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"Seeing the Future with Imaging Science"
November 16-19, 2010

“Indeed, we are ‘seeing’ in so many ways that the enormous amount of data seems to be burying us…visualizing data in easily interpreted images can turn millions of bits of data into information that humans can more readily comprehend. In order to advance science, researchers need to be able to correlate this new information with their present knowledge. For example, color-coding the signal from the different elements detected by electronbeam x-ray microanalysis has been used for many years to reveal the chemical composition of surfaces. Astronomers now use similar methods, superimposing images obtained with telescopes in the x-ray, ultraviolet, visible, and infrared wavelengths to provide more comprehensive and visually comprehensible information about the universe. Participants at the 1999 roundtables warned us about ‘the data problem.’ Nearly a decade later, we have seen progress in the challenge of turning the vast amounts of data into knowledge, but there is much left to do.”
-Thomas E. Everhart
W.M. Keck Foundation 2006 Annual Report Promising DirectionsII: New Eyes

“Seeing the Future with Imaging Science”
Understanding CO2 concentrations, ozone depletion, and many other topics of current concern is advanced, and in some cases made possible by Imaging Science. This list includes changing ocean levels, deforestation, natural hazards, medical diagnostics and therapeutics, national security, surveillance, communication, entertainment, our solar system, and other representations of the universe in which we live. Imaging Science reveals material and processes as small as microscopic particles to scenes of nature and galaxies. Its applications are far reaching – from astronomy to environmental monitoring, education, and healthcare. It can be used to educate, convince, or prove very complicated hypotheses that otherwise would be inexplicable.

We humans are what we are because of our eyes and interpretation of images though our brains. The future is rich, then, for learning how to image what we cannot “see” such as a whale, submarine, or plant’s “view,” potentially to improve diagnosis of disease or national security, develop new materials, or study the environment, for example. Simply put, “You can’t see tomorrow with yesterday’s ‘eyes.’ ”

Because we are so visually oriented, the capability of viewing objects and processes that are normally hidden from us has led to many incredible advances in science and technology that shape our interactions with the world. Medical devices that permit pictures of living organs within the human body save countless lives by minimizing invasive surgery. Quite justifiably, Nobel Prizes in medicine have been given for computerized axial tomography (CT, CAT scans) and, separately, for magnetic resonance imaging (MR, MRI). Visualizing the concentrations of strain in the Earth’s crust has led to a better understanding of earthquakes and volcanoes. The ready availability of video or still images of human activity, manmade and other objects, now available on the Web has had a tremendous impact on how we view ourselves and our neighbors on earth.

As Imaging Science progresses, core intellectual issues common to all applications increase. Some feel there are common languages, but they are generally being addressed in an ad hoc fashion. A NAKFI conference on imaging science will bring together a wide range of scientists, engineers, medical professionals, artists, economists, philosophers, as well individuals from public and private funding institutions and the science media to push Imaging Science forward in a non-linear way. The objectives of the conference are to:

