March 2, 2005 Interview with Dr. Peter Basser, NICHD, conducted by
Claudia Wassmann, M.D., Ph.D., DeWitt Stetten, Jr., Memorial Fellow in
the History of Biomedical Sciences and Technology, Office of NIH
History.

Peter Basser became *Chief* of National Institute of Child Health and
Human Development *Laboratory of Integrative and Medical Biophysics*, in
1997, and is Head of the *Section on Tissue Biophysics and Biomimetics*.

***"THERE WAS SOMETHING REALLY FUNDAMENTALLY WRONG WITH THE APPROACH
THAT THEY WERE USING..."***

THE DEVELOPMENT OF DIFFUSION TENSOR IMAGING AND TRACTOGRAPHY AT NIH

CW: Can you start with telling me about the beginning of your scientific
career at the NIH.

Peter Basser: Well I had formally come in 1986 but I didn't start
working here until 1987, and I began in what was called the [*Biomedical
Engineering and Instrumentation
Program*](http://www.training.nih.gov/handbook/resource.html) \[BEIP\]
at that time. I was hired in the mechanical engineering section. My
background had been in fluid mechanics and medical equipment development
in graduate school and I expected to do similar kinds of activities here
at the NIH. However, the opportunities to do that were becoming more and
more limited because physiology and cell biology were, at that time, in
the decline, and molecular biology was becoming the dominant activity
here on campus. And so increasingly there were fewer and fewer
opportunities for people with engineering backgrounds to find