KEYWORDS: 3D modeling, Tissue optics, Data modeling, Absorption, Reconstruction algorithms, Finite element methods, Hemodynamics, Brain, Head, 3D image processing
This paper proposes a new reconstruction method for diffuse optical tomography using reduced-order models of light transport in tissue. The models, which directly map optical tissue parameters to optical flux measurements at the detector locations, are derived based on data generated by numerical simulation of a reference model. The reconstruction algorithm based on the reduced-order models is a few orders of magnitude faster than the one based on a finite element approximation on a fine mesh incorporating a priori anatomical information acquired by magnetic resonance imaging. We demonstrate the accuracy and speed of the approach using a phantom experiment and through numerical simulation of brain activation in a rat’s head. The applicability of the approach for real-time monitoring of brain hemodynamics is demonstrated through a hypercapnic experiment. We show that our results agree with the expected physiological changes and with results of a similar experimental study. However, by using our approach, a three-dimensional tomographic reconstruction can be performed in ∼3 s per time point instead of the 1 to 2 h it takes when using the conventional finite element modeling approach.
This paper presents an active source Electromagnetic Induction (EMI) sensor that offers extended detection ranges (>
2m) with minimal sensitivity to magnetic geology. The Ultra Deep Search (ULTRA) EMI system employs a large (20 -
40m), stationary, surface-laid transmitter loop that produces a relatively uniform magnetic field within the search region.
This primary field decays slowly with depth due to the non-dipolar nature of the field within the search volume. An
array of 3-axis receiver cubes measures the time derivative of secondary field decays produced by subsurface metallic
objects. The large-loop transmitter combined with the vector sensing induction coil receivers produces a deep search
capability that remains robust in environments containing highly magnetic soils. In this paper, we assess the general
detection capabilities of the ULTRA system and present data collected over a set of standardized UXO targets.
Additionally, we evaluate the potential for target feature extraction through dipole fit analysis of several data sets.
Because the recovery of underwater munitions is many times more expensive than recovering the same items on dry
land, there is a continuing need to advance marine geophysical characterization methods. To efficiently and reliably
conduct surveying in marine environments, low-noise geophysical sensors are being configured to operate close to the
sea bottom. We describe systems that are deployed from surface vessels via rigid or flexible tow cables or mounted
directly to submersible platforms such as unmanned underwater vehicles. Development and testing of a towed
configuration has led to a 4 meter wide hydrodynamically stable tow wing with an instrumented top-side assembly
mounted on the stern of a surface survey vessel. An integrated positioning system combined with an instrumented cable
management system, vessel and wing attitude and wing depth measurements to provide sub-meter positional accuracy in
up to 25 meter water depths and within 1 to 2 meters of the seafloor. We present the results of data collected during an
instrument validation survey over a series of targets emplaced at measured locations. Performance of the system was
validated through analyses of data collected at varying speeds, headings, and height above the seafloor. Implementation
of the system during live-site operations has demonstrated its capability to survey hundreds of acres of marine or
lacustrine environment. Unique deployment concepts that utilize new miniaturized and very low noise sensors show
promise for expanding the applicability of magnetic sensing at marine sites.
KEYWORDS: Magnetometers, Global Positioning System, Magnetism, Magnetic sensors, Sensors, Data acquisition, Land mines, Standards development, Target detection, Data processing
Detection and discrimination of unexploded ordnance (UXO) in areas of prior conflict is of high importance to the
international community and the United States government. For humanitarian applications, sensors and processing
methods need to be robust, reliable, and easy to train and implement using indigenous UXO removal personnel. This
paper focuses on magnetometer sensing techniques, processing, and operation for UXO detection and discrimination
applications. Specifically, we discuss the collection, processing, and discrimination of data collected using the PACMAG
man-portable system consisting of arrays of sensitive total-field magnetometers, global positioning (GPS)
combined with digital odometers, and a data acquisition system. We outline preliminary standard operating procedures
for optimal collection of UXO-induced magnetic fields and associated position data using either a GPS, or odometer
when surveying in GPS-denied areas. Processing techniques such as gridding and filtering, target picking, and
discrimination lead to estimates of target size and location. Emphasis is placed on simplifying the production of
magnetometer hardware and software for use by minimally-trained personnel with no advanced knowledge of magnetic
sensing and geophysics.
In UXO contaminated sites, there are often cases in which two or more targets are likely close together and
the electromagnetic induction sensors record overlapping signals contributed from each individual target. It is
important to develop inversion techniques that have the ability to recover parameters for each object so that
effective discrimination can be performed. The multi-object inversion problem is numerically challenging because
of the increased number of parameters to be found and because of the additional nonlinearity and non-uniqueness.
An inversion algorithm is easily trapped in a local minimum of the objective function that is being minimized.
To tackle these problems we exploit the fact that, based on an equivalent magnetic dipole model, the measured
electromagnetic induction signals are nonlinear functions of locations and orientations of equivalent dipoles and
linear functions of their polarizations. Based on these conditions, we separate model parameters into nonlinear
parts (source locations and orientations) and linear parts (source polarizations) and proceed sequentially. We
propose a selected multi-start nonlinear procedure to first localize multiple sources and then get the estimated
polarization tensor matrix for each item through a subsequent or a nested linear inverse problem. It follows that
the orientations of the objects are estimated from the computed tensor matrix. The resultant parameter set is
input to a complete nonlinear inversion where all of the dipole parameters are estimated. The overall process can
be automated and thus efficiently carried out both in terms of human interaction and numerical computation
time. We validate the technique using synthetic and field data.
