Download Georgetown MRI Reading Center (GMRC)

Georgetown MRI Reading Center (GMRC)
1. Overview
The Georgetown MRI Reading Center (GMRC) brings together a group of researchers and computer specialists
uniquely capable of carrying out the responsibilities of the MRI Reading Center part of the SPRINT-MIND
(Systolic Blood Pressure Intervention Trial - Memory and Cognition IN Decreased Hypertension) multi-site
study. Dr. VanMeter has extensive experience in three areas critical for the successful implementation of the
MRI Reading Center: 1) large volume multi-site MRI data collection and QC procedures; 2) management of
large-scale software and networking projects; and 3) image processing and segmentation expertise. Dr. Fricke is
an MRI physicist with over 20 years of experience in the MRI field and has special expertise in MRI protocol
development and optimization of QC procedures. Vlad Staroselskiy is a senior systems and network engineer
with 16 years experience in developing and implementing secure network transfer systems including those used
at the Pskov Savings Bank of the Russian Federation. These individuals have a long track record of working
together on a number of different projects going back 6 years. This core group of experts will be assisted by
additional personnel with the requisite skills needed to fulfill the mission of a project of this size and scope.
The remainder of this proposal describes the GMRC team members’ expertise and their role on the project, MRI
segmentation methodologies, the MRI protocol development, quality control procedures, the data transfer plan,
and the infrastructure needed to implement this project.
2. Personnel
John VanMeter, Ph.D., is Assistant Professor in the Department of Neurology at Georgetown University
Medical Center (GUMC) and Interim Director of the 3T MRI facility at Center for Functional and Molecular
Imaging. Dr. VanMeter received his Ph.D. in computer science from Dartmouth College. He has over 15 years
experience in the design and implementation of neuroimaging experiments as well as their data analysis. Dr.
VanMeter’s experience includes two years as a staff fellow in the Laboratory of Neuroscience in the National
Institute of Aging, where he co-authored the first paper to use fMRI to investigate dyslexia. Dr. VanMeter has
led the development of a number of major software programs as the Director of Research and Development at
Sensor Systems, Inc. This includes a commercial software package that is utilized at over 300 research
institutions worldwide in the analysis of multi-modal datasets including structural MRI and fMRI as well as the
subsequent development of one of the first FDA cleared fMRI clinical analysis package. As PI of an NIH
funded STAART (Studies to Advance Autism Research and Treatment) center grant project, Dr. VanMeter is
applying a number of MRI based techniques including fMRI, Diffusion Tensor Imaging (DTI), and MR
spectroscopy to investigate the neurobiological basis of autism. He also designed and developed the database
and data transfer systems used for the MRI data collected in the NIH Pediatric Brain Development project
(http://www.brain-child.org), which was a longitudinal study that included MRI scanning and
neuropsychological evaluation of 500 children of various ages at seven sites across the country. Dr. VanMeter
developed the secure data transfer mechanisms and tracking tools, a suite of data retrieval and viewing software
used in the quality assurance of the imaging data, and the database used for the imaging data collected. The
volume of data collected, stored, and databased at the end of this project was over 20TB. He has also developed
a database system for the Georgetown’s Center for Functional and Molecular Imaging (CFMI) that integrates
and manages the neuropsychological and imaging data collected in the 3.0Tesla MRI facility at Georgetown.
Dr. VanMeter will be the Director of the GMRC and be responsible for managing the overall efforts of the
GMRC throughout the SPRINT-MIND project period. In addition, he will be responsible for establishing and
coordinating with the CC the submission of the GMRC’s data to the CC and incorporating the MRI data with
the CC’s database systems. His responsibilities in the GMRC will include designing the database and MRI scan
transfer mechanisms including automated removal of PHI (personal health information) from the MRI scan
headers. He will work closely with Mr. Staroselskiy in the development efforts and oversee the project
programmer. Dr. VanMeter will work with Mr. Staroselskiy on the implementation of the data flow strategies to
coordinate the data collected at the MRI sites. Dr. VanMeter will work with Dr. Fricke on the development and
implementation of both the MRI scanning protocol and the QC (quality control) procedures.
