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will require large memory capacity and high speed.
In a clinical setting, it is desirable to be able
to compute complete images in under 5 minutes. This “benchmark” is roughly the time it takes
to process an X-ray film. Computation of spontaneous brain activity at a specified location
(simulating an invasive depth electrode) must be computed in realtime.
D. 41 *1 Generation Brain Activity Imaging System Design Goals
The overall goal of this aspect of the project is to develop an instrument capable of generating a
moment-to-moment image of the brain’s electrical activity. To accomplish this, the instrument
must measure the magnetic fields generated by the brain over the entire head with high spatial
resolution. Based upon a sensor spacing of approximately
1 cm, this will require on the order of
1000 independent SQUID sensors.
Using present manufacturing practices, it is impractical to scale the current MEG system designs
to 1000 channels. Analog dc SQUID sensors require a minimum of four wires (excluding the
superconducting connections to the sensing coils)— two for bias and two for modulation and
feedback— to connect with their room temperature flux-locked loop (FLL) electronics. These
connections, which go from 4.2 K to 300 K represent a significant thermal load to the SQUID
dewar/refrigeration scheme. Furthermore, it will be necessary to address the cross-talk between
channels arising from the close proximity of these leads to one another. Even if we were to
presume that such a system could be built using current techniques, the cost— on the order of
$50,000 per channel — would make a 1000 channel system unmarketable. Clearly, in order to
engineer the next-generation MEG system, we must address questions of cost and
manufacturability.
Experimental all-digital thin film dc SQUID magnetometers have been demonstrated by several
laboratories. The most noteworthy example was developed by Fujitsu, implementing the entire
flux-locked loop on a single superconducting integrated circuit chip. This digital SQUID
requires only three wires— bias, pulse output, and common. Since the bias and pulse output
lines may be digitally multiplexed, a practical all-digital SQUID can significantly improve the
thermal design by reducing the number of wires per channel going from 4.2 to 300 degrees K,
and reduce crosstalk by virtue of a digital rather than analog output. The all-digital design also
eliminates much of the room-temperature electronics, including the data acquisition subsystem.
Furthermore, the input (sensing) coils would be integrated “on-chip,” eliminating the need for
superconducting interconnects, thus increasing reliablility. Development of a single chip digital
SQUID magnetometer could be the key to making large array MEG systems commercially
practical.
The cryogenic support of the current generation of commercial MEG systems consists of a
dewar in which the dc SQUIDs and sense coils are immersed in a bath of liquid helium. The
evaporation of liquid helium provides the refrigeration for the SQUIDs, which must be
maintained below 10 K to be superconducting. A helium dewar system requires periodic
replenishment of the liquid cryogen, and also poses limits to the degree to which the dewar and
SQUIDs may be tipped in order to position the system around the head. Closed-cycle
refrigeration of a dc SQUID magnetometer, using a Joule/Thompson heat exchanger has been
successfully demonstrated using commercially available refrigeration components. In order to
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keep the proposed 1000 channel neuromagnetometer as small as possible, it will be necessary to
develop a low-noise closed-cycle refrigeration scheme— a cryocooler— eliminating the need
for a liquid helium reservoir large enough to immerse all sensors.
The Earth’s magnetic field is approximately
1 billion times larger than the brain’s magnetic
signals, which are about 5 x 10' ‘3 T peak-to-peak. In a typical city hospital, electrical
machinary and ferrous objects moving in the Earth’s magnetic field result in magnetic noise on
the order of 10'^ T peak-to-peak. Many commercial neuromagnetometer designs include a
magnetically shielded room as part of their system. This shielding attenuates the magnetic noise
to levels low enough for the brain’s MEG signals to be seen. However, a magnetically shielded
room adds about $500,000 to the cost of an MEG system. Canadian Thin Films (CTF) has
demonstrated a SQUID neuromagnetometer that does not require a shielded room. Using wide
dynamic range digital SQUID electronics, they use noise reference channels to sample and
subtract the ambient magnetic noise. A wide dynamic range design and adaptive noise reduction
must be explored in the 4^ generation MEG system design.
What does one do with 1000 channels of information? The traditional approach borrowed from
EEG— displaying individual channel time series on a strip-chart recorder— is impractical.
Furthermore, conversion of the MEG data into single and multiple equivalent current dipoles
(misleadingly referred to as “magnetic source imaging” by some companies) discards most of
the information obtainable from a large array MEG system. To address this, we propose using
beamforming and beamsteering methods, a procedure borrowed from the processing used for
large phased array antennas in radar and radio. This technique will permit us to estimate a three
dimensional image of the instantaneous electrical currents throughout the brain. In addition, the
electrical activity in any location within the brain could be examined as ifan invasive depth
electrode had been implanted there.
In order to compute accurate high-resolution tomographic images of brain activity, the precise
shape of the head, together with that of each tissue layer (scalp, skull, dura, cerebral cortex, etc.)
must be known. Thus “turnkey” software must be developed for automatically segmenting MRI
or CT images of the head into a finite element computer representation of each different tissue
layer. It is this finite element model that enables us to compute the magnetic fields and electrical
potentials due to the electrical activity generated by the brain. It is expected that a low-end
parallel supercomputer will be required in order for image segmentation and FEM compution
time to take less than 5 minutes.
