The Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems

The problem of imaging under a surface arises in a wide variety of contexts, and these problems are among the most difficult and intractable system challenges known. The Subsurface Sensing and Imaging (SSI) challenge is to extract information about a subsurface target from scattered and distorted waves received above the surface.
Imaging techniques, whether ultrasound sensors in tissue or electromagnetic probes in soil, can be described by the properties of the probe wave, the wave propagation characteristics of the medium and the surface, and the nature of the target and probe interaction. The framework describes not only underground imagery, but also underwater imaging, medical imagery inside the body, and 3-D biological microscopy inside a cell or collection of cells. Gordon-CenSSIS research impacts all of these areas.
The fundamental problem of SSI is to differentiate the target of interest from irrelevant clutter and scattered radiation, i.e., to distinguish a landmine from roots, stones, shell-casings, or ground-surface reflections. For example, in pulse-reflection ground-penetrating radar (GPR), the signal from a plastic cylinder could be obscured by the rough-surface reflection above the object. The task is to extract the signal from the complex scattered field of random surface irregularities.
An Integrated Systems Approach
A systems solution is required so that a priori information from the fundamental science of the target phenomenon and its interaction with the subsurface probe can be used to advantage in the processing, imaging, and decision steps that follow. Conversely, the physical probes and sensors can be optimally configured based on processing and recognition criteria. We have created an integrated "end-to-end" approach of the design of next generation SSI systems by teaming multi-disciplinary researchers who have deep knowledge of fundamental science with pragmatic systems engineers. Our three research thrusts (R1, R2, and R3) are engineered to address the barriers at every stage of this "end-to-end" systems approach.
R1: Subsurface Sensing and Modeling explores the physics of promising new non-linear and multi-modal subsurface probes and develops accurate, efficient, wave-based forward modeling algorithms. These models are essential to the understanding of new subsurface probes and form the basis of the R2 inverse methods.
R2: Physics-Based Signal Processing and Image Understanding creates and verifies inverse algorithms to infer subsurface details from measurement of above-surface sensors. This thrust focuses on optimizing the entire detection system, from the sensor data to the information desired, particularly addressing problems where the map from data to decision is non-linear or multi-modal.
R3: Image and Data Information Management develops fast, efficient computational processing and visualization tools as well as means to organize, catalog, and retrieve data sets for algorithm verification.
We test and verify the advances in these research thrusts on fully characterized ground-truth data from our validation testbeds in the biological, medical, underground, and underwater regimes (BioBED, MedBED, SeaBED, and SoilBED). Finally, we apply our methods to real problems of societal significance. We have strong collaborative partnerships with our strategic affiliate institutions (MGH, MSKCC, INL, LLNL, and WHOI) to help us identify significant unsolved biomedical and environmental problems, and we have additional industry and government partners that ensure that our technological innovations are extracted and appropriately applied to the real world.
The Grand Challenge
The Gordon-CenSSIS grand challenge is to use our unifying framework and integrated systems approach to solve important real-world subsurface problems.We aim to achieve critical technical advances that will dramatically improve subsurface imaging for important societal problems. The vehicle for these advances is an Integrated Process for Looking under Surfaces (I PLUS). This engineered system contains the following elements:
A unifying physical and analytical framework to produce optimal solutions to diverse SSI problems;
Validating testbeds that combine unique sensors with innovative modeling and inversion algorithms;
An integration of the sensors, lessons, and tools of subsurface system solutions from a wide range of real-world applications.
Products that we envision emanating from I PLUS include new multi-sensor instruments and capabilities such as a 3-D fusion microscope to observe subcellular reproduction processes, a portable, non-invasive breast scanning device that provides diagnostic readout of incipient cancer development and functions in real time, sea-floor visualization and satellite based coastal ecosystem erosion monitoring, and large-area buried waste mapping.