Stan Fricke, Ph.D., is currently actively working on five NIH funded grants of which two involve brain
imaging and one involves MRI equipment development for ultra high-speed imaging. Dr. Fricke has a Nuclear
Engineer's degree (Nucl. Eng.) in radiological sciences from the Massachusetts Institute of Technology, as well
as a degree in Statistical Physics from the University of Torino (Turin, Italy). These academic qualifications
make him well suited to field almost any question in the field of in-vivo imaging. He has extensive experience
in in-vivo magnetic resonance spectroscopy and in magnetic resonance imaging.
Dr. Fricke's first experience in human imaging was in Tulsa Oklahoma (1985-1986). As a summer research
student Dr. Fricke worked at Oral Roberts City of Faith Hospital, imaging cancer patients and assisting in
planning radiation therapy based on the image data. From this experience was born his first publication in 1987
(meeting abstract that can be searched on the "Web of Science") was on manganese chloride's and nickel
chloride's effect as contrast agents for magnetic resonance imaging studied at various magnetic field strengths.
In just the last few years manganese has been used as a neuronal tracer/contrast agent for MRI with phase one
clinical trials. During 1991-1999, Dr. Fricke worked with the University of Florence on various projects
involving multiplatform imaging for the diagnosis of vision disorders due to cerebral oncological complications.
Later in 1994-1999, Dr. Fricke worked at Italy's Scientific Institute for Tumor Research (IST, Genoa, Italy). Dr.
Fricke was a faculty member in the Department of Psychiatry at Wayne State University for three years and an
Associate Professor in the department of Neuroscience at Georgetown University Medical Center for five years.
He has lectured at Georgetown University on the topic of imaging of CNS trauma and he is well published in
the field of brain trauma and rare disease linked neurodegeneration. During his time at Georgetown University
Medical Center, he helped to setup the quality control procedures that are still in use today, defined a set of
protocols most notably the MR spectroscopic protocol used to reliably and reproducibly acquire spectra from a
number of different brain regions, which has been used in two large scale imaging projects: STAART (Studies
to Advance Autism Research and Treatment) and the UCRDRC (UREA Cycle Rare-Diseases Research Center)
center grants. Currently Dr. Fricke is the MRI Physicist at Children's National Medical Center. Dr. Fricke has
worked on General Electric, Siemens and Philips MRI platforms and worked with a neuroradiologists,
radiologists, and research to develop MRI protocols that are uniquely suited for the specific study.
Dr. Fricke will have primary responsibility in the development of the MRI scan protocol and the QC
procedures. He will work with Dr. VanMeter to ensure the scanning protocol that is used meets the needs of the
project in terms of contrast required for the various segmentation procedures and acquisition parameters with an
eye towards maximizing the trade-off between total scan time and optimization of MRI scan quality. Dr
Fricke’s long history in the MRI field makes him ideally suited to design the QC procedures for this project. He
will work with Dr. VanMeter on the development of these procedures and their implementation to ensure that
the data are truly comparable across all sites. In addition, he will make the annual onsite visits to each MRI
center to ensure proper staff training and conduct a full and rigorous QC protocol.