Computation of an image of the source current distribution using the measured magnetic fields
and will also be programmed on a supercomputer. A time-moving image of brain activity will be
computed frame by frame. The source current images will be “fused” with the anatomic image
of the brain, thus enabling the viewer to see the relationship of the brain activity with its
corresponding anatomic structures. A graphics workstation will be used to permit flexible
display of the fused images. Selection of an anatomic coordinate from the brain image will allow
selective display of the time-changing brain activity waveform that was estimated for that
location.
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Clearly, the development of such an advanced neuromagnetometer will require a systems
engineering approach. Developing processes for improved sensors and cryogenic support are
important, but don’t constitute a whole system, whose design will guide potential manufacturers
in production techniques needed for the next-generation neuromagnetometer.
V. Proposed Effort
The proposed effort consists of two phases.
In the first phase we will undertake risk reduction
experiments that will lead to the development of a small-scale near realtime MEG based imaging
system. While this instrument will not give simultaneous full-head coverage, it will constitute a
proof-of-principle instrument and will enable the second phase of the proposed effort.
In the
second phase we will implement the full-scale instrument design that minimizes the associated
risks as identified in Phase I. The second phase will end with a full-scale demonstation of a near
realtime MEG based imaging system.
The configuration of the full-scale MEG imaging system is shown in Figure
1. The cryogenic
assembly is positioned over the patient's head.
It contains the array of SQUID magnetic-field
sensors, which spans the entire scalp and must be closely aligned with it, along with the cryogenic
pickup electronics. An alignment subsystem monitors and adjust the position of the assembly
relative to the head. The low-level signals from the pickup coils are brought out to a bank of
room temperature amplifiers, then sampled and digitized by the acquisition subsystem to create
digital data files which are stored on a host computer. The host provides a user interface for
control of system parameters such as amplifier gain and sampling rate.
It also serves as a front
end to a parallel supercomputer which calculates the extracranial magnetic fields due to a point
source using the electromagnetic brain model (constructed from auxiliary data such as MRI scans)
and uses these results, along with the SQUID data, to compute maps of brain activity in space and
time. The host, under user control, post processes the maps and feeds them to a display
subsystem. The system software also provides for system calibration using test inputs developed
from SQUID measurements of simulated brain-like signal sources, i.e., phantom measurements.
Figure
1. System configuration
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Phase
I of this project will include a detailed, end-to-end proof of principle. We will develop
prototype computer codes for electromagnetic brain modeling and activity mapping, and we will
acquire the supercomputing resources needed to run these codes rapidly. The algorithms will be
based on realistic models of brain signals derived from patient specific brain data. Analyses and
simulations will establish the capabilities, such as number of channels, sensor spacing and noise
floor, that the full-scale MEG system will need to make useful whole-brain maps of the
phenomena of interest
To facilitate brain data collection necessary for Phase I proof-of-principle experiments, a state-of-
the-art MEG system (37 channels or more) will be procured and installed at the VA Medical
Center in Albuquerque. Although such a system does not provide simultaneous whole-head
coverage, nor adequate sample density to measure all types of brain activity in a clinical setting, it
is valuable as a source of research data from which space-time profiles of important brain events
can be generated. Today’s state-of-the-art models of brain activity are much too crude to yield
predictions at this level of detail, and so cannot serve as a guide to MEG system design. Once the
empirical profiles are found, they will be expressed as computational models suitable for analysis
and simulation.
A computational electromagnetic model of the brain is needed to predict what data a signal of
known profile will produce in a MEG sensor array of known configuration. This is referred to as
a "forward” model, and is a necessary part of the "inverse" model which reconstructs the brain
activity from a given set of sensor data. The brain signals can be thought of as current sources
("primary currents") which induce "secondary" return currents that obey Ohm’s law. These
currents produce magnetic fields as described by the Biot-Savart law; each SQUID sensor
measures a component of the total field at a point in space. The task of the forward model is to
compute the magnetic fields, given the primary currents.
In most models, the head is treated as a
set of concentric spheres, for which the field can be expressed by an analytic formula. The errors
in such a model are unacceptable in a system that is to localize the measure the primary sources
with high accuracy, because they cause high-performance inversion algorithms to fail. With a
realistic electrical model of the head, no analytic solution is available; the field must be computed
numerically. As part of the proof of principle, we will develop codes to compute a 3-D finite-
element model tailored to the shape of each patient's head, as derived a priori from auxiliary data
such as an MRI scan. The code will be validated, first through tests on standard shapes with
known analytic models, and later through comparison to data collected on phantoms. An existing
parallel supercomputer, belonging to Dept. 9735 at Sandia, will be upgraded as necessary to run
the codes at a speed sufficient for large-scale simulation and testing.
To provide clinically relevant data requires both timely and accurate patient specific brain
information. The ability to accurately image brain source currents is dependent on having an
accurate forward solution. The forward solution consists of solving for the extracranial magnetic
field generated by a localized source current within a brain model.
In order to generate an
accurate forward solution the brain model will be generated directly from patient specific
anatomical data gathered through a standard MRI session. During Phase I and using the MRI
data we will demonstrate that (1) a detailed finite element mesh of the brain can be constructed
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