THRUST R1
SUBSURFACE SENSING & MODELING
Subsurface Sensing & Modeling
Subsurface Sensing and Modeling explores the physics of promising new non-linear and multi-modal subsurface probes and develops accurate, efficient, wave-based forward modeling algorithms. These models are essential to the understanding of new subsurface probes and form the basis of the Thrust 2 inverse methods. The research thrust is divided into two areas:
Nonlinear & Dual Wave Probes investigates the fundamental physical mechanisms involved in subsurface imaging and the development of new imaging modalities. A principal theme of research is the use of multiple interacting probes of either the same physical nature (e.g., electromagnetic waves of different wavelengths), or of different nature (e.g., an acoustic wave and an optical wave). These probes may be independent or coupled through some physical interaction.
Nanoscale Imaging is based on broadband optical interferometry. Entangled-photon Sensing and Imaging is another imaging modality using two optical probe waves. Imaging with two interactive waves of different physical nature has also been pursued; a Gordon Center team (Dual-Wave Methods for Biomedical Imaging: Acousto-Optic and Opto-Acoustic Imaging) uses a diffuse optical wave and an ultrasonic wave, interacting in an optically scattering medium, to obtain enhanced diffuse-optical tomography images as well as images of opto-mechanical properties. Development of High-Resolution THz Imaging Systems aims to use terahertz imaging for non-destructive evaluation of the local properties of semiconductor material.
Effective Forward Models investigates models that can be used to advance fundamental understanding and as tools for engineering design, analysis and optimization. Research addresses the barriers that limit rapid, real-time analysis and inversion and serves as the bridge between Thrust 1 and Thrust 2, generating simulated data for a fixed sensor configuration and serving as an integral part of the reconstruction algorithms in:
ground penetrating radar propagation through rough surfaces, modeling of large collections of weak scatterers such as mitochondria in cells, simulation of the effects of inhomogeneities, rough layers, frequency-dependent dispersive media, and sensor/media coupling (Wave-Based Computational Modeling for Detection of Tumors, Buried Objects and Subcellular Structures).
elasticity imaging of inclusions imbedded in soft tissue (Biomechanical Imaging).
THRUST R2
PHYSICS-BASED SIGNAL PROCESSING & IMAGE UNDERSTANDING
Physics-Based Signal Processing & Image Understanding
Physics-Based Signal Processing and Image Understanding creates and verifies inverse algorithms to infer subsurface details from measurement of above-surface sensors. This thrust focuses on optimizing the entire detection system, from the sensor data to the information desired, particularly addressing problems where the map from data to decision is non-linear or multi-modal. The goal is to identify common mathematical structures and develop general approaches that are applicable across diverse application domains. The work is described under four areas, representing distinct problem classes and information extraction strategies. Each of the areas is developing algorithms that are broadly applicable across different applications in these problem classes. The four areas are summarized below:
Multi-View Tomography (MVT) is concerned with problems where individual sensors capture integrated properties of overlapping areas of the observed subsurface region. These problems arise in many Center applications, ranging from ground penetrating radar subsurface imaging to Electric Impedance Tomography.
Localized Probing and Mosaicing (LPM) is concerned with problems where individual sensor information reflects properties of a highly localized sub-region of the subsurface problem of interest. In these problems determination of the global properties of the material requires registration and fusion of the multiple sources of localized information. Current applications which require LPM strategies involve retinal subsurface imaging, underwater imaging with sidescan sonar and strobe video, and confocal microscopy.
Multi-Spectral Discrimination (MSD) is concerned with problems where sensors collect information on the observed problem of interest across multiple cross-registered spectral bands. Properties of the subsurface volume of interest must be inferred from fusion of the spectral information. Applications that require MSD strategies are skin and brain imaging, underwater quantitative imaging from airborne or satellite based hyperspectral sensors, and multispectral optical biopsy for cancer identification.