Vlad Staroselskiy, M.S., has excellent expertise in systems and network protection. His experience has
included designing and implementing the networking infrastructure for several banks, which obviously require
high-level of data security. He has successfully planned, designed, installed and configured a number of systems
around the world. Between 2002 and 2004 he also took a part in development of NetBait product, which acts as
a trap for network intruders, trying to obtain unauthorized access to protected systems. More recently he has
worked for a number of academic institutions including Wayne State University and Georgetown University
Medical Center. Through these various positions he has been thoroughly trained in the requirements for HIPAA
(Health Insurance Portability and Accountability Act) compliance and the need to protect PHI (Personal Health
Information). As a Senior Systems Administrator at Georgetown University Medical Center, Mr. Staroselskiy
has been involved in the maintenance of multi-platform computer systems, providing day-to-day support for
research faculty and staff. This has included designing the websites for both the Center for Function and
Molecular Imaging (CFMI) and the Center for the Study of Learning (CSL). The website for CFMI included
developing the calendar system for scheduling MRI scanner and EEG time. In addition, this system includes a
database that is used to record laboratory notes regarding the scanning sessions. Specific projects have included
planning, installing and configuring Cisco PIX firewalls for Center for the Study of Learning CFMI, CSL, and
the Small Animals Imaging Lab with network monitoring, using MIDAS, Nagios, Cacti and MRTG software.
He also implemented secure site-to-site VPN connections between these labs with secure off-site access. Mr.
Staroselskiy has also setup and administers CFMI’s 19-node Linux cluster using a diskless LM/MPI
computational design with a dedicated management node. He also has extensive experience installing,
configuring, and maintaining various MRI image analysis and spectroscopy applications, such as MEDx,
LCModel, FSL, Matlab/SPM, AFNI, Paravision, and others.
Mr. Staroselsky will be primarily responsible for the development of the networking protocols and procedures.
He will implement the necessary procedures to ensure the appropriate level of security across the network of
MRI facilities and the GMRC. He will also be the main developer of the web-based database system used in this
project leveraging his experience in the development of the CFMI database system. He and Dr. VanMeter will
oversee a programmer dedicated to this project who will implement the database system, the MRI data transfer
system, and the various notification systems. He will also oversee a system manager for the GMRC whose
responsibility it will be to troubleshoot network and computer systems problems.
3. MRI Segmentation and Volume Measurement
MRI provides a method for examining tissues in-vivo and with the use of high-resolution MR imaging it is
possible to produce very detailed images of the brain. It has long been possible to quantify the volume various
complicated tissues such as gray matter, white matter, and CSF from MR images of the brain (VanMeter and
Sandon 1992). A number of morphometric techniques have relied on human raters to manually trace the
boundaries of the tissue of interest; however this method inherently introduces subjectivity. To reduce this
subjectivity, the raters are trained and their ability to trace the same boundary must be compared within rater
and across raters.
Image segmentation techniques are a class of computer algorithms designed to automatically extract the
boundaries of a given class of tissue or an organ. By their nature these algorithms provide an objective way to
identify and measure particular parts of the image separate from the rest of the image. There are several basic
techniques available that can be applied to a given image segmentation problem. These include 1) thresholding
which rely strictly on the intensity (pixel brightness) to isolate a given tissue; 2) region growing which starts
from a seed point in the tissue of interest and iteratively grows outward until the boundaries of the tissue are
reached typically based on a threshold; and 3) clustering which iteratively assigns pixels to one of k-classes
based on how close it is to the intensities of a given class (Duda and Hart 2000). These image segmentation
techniques provide a basic toolbox
and are often combined to
improve the overall result.
An example of the output of a
computer algorithm used to
identify gray matter in a T1weighted coronal slice of a human
brain (VanMeter and Sandon
1992) is show in Figure 1. This
particular method uses a
mathematical model of the
distribution of MR image
intensities called a material
mixture model. In this model the
Figure 1. Example of volumetric measurement of gray matter. In the MR
image pixels classified as gray matter are shown in red. The histogram
shows the distribution of intensities regardless of tissue type (blue) and
the Gaussian distribution models for the expected range of CSF (green),
gray matter (red), and white matter (yellow).
distribution of intensities for each tissue class (gray matter, white matter, and CSF) is modeled with Gaussian
distributions (Figure 1b). This type of model is required even though a given tissue class such as gray matter
might be darker than white matter because it is not always the case that gray matter will have the exact same
range of intensities across a series of images. Thus, this model provides a mechanism for identifying the range
of intensities (thresholds) that most likely correspond to a given tissue class. In the material mixture model the
thresholds correspond to where the distributions of two neighboring tissue classes cross. The complete
algorithm combines the thresholds obtained from the material mixture model with a neural network trained to
recognize the overall morphology of the different tissues (VanMeter 1993).
