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and (2) the appropriate electrical characteristics of the brain media can be estimated. To
accomplish this in a timely manner will require an automated data segmentation procedure that
can accurately extract boundaries of the five different media which comprises the brain. The
multiple MRI data segmentation tasks will be accomplished through a static decomposition on a
parallel supercomputer for high parallel efficiency. Necessary electrical properties of the various
brain media will be extracted directly from the MRI data. Once the boundaries and electrical
characteristics have been determined a 3-D finite element mesh, the brain model, will be
generated. Within the brain model one can now place a localized current source. The extracranial
magnetic fields generated by this current are then computed using the parallel supercomputer and
used to guide the beamsteering algorithm to a specific site within the actual brain (MEG) data
space. By moving the current source around in the brain model one can localize, i.e., map the
actual brain source currents contained within the MEG data.
We will undertake analysis and computer simulations to assess the usefulness of an MEG sensor
array as a noninvasive probe of brain activity. Such a probe must take accurate measurements
over time of currents within a small, localized portion of the brain.
Interfering signals from other
parts of the brain must be suppressed, along with signals from sources outside the head such as
RF interference from electronic equipment. The effect of random noise within the MEG sensors
must also be kept low.
Given a MEG based imaging system, the key to accurate localization of brain activity then
depends upon the signal processing techniques implemented. The signal from the desired region
appears at each of the MEG sensors with a strength that depends on its location within the brain.
The signal processing algorithm must combine the sensor outputs in such a way as to reproduce
the desired signal while suppressing noise and interference.
It relies on the forward model of
sensor gain vs. location in making the combination. The model is adjusted to an individual patient
using the same auxiliary data used in finding the head shape, to create a "brain atlas" showing the
region of possible signal sources. To avoid the high cost of human interpretation,
the atlas must
be created by an automatic process involving segmentation of the auxiliary images to identify
regions composed of tissues of different types. This is a classic computing problem for which a
variety of promising approaches exists. Guild members from the University of New Mexico will
develop image segmentation methods specific to the MEG problem. These will be combined with
the computational electromagnetics codes to create a system capable of solving the forward
modeling problem rapidly.
Given a forward model, the "inverse" problem of reconstructing the brain activity from the MEG
sensor outputs is solved by computing an ensemble of weighted linear combinations of the sensor
outputs. Each combination is an estimate of the activity vs. time in a small region of the brain,
and its weights are chosen to select activity in that region while suppressing activity elsewhere in
the brain and interference from outside the head. The problem of choosing the weights is
analogous to the problem of beamsteering in array antennas.
Its solution is aided by the fact that
over short periods of time, only a small fraction of the region of signal sources is active.
This
makes possible the use of concepts from the theory of adaptive arrays, in which the location of
interfering sources is estimated from the data and the array weights chosen to direct antenna
pattern nulls at the interfering sources. Techniques of this kind have been used successfully to
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enhance data from existing MEG systems. Guild members from Sandia, the Veteran's
Administration, and other laboratories will develop algorithms for the inverse problem on large
arrays, using brain data to refine the reconstruction model. These codes, combined with the
forward solution codes, will make up the prototype of an end-to-end capability to compute maps
of brain activity vs. space and time from MEG and auxiliary input data.
As the algorithms are developed, they will be incorporated into analyses of the performance of
candidate MEG sensor configurations.
Simplified models will be used to identify the most
promising candidates, then detailed simulations will be used to choose the configuration preferred
for full-scale development.
A. Hardware System Development
The proposed design approach will work from the bottom up and from the top down
simultaneously.
Clinical information obtained from the Phase 1 study using existing 37 channel
MEG machines and improved source reconstruction algorithms will be used to predict the
minimum sensor density necessary to detect various brain functions and to diagnose various
disorders. While this is underway, a second study will determine the maximum number of brain
sensors which may be supported by a fixed level of supercomputing support. Although the
optimal number of brain sensor channels will be determined from these studies during the first
phase, we anticipate the optimum number will be about one thousand. These studies will consider
two different operating scenarios; one which provides near realtime information with no more
than a 5 minute delay, and the other which accumulates data on the evoked cortical response due
to repeated stimulation. The first mode of operation, which will be genuinely new for MEG,
should prove useful in rapid mass-screening for particular disorders.
It should also prove useful
during useful during certain surgical techniques. The second scenario will expand the capabilities
of previous MEG machines, providing greater utility to drug action studies and to the brain's
functional mapping. Although these initial designs are optimized for brain research, the design
will remain flexible so that it may be re-deployed for other research, such as heart and fetal
studies.
B. Machine Design Issues
Today, commercially available MEG machines are limited to a maximum of 37 channels.
Canadian Thin Films, Inc. plan to introduce a 90 channel system soon, and Neuro Mag, Ltd. of
Helsinki, Finland have recently reported their successful testing of a 122 channel system.
Japan's
MITI are attempting to develop a similar machine with at least one hundred channels designed for
full-cortex coverage.
Considering the current state-of-the-art it is apparent that this proposed
1,000 channel MEG system will represent a dramatic increase in the overall system complexity.
As discussed below, this increase in system complexity will mandate a genuinely new system
architecture which permits a much more dense packing of sensor channels, and which utilizes
novel new cryogenic support designs which will permit low-noise operation in any orientation of
the MEG sensor array. As such, we propose a number of risk reduction experiments during the
first phase designed to define the optimal approach for increasing the density of sensor channels in
the proposed super-MEG machine. Two different technical approaches will be pursued to
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optimize the SQUID and sensor configuration initially. The first, lower-risk approach will
determine the optimal method of close-packing conventional thin-film analog SQUID amplifiers
and their associated micro-lithographic sensor coils at the density required in this super-MEG
machine. The second approach, entailing higher risk and a much higher potential benefit, will
pursue technical improvement of a new class of digitally biased SQUIDs which have only recently
been developed, primarily by Fujitsu in Japan. We anticipate that this second approach will
rapidly close our existing technical deficit, relative to Japan, in this emerging new SQUID
technology, which should prove useful in many civilian and military applications, as well as in the
proposed MEG work.