Explore core issues common to all imaging applications: Develop common terminology and taxonomy for, and explore issues related to many matters:
  • New applications for imaging in creative ways
    • The advent of cheap and pervasive computing means that relatively complex imaging systems, for example in inverse problems and reconstructing patterns such as the intensity of a spatial Poisson process. What are the opportunities here?
    • New data sources, from satellites to GPS to cell phone tracking can be applied to “image” human activities in new and expanding ways.
    • Basic science benefits from new ways to visualize and understand the world around us.
    • Emerging threats ranging from plagues to national security can be plotted and studied with increasing ease if their underlying causes can be quantified and displayed.
  • Image processing/recovery
    • There is much shared processing of data that are collected in different modalities.
    • Fusing data from multiple imaging methods/multimodal/hyperspectral, multi-technique imaging. In medicine this can mean combining data from emission tomography and magnetic resonance imaging.
    • Developing non-instrusive imaging
    • Image enhancement, reconstruction, or restoration
    • Use of sparse representations in image representation, enhancement, and processing
    • How does pervasive computing in everything from computers to cell phones present opportunities for image exploitation?
  • Image tracking/recording/distribution/data mining and storage
    • Discuss issues related to searching for, matching and reorienting images. Words or documents can be searched more easily than before, but registering images remains a difficult problem.. 
    • How can a series of images of an ongoing process be analyzed for the time history of objects captured by the images?
    • How will time-variable medical advances, such as functional MRI, affect our ability to identify and treat disease while allowing more comfort for the patient?
      • Advances are available with magnetic resonance mammography and virtual colonoscopy that are likely to change ways in which medicine is practiced.
  • Image representation/display/visualization: “What happens when images and their scientific explanations become dissociated? Do images take on meanings of their own? Are new meanings attributed to them as they are disseminated?” (Weiss PS. What do images mean [editorial]. ACS Nano 2008:2(1);1-2.)
    • Spatial dynamics: (Static and Dynamic) – showing images of “snap shots” of objects vs. showing the changes in objects over time (evolution of animals or the universe) and space (movement of traffic or asteroids). How can complex, spatially-distributed dynamics be represented in a way that’s easily understood?                                                                                                               
    • Real vs. virtual: The difference between using real time images versus virtual imaging and the applications for both. E.g., using real time images to help fix a broken leg vs. a model; viewing the model of a supernova explosion vs. an observation of a real one; or using a graph to show poverty trends vs. showing a starved child becoming well.
    • Modeling and simulation: Modeling and simulation have become the de-facto tools for studying new concepts in engineering and are used in every aspect of design and analysis. In recent years, many modeling and simulation tools have matured significantly. They include dynamics, computational fluid mechanics, and finite element analysis, fitting models using regularization combined with visualization, to mention a few. The focus of this research is on using human modeling and simulation to address issues of safety ergonomics, cognition, fit and function. Such a focus would span breadth and depth of research across multi-disciplines that include medicine, engineering, and the sciences. 
  • Image analysis/understanding: The pace of acquisition is outstripping the ability to analyze, understand and interpret these images. The way in which image acquisition technology characterizes the real world needs to be clarified.
    • Leveraging sophisticated data analysis to improve image formation. Images can be misleading. It can feel like the interpreter is looking at something – seeing it for what it is – when in fact the image is a highly processed slice of data that emphasizes some things and obscures others.
    • How fast or slow certain large-scale image analysis operations can be performed?
    • Challenges associated with amount of images to be interpreted, as well as the quality and authenticity of these images
    • Automated analyses and annotation. What are the opportunities with/barriers to automated analyses and annotation?
    • Infusing graphical representation of images with meaning so that people who see them are led to understanding and intuition is developed about the world that is being viewed.
  • Image evaluation
    • How to measure image quality objectively? This involves subtle questions and attention to what a “gold standard” is.
    • What are the fundamental limits on performance? How does instrumentation affect the quality of images?
  • Policy issues related to Imaging Science research
    • Using images to persuade rather than inform, intentionally or otherwise, when is this acceptable?
Understand where Imaging Science is today and visualize its future
Developing common terminology and taxonomy for – and exploring issues related to imaging science could be conducted in the context of current or future applications such as:
  • Image-guided, non-invasive surgery
  • Targeted drug delivery
  • The integration of imaging technologies with complementary advantages
  • Model-based applications to redefine product process and innovation
    • Imaging science/virtual reality will provide the capability to “try before you buy”
  • Understanding growth and control of plants that may be useful as biofuels, or studying green chemistry at the molecular level
  • Multi-scale biological imaging: multiscale data integration and analysis; statistical modeling and inference in an environment of a small number of observations, typically tens, related to the large number of variables (e.g., genes), typically thousands; machine learning methodologies that can be used effectively for finding biomarkers of untoward phenotype automatically; and hypothesis testing techniques for the automatic generation of biologically relevant hypotheses from multi-scale imaging data.
  • Earth science: Imaging of geologic processes leading to natural hazards can greatly reduce the threat to lives and property from such hazards and earthquakes and volcanoes, among the major causes of loss of life remaining.
  • Military applications that relate to the public’s welfare in terms of national security.
  • How does our knowledge of the universe depend on advanced imaging methodologies? What new telescope systems might lead to insights of the evolution of the universe? How do these studies produce technologies that lead to advances in our actual lives?