Measurement of total cerebral, left- and righthemisphere, and cerebellar volume can be calculated
from high-resolution T1-weighted images such as the
Siemens MPRAGE or the GE SPGR. Pre-processing of
the images included: rating of the individual scans for
quality; removal of non-cortical structures, such as the
scalp; intra-subject registration of the stripped
MRPAGES; calculation of the mean MPRAGE; and
bias-field estimation and correction. The scalp stripping
and bias field correction was performed using BET
(Smith 2002) and FAST (Zhang, Brady et al. 2001) from
the FSL software library (http://www.fmrib.ox.ac.uk/fsl/)
respectively. The images were registered using a rigidbody transformation with AIR (Woods, Grafton et al.
1998).
Figure 2. Results of automatic segmentation of the
cerebellum (red) and left (yellow) and right (green)
hemispheres.
Using a program called Graph-Cuts, each subject’s mean MPRAGE was subdivided into three compartments:
the first two consisted of the left- and right-cerebral hemispheres and the third included both the cerebellum and
brain stem (Liang, Rehm et al. 2005). This program uses a 12 degree-of-freedom transformation derived from
registering a template volume to the subject’s mean image to reslice a volume with the three compartments
previously labeled. From this initially labeling, three connected graphs representing the two cerebral
hemispheres and the cerebellum are formed and then expanded providing a preliminary labeling of the subject’s
brain. This labeling is refined by determining the best locations to cut the three graphs from one another using a
standard graph-cuts method (Liang, Rehm et al. 2005). The volume of each of these compartments was
computed from the resulting labeled image. Total cerebral volume was computed by summing the volumes of
the two hemispheres with the cerebellum and brain stem. The volume of CSF was removed in all volumetric
calculations. In addition, a left-right symmetry index (SI) was computed as follows:
(Lv – Rv)
SI = 100 x
½ (Lv + Rv)
where Lv and Rv are equal to volume of the left and right hemispheres respectively (Galaburda, Rosen et al.
1987).
In the STAART (Studies to Advance Autism Research and Treatment) Corpus callosum (CC) area
measurements have been calculated using the seven subdivisions defined by Witelson: rostrum, genu, rostral
body, anterior midbody, posterior midbody, isthmus, and splenium (Witelson 1989). The brain is first oriented
with the Talairach atlas using a 6 degree-of-freedom transformation based on manual rotation of the midline and
identification of the AC and PC using tools in MEDx. The outline of the corpus callosum is manually traced in
the mid-sagittal plane of the transformed brain. The exact boundaries of the CC are identified using an intensity
threshold inside the manually traced region. In addition, the anterior most point of the inner convexity is
identified. Wtielson’s method subdivides the length of the CC between its anterior and posterior extents into
areas using specific geometric rules. The area of each subdivision is calculated from the overlap of the
subdivided regions and the hand-traced outline of the CC. In
addition, the cerebellar vermal areas have been computed by
manually tracing of lobules I-V, lobules VI-VII, and lobules
VIII-X in the mid-sagittal plane of the Talairach oriented brain.
Statistical comparisons of all the subregions of the CC and the
cerebellar vermis are examined with and without correcting for
total brain volume. A one-way multivariate analysis of variance
(MANOVA) was conducted using SPSS 14.0 (SPSS, Inc,
Chicago, IL) to test the null hypothesis that area of these regions
was not different between the two populations (autistic and
typically developing children).