Magnetic soils are a major source of false positives when searching for landmines or unexploded ordnance (UXO) with
electromagnetic induction sensors. The viscosity effects of magnetic soil can be accurately modeled by assuming a
ferrite relaxation with a log-uniform distribution of time constants. The frequency domain response of ferrite soils has a
characteristic negative log-linear in-phase and constant quadrature component. After testing and validating that
assumption, we process frequency domain electromagnetic data collected over UXO buried in a viscous remanent
magnetic host. The first step is to estimate a spatially smooth background magnetic susceptibility model from the
sensor. The response of the magnetically susceptibility background is then subtracted from the sensor data. The
background removed data are then inverted to obtain estimates of the dipole polarization tensor. This technique is
demonstrated for the discrimination of UXO with hand-held Geophex GEM3 data collected at a contaminated site near
Denver, Colorado.
Magnetic soils are a major source of false positives when searching for unexploded ordnance with electromagnetic
induction sensors. In adverse areas up to 30% of identified electromagnetic induction anomalies have been
attributed to geology. In the presence of magnetic soil, sensor movement and surface topography can cause
anomalies in the data that have similar size and shape to those from compact metallic targets. In areas where
the background geological response is small relative to the response of metallic targets, electromagnetic induction
data can be inverted for the dipole polarization tensor. However, spatially correlated noise from the presence
of a geologic background greatly reduces the accuracy of dipole polarization estimates. In this presentation we
examine the effects of sensor movement on the measured EM response of a magnetic background signal. We
demonstrate how sensor position and orientation information can be used to model the background soil response
and improve estimates of a target's dipole polarization tensor.
Magnetic soils are a major source of false positives when searching for landmines or unexploded ordnance (UXO)
with electromagnetic induction sensors. In adverse areas up to 30% of identified electromagnetic (EM) anomalies
are attributed to geology. The main source of the electromagnetic response is the magnetic viscosity of
the ferrimagnetic minerals magnetite and maghaemite. The EM phenomena that give rise to the response of
magnetically viscous soil and metal are fundamentally different. The viscosity effects of magnetic soil can be
accurately modelled by assuming a ferrite relaxation with a log-uniform distribution of time constants. The
EM response of a metallic target is due to eddy currents induced in the target and is a function of the target's
size, shape, conductivity and magnetic susceptibility. In this presentation, we consider different soil compensation
techniques for time domain and frequency domain EM data. For both types of data we exploit the EM
characteristics of viscous remnantly magnetized soil. These techniques will be demonstrated with time domain
and frequency domain data collected on Kaho'olawe Island, Hawaii. A frequency domain technique based on
modeling a negative log-linear in-phase and constant quadrature component was found to be very effective at
suppressing false-alarms due to magnetic soils.
Inversion algorithms for UXO discrimination using magnetometery
have recently been used to achieve very low False Alarm Rates,
with 100% recovery of detected ordnance. When there are many UXO
and/or when the UXO are at significantly different depths, manual
estimation of the initial position and scale for each item, is a
laborious and time-consuming process. In this paper, we utilize the multi-resolution properties of wavelets to automatically estimate both the position and scale of dipole peaks. The Automated Wavelet Detection (AWD) algorithm that we develop consists of four-stages: (i) maxima and minima in the data are followed across multiple scales as we zoom with a continuous wavelet transform; (ii) the decay of the amplitude of each peak with scale is used to estimate the depth to source; (iii) adjacent maxima and minima of comparable depth are joined together to form dipole anomalies; and (iv) the relative positions and amplitudes of the extrema, along with their depths, are used to estimate a dipole model. We demonstrate the application of the AWD algorithm to three datasets with different characteristics. In each case, the method rapidly located the majority of dipole anomalies and produced accurate estimates of dipole parameters.
KEYWORDS: Data modeling, Magnetism, Data acquisition, Electromagnetism, Sensors, Unexploded object detection, Roads, Signal to noise ratio, Algorithm development, Target acquisition
Approximately 75% of buried UXO cleanup costs are expended excavating false alarm anomalies (i.e., digging on the locations of geophysical anomalies that are not caused by UXO). Although probabilities of detection at documented UXO test sites are commonly >90%, there is little documented discrimination capability. This lack of discrimination capability leads to excessively high false alarm rates for both test site and live site surveys. Despite considerable advances in quantitative interpretation methods for discrimination, the state of practice is qualitative or empirical. The UXO thrust of the Army Engineer Research and Development Center's (ERDC) Environmental Quality Technology Program seeks to develop enhanced detection and discrimination capability for survey data from total field magnetometry, time-domain electromagnetic induction, and frequency-domain electromagnetic induction methods. Enhanced discrimination capability by formal geophysical inversion is demonstrated at documented test sites and live sites. A current emphasis is the development of formal inversion procedures that utilize the information content in multiple geophysical datasets. Two approaches are considered: (1) cooperative or constrained inversion; and (2) joint inversion. Cooperative inversion is the process of using inversion parameters from one dataset to constrain the inversion of other data. In true joint inversion, the target model parameters common to the forward models for each type of data are identified and the procedure seeks to recover the model parameters from all the survey data simultaneously. High-quality datasets acquired at seeded test sites at Former Fort Ord, California, demonstrate the confidence in applying these two approaches to discrimination of UXO from non-UXO targets.
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