of about 85 persons, working in areas such as flight control, robotics, neural networks, and image processing. In 2000 he moved to the Indian private sector as an Executive Vice President of India's largest software company, Tata Consultancy Services. In the city of Hyderabad, he created the Advanced Technology Center, an industrial R&D laboratory of around 80 engineers, working in areas such as computational biology, quantitative finance, e-security, identity management, and open source software to support Indian languages. In 2009 he retired from TCS and joined the Erik Jonsson School of Engineering & Computer Science at the University of Texas at Dallas, as a Cecil & Ida Green Professor of Systems Biology Science. In his latest incarnation, he conducts teaching and research in two distinct areas: computational biology and quantitative finance. He is now the Head of the newly created Bio-Engineering Department at UT Dallas.

Title: Predicting adverse events in clinical trials: A nonstandard problem in statistical learning?

Talk slides (PDF)     Mathukumalli Vidyasagar's website     back to top