4. MRI Protocol Development
Drs. VanMeter and Fricke will work together to develop the MRI scanning protocol. Development of this
protocol will build on the experience Dr. VanMeter has gained by participating in the ADNI (Alzheimer’s
Disease Neuroimaging Initiative), the Valproate Neuroprotection study, and the NIH Pediatric Brain
Development project. All of these projects have developed standard acquisition protocols that are designed to
collect MRI data that has as uniform as possible contrast characteristics and quality from all three of the major
scanner manufacturers: GE, Philips, and Siemens. In particular, the ADNI protocol is quite attractive as each of
the scanner manufacturers have worked with the ADNI participating sites to distribute the sequences
appropriate for that site’s particular scanner model. In addition, this protocol has been developed to work with
both 1.5T and 3.0T MRI scanners. This protocol includes the collection of two high-resolution
(1.0x1.0x1.2mm3 voxels) T1-weighted images and a high-resolution (0.9x0.9x3.0mm3 voxels) double-echo
sequence, which acquires both the T2 and Proton-Density weighted images in the same scan. Additional
calibration scans that characterize the B0 field are also acquired. The entire protocol can fit into a 30-minute
scan slot. The NIH Pediatric Brain Development project also has a standard protocol optimized to provide
cross-site consistency in contrast and quality. The particular needs of the SPRINT-MIND study will be taken
into consideration when developing the protocol to ensure the scans collected provide the optimal data for
measuring the different volumes of interest. For example, if the study requirements call for the measurement of
hippocampal volume one of the scans in the protocol will be collected perpendicular to the long axis of the
hippocampus as is used in the Valproate Neuroprotection study.
5. Quality Control Procedures
The quality control procedures will be developed based on the best practices and
accumulated knowledge that Drs. VanMeter and Fricke have gained through their
participation in other multi-site neuroimaging studies and their own experiences in
the operation of MRI systems. The procedures will include four levels of quality
control: site qualification, monthly QC on a phantom, acquisition of QC scans within
24-hours of each subject’s scan, and annual onsite QC visit. The Magphan®
Quantitative Imaging Phantom will be purchased for each MRI-center from the
Phantom Lab, Inc. (Salem, NY). This phantom contains 165 polycarbonate spheres
mounted on a series of polycarbonate plates and posts filled with copper sulfate and
water solutions. Measurements of the phantom are compared with the known
positions of the spheres to give an accurate measurement of the distortion of the scanner. In addition, 4 of the
spheres produce unique contrast on T1, T2, and PD-weighted scans and can be used to track changes in contrast
over time. This phantom has the advantage of having been tested and used in the ADNI project. All of the QCscanning data will be uploaded to the GMRC using the same methods for the subject data. The GMRC will
develop automated procedures to track the quality of each scan over time measuring both spatial fidelity and
contrast consistency. The annual onsite QC visit will be used by the GMRC to perform a more rigorous set of
quality control tests as well as a living phantom scan using the MRI protocol from the study. This onsite visit
will also be used to train and re-test the staff at each site in the appropriate procedures for the collection of the
subject and QC data.
6. Data Transfer Plan
Data Transfer Experience
Dr. VanMeter’s role in another database system was specifically with regards to MRI data submission. The NIH
Pediatric Database (NIHPD) project collected both longitudinal imaging and neuropsychological data over 6
years on 500 children ranging in age from several months to 18 years old at baseline. The purpose of the project
is the development of a number of age specific imaging templates and to examine normal brain development.
Data were collected at seven research centers across the country and transmitted to a central coordinating center
at the Montreal Neurological Institute (MNI). All of the data collected is made available to both the center
collecting the data and the central coordinating center. Dr. VanMeter developed the image transfer database and
protocols for this project. This database architecture was designed to be scalable in nature.
Transmission and storage of the image data collected at each of the sites participating in the NIHPD project
required the development of an imaging database, specialized data transfer software, and quality control tools.
Of particular concern is the proper tracking of the data with respect to accurate subject and MRI sequence
identification. Data collected in this project are transferred from the MRI scanner to an onsite workstation that
has a fully functional version of the database and image retrieval server. The data are reviewed locally for
image quality using a set of tools designed and developed by Dr. VanMeter. All imaging studies are then
transferred to a central data integrity center managed at Georgetown University by Dr. VanMeter. The data are
passed through automated integrity checks such as identify missing slices, violations of the established imaging
protocols, and mislabeled data before being sent onto the central coordinating center.
The data transfer system developed for this project uses the international image transfer and storage protocol
DICOM, Digital Imaging and Communications in Medicine (ACR/NEMA 2001). The DICOM standard
includes complete specification of attributes of the data including fields for subject ID, imaging parameters, and
sequence labels. The DICOM standard also defines a rigorous protocol for the transfer and storage of medical
image data from a variety of sources including MRI. An advantage of the DICOM image transfer protocol over
simpler methods such as FTP is that the receiving application must communicate any type of transfer failure to
the sender. Furthermore, when such failures are detected, the sender is configured to automatically attempt to
re-send the images that failed.
This system has been in place since 2002 and has been used to successfully transfer over 10,000 image
acquisitions to date with only 150 cases requiring manual intervention. The stability and reliability of this
system has exceeded the original requirements. In addition, laborious hand identification and manual transfer of
the data has been eliminated increasing the integrity and accuracy of the data submitted through this process.
Data Transfer Procedures
The flow of MRI data to the GMRC is shown in Figure 3. The following standard operating procedure will be
used for data collection:
1. DCFs (document control forms) will be filled out by the MRI technician at the time of each scan
will be entered in a secure web data entry form hosted at the CC. Data in the DCFs will be
checked for completeness at the time of entry.
2. MRI data will be sent from the MRI site to the GMRC using a Java-based program that will first
remove all PHI information from the DICOM headers of the scans. The person sending the data
will be prompted to provide the appropriate subject ID. Standardized MRI protocol scan names
will be used at each site and used to identify the different parts of each study.
3. An automated tracking system will begin a process of checking for all of the scans in the study.
Incomplete or missing scans will be trigger an email notification to MRI site’s point of contact if
the scan data has not been received within three business days of submission of the DCF.
4. Scans missing or incomplete after 5 business days will trigger a review by the GMRC to track
down the source of the problems.
5. Once the scan data has been successfully uploaded to the GMRC a trained research assistant will
check the quality of each scan. The results of the QC will be entered into the GMRC database.
6. All scans will pass through an automated QC procedure to ensure it can be used in the
volumetric analyses. Automated segmentation analyses will be run on each complete dataset and
the results will be automatically compared to expected norms. Scans that are 2 standard
deviations outside of the norm will be flagged and checked by the GMRC.
7. Scans not passing QC will trigger a notification to the MRI site that the subject should be
rescanned.
MRI 1
DCF
Forms
MRI 2
CC
MRI 3
DICOM based
MRI scan
submission
GMRC Database
System
Figure 3. Data flow diagram
Data Backup Procedures
While the use of RAID level-5 provides a high level of fault tolerance since the data is redundantly stored and
striped across multiple disk drives, all of the data in the database system will be backed up on a regular basis.
The current backup system will be expanded with additional tape drives.
The following backup regime will be implemented:
 Daily incremental backups will be used to backup any data changed since the last full backup.
 Weekly full backups with a set of 4 tapes used on a rotating schedule.
 Monthly full backups with a set of 3 tapes used on a rotating schedule.
 Quarterly full backups with a set of 4 tapes used on a rotating schedule. These tapes will be stored
off-site to protect the data against theft, or loss, such as fire, flood, or earthquake.
 Yearly full backups will be stored offsite. The tapes used for these backups will not be rotated.
With this backup system in place it will be possible to recover data going up to four months back in time using
the onsite tapes. In addition, the offsite tapes will provide the ability to recover data even further back in time.
Training Procedures
The GMRC will ensure that all MRI site personnel that will be working on the SPRINT-MIND project are fully
versed with the entry forms, the MRI data submission program, and the scan tracking system. We will develop
procedure manuals on the use of the DCFs, scan data submission, and scan tracking procedures. Additional,
procedures for tracking MRI scanner quality over time will be setup. The manual will include detailed, step-by-
step instructions for filling in all relevant DCFs, what constitutes an acceptable scan, and the scan tracking and
notification system. This procedures manual will also include instructions on personnel requirements, training
procedures for new personnel, and QC procedures. A combination of onsite and central training will be
provided for personnel from the MRI sites.
The procedures used to train new raters and certify all personnel involved in the morphometric rating process
will include measuring inter- and intra-rater reliabilities. A standard set of images will be used for this purpose.
With the help of a neuroradiologist the gold-standard of the boundaries of all of the structures being measured
in the SPRINT-MIND study will be identified. A minimum of 10 separate scans will be included in the goldstandard dataset. The GMRC director will validate each rater by examining their inter- and intra-rater reliability.
A minimum of 0.95 and 0.97 will be required for inter-rater and intra-rater reliability respectively. All raters
will be retested annually. A list of personnel and their certification status on all morphometric measurement
techniques will be maintained at the GMRC.
7. MRI Reading Center Infrastructure
The GMRC computer and networking systems will be built upon the existing resources in CFMI’s 3.0T MRI
facility. These resources include a 750 square feet equipment room that has a dedicated Liebert Challenger 3000
temperature and humidity controlled equipment room; a new IBM TS3310 tape library (3576-L5B) with 2
LTO4 drives with a total backup capacity of 24Tb; Arkeia 7.0 Network Backup data backup/retrieval software
that includes a federated data protection architecture; and 3 72”x29”x36” rack-mount computer cabinets fully
protected by 5 APC Smart UPS 5000 uninterrupted power supplies that connect to master computer to initiate
orderly shutdown of all equipment in the event of sustained power failure.
Other major equipment in CFMI includes a 40-node Linux compute cluster, which has 20 TB of attached disk
storage. Every node is equipped with standard software for statistical analysis of fMRI and structural MRI data
as well as visualization utilizing software packages such as SPM, FSL, and MEDx. All computers are linked via
an area 1000 base-T Ethernet Local Area Network (LAN). All of the CFMI computer equipment is connected to
the internet via Georgetown University’s Internet2 connection. The CFMI LAN is protected by Cisco PIX 525E
firewall, which has 2x1Gbit ports for LAN and WAN traffic, and 100Mbit interface for "demilitarized" zone
(DMZ), that hosts the CFMI web server. It provides security from outside threats and supports constant virtual
private network (VPN) connectivity for remote access by CFMI users.
Expansion of the CFMI Infrastructure for SPRINT-MIND
The CFMI infrastructure provides an excellent base upon which to build the infrastructure that will be needed
for the MRI Reading Center at a relatively low cost. To meet these needs and to maintain a strict separation
between the CFMI data operations and those of the GMRC the following additional equipment will need to be
purchased: 5 IBM x3350 compute node servers, 1 IBM x3350 backup server, a 3Com network switch, an IBM
LTO TS3100 Tape Library, APC Smart UPS, and software licenses for Red Hat Enterprise Linux and Arkeia
network backup software. These additional hardware components will be housed in the CFMI equipment room
in a separate computer rack. Access to these systems will be strictly limited to GMRC related work.
GMRC Training Facilities
Other resources available at Georgetown University that will be useful for conducting training include a Video
Teleconferencing facility. This service is made possible via the University’s phone system, a conventional TV,
and a PolyCom View Station. There are two rooms on campus that have been specially wired for
teleconferencing.
Georgetown University has several conference rooms available for departmental functions. The Research
Auditorium located in the Research Building can be reserved to accommodate larger workshops and seminars.
The Research Auditorium houses state of the art equipment and technical experts to assist with functions. In
addition to individual conference rooms located throughout the University campus, The Leavey Conference
Center houses an on-campus hotel for out of the area participants along with catering services and several
interconnected conference rooms.
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