After 15 months, the optimal SQUID and sensor technology will be
selected and integrated into the overall super-MEG system design. These risk reduction
experiments, together with others related to the system's cryogenic and thermomechanical design,
are described below.
C. SQUID Risk Reduction Studies
As more sensors and SQUID amplifiers are packed into a limited surface coverage about the
human cortex, problems associated with channel cross-talk and bias/feedback sub-system
interference become a major limitation to continued system expansion.
Recently Fujitsu has
begun an extensive program, coordinated through MITI, focused on the development of digitally-
biased SQUIDs in which all feedback and readout functions are facilitated on the same integrated
circuit as the SQUID and sensor coil. Although this technology could solve many of the high-
density packing problems mentioned above, overall noise level and other system integration issues
greatly limit its utility at this time. Improvement of this technology for incorporation into the
super-MEG system is considered high-risk, however, the remaining technical developments
necessary to make this technology viable seem readily achievable. A series of risk reduction
experiments designed to utilize these new SQUID devices will be pursued, together with less risky
experiments which use conventional analog SQUIDs with room temperature biasing and feedback
electronics. At the end of these experiments, and at the culmination of the first phase of this
proposed research, we anticipate that the final super-MEG system design will utilize an optimized
hybrid of these two SQUID design concepts.
Numerous domestic manufacturers of thin-film SQUIDs are capable of producing the channels
which utilize conventional, analog SQUID technology. Firms such as IBM, Quantum Design,
Conductus, BTi, Hypres, and possibly others would be potential contractors for this aspect of the
work.
In order to minimize the SQUID cross-talk at our required high density of sensor packing,
we will issue a contract to at least one of these firms (during the first phase) to produce a
miniature fifty-SQUID sensor array at this packing density. This will permit the materials and
micro-lithographic designs to be optimized to minimize cross-talk and feedback channel
interference to the incoherent noise level. These small contracts executed during the first phase
will greatly reduce the technical and business risks associated with the super-MEG machine
development during the second phase.
The higher-risk digital SQUID technology will also be pursued aggressively during the first phase.
TRW owns the basic digitally-biased SQUID patents within the United States, and as such we will
attempt to team with them in all such efforts.
Fujitsu, with extensive funds from Japan s MITI,
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have taken the world lead in digital SQUID technology.
Fortunately, Fujitsu has been very open
with this technology through numerous publications.
Fujitsu's scientists seem willing to
collaborate with their USA colleagues, so we propose to place a physicist at Fujitsu's
development laboratories to work with their lead physicist. Dr. H. Fujimoto, during the first phase
of this development. We know of two physicists, both fluent in Japanese and USA citizens, who
may be interested in this position at Fujitsu through a contract with their current employers. This
physicist may act as a technical liaison, conveying technical information from the MITI
laboratories in Japan to firms in the USA such as Sandia and TRW. Once the basic self-contained
digital SQUID sensor has been developed, we will study the way in which they may be close-
packed, in a manner similar to the study with analog SQUIDs described above.
D. System Integration Developments
The first two sets of risk reduction studies outlined above involve the optimization of the basic
SQUID and sensor design.
In essence, this aspect of the system development centers on
optimizing the basic "building block". System integration and cryogenic support studies will also
be conducted during the first phase in order to determine the optimal method of thermo-
mechanical support of the very large array of sensors in the super-MEG machine.
Recent advances in closed-cycle cryogenic refrigeration technology have resulted in dramatically
longer intervals of highly reliable continuous operation (typically 20,000 hours), and in astounding
cost reductions, both in purchase price and operating costs. New cryogenic designs which
integrate the SQUID and sensor array into a plenum structure which serves as the closed-cycle
refrigerator’s lowest temperature heat exchanger will permit operation of the super-MEG machine
in any orientation. This will greatly increase the clinical utility of these machines.
Present day MEG machines locate the fifty liters of liquid helium, which maintain the SQUIDs and
sensors in their superconductive state, directly over the patient's head. This primitive cryogenic
design, which Newsweek referred to as the "Hair dryer from Hell" in their April 20, 1992 issue,
uses gravity to stabilize the liquid helium under its vapor throughout the clinical measurements.
Any repositioning of the apparatus above the patient's head requires a cumbersome relocation of
this large apparatus on a hefty overhead supporting transit system. Such motion inevitably creates
a sudden increase in the liquid helium boil-off rate, resulting in a loud hissing sound as the cold
helium vapor escapes from the system's check valves. This process is disruptive even to healthy
humans, and it is often absolutely terrifying to patients afflicted with major psychiatric disorders
such as paranoid schizophrenia. The new cryogenic support systems to be incorporated into the
super-MEG machine will operate silently in any orientation. The space saved by removing the
liquid helium storage volume should permit the thousand-channel super-MEG system to occupy
less overhead volume than existing 37-channel systems.
E. Thermo-Mechanical Risk Reduction Experiments
The development of this integrated cryogenic support architecture will involve the construction of
micro-lithographic arrays of SQUIDs and superconducting sensor coils which are supported by
thousands of thermally-conducting electrical feed-throughs protruding from the refrigeration
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