Image Understanding and Sensor Fusion (IUSF) aims to extract useful information from the images generated by subsurface inverse problems, such as the underlying object structure contained in a subsurface environment. In many cases, combination of diverse sources of information, obtained at different times and with different modalities, is needed to characterize the structure of the subsurface phenomena under observation. Applications that require IUSF include tumor detection and localization in low-contrast imagery, shape estimation for buried object classification, multi-sensor fusion for coral reef monitoring, multi-modal sensor fusion in breast imaging, and multi-mode microscopy.
THRUST R3
IMAGE & DATA INFORMATION MANAGEMENT
The underlying motivation for work in the R3 thrust area is the recognition that many of the CenSSIS projects and TestBEDs encounter massive datasets, computational barriers and software development challenges which impede the research progress within the Center.
R3 develops scalable, computational tools and resources to enable realistic models to be used for inversion. This thrust is responsible for the development and implementation of efficient sensor data databases, metadata, and multidimensional data search capabilities. R3 also develops software-engineered SSI toolsets.
Researchers have been working with many other Thrust and I-PLUS projects to provide solutions for motion prediction, registration, clustering, and reconstruction, and develop new parallelization techniques to exploit parallel cluster, programmable hardware, and Grids. The efforts in Thrust 3 have been organized into two major areas that address the following challenges:
Parallel Hardware Implementation for Fast Subsurface Detection
The development of scalable computational tools and resources to enable researchers to develop and run more computationally challenging inversion and reconstruction methods. Grid-Enabled High Performance Computational Modeling Applications has created a systematic methodology to parallelize serial algorithm so that scalable computational resources (GRID-level systems) can be effectively exploited. A Toolkit for Implementing Image- Processing Algorithms in Reconfigurable Hardware uses programmable devices (FPGAs) to accelerate elements of image acquisition and registration.
Solutionware
The development and implementation of efficient image data databases, metadata, and time-varying pattern classification capabilities to explore Center datasets in new ways, coupled with the development of software-engineered SSI toolsets. The Image Database System handles the acquisition, storage, indexing, and dissemination of a large variety of image and sensor datasets and applies database technologies to image related problems (e.g., tumor tracking). Solutionware Toolboxes supports three existing subsurface sensing and imaging toolboxes available for download
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S-LEVEL RESEARCH
System Level Applications is the final integration of Center activity that aims to achieve critical technical advances to dramatically improve subsurface imaging for important societal problems.
Current system level projects have biological-medical and civil-environmental applications:
- Application of 3D Fusion Microscopy to Subsurface Imaging of Mouse Oocytes, Embryos, and Embryonic Stem Cells: Non-destructive evaluation of embryo viability that improves the success rate of in vitro fertilization and reduces the number of multiple-birth pregnancies.
- Application of 3D Fusion Microscopy to Skin Cancer Characterization: Imaging of skin cells to detect skin cancer, with the eventual goal of producing a hand-held skin cancer detection device that can be used in clinical settings.
- Four-Dimensional Image-Guided Radiotherapy: Improvement of the effectiveness of cancer therapy through reduction of the time needed to calculate a treatment plan, modeling of respiratory patterns to better direct radiation to the tumor, and development of a toolkit that visualizes CT scans over time.
- Multi-Sensor Breast Cancer Imaging: Integration of multi-sensor instruments and processing schemes to decrease the incidence of unnecessary biopsies for breast cancer and identify cancer not seen using x-ray mammography.
- Multi-Scale Sensing for Benthic Habitat Monitoring: Remote Sensing: Use of sensors and processing algorithms to develop tools that monitor shallow Caribbean reefs and maintain marine health and biodiversity.
- Imaging the Deep Coral Reef Habitat with the SeaBED AUV: Deployment of the Center's Autonomous Unmanned Vehicle to study previously inaccessible deep-water coral reefs, identifying both potential sources of both commercially desirable fish and coral larvae to redevelop the shallow reefs.
- 4-D Multi-Sensor Underground Assessment: Development of techniques to detect site contamination, flaws in civil infrastructure, and unexploded ordnance such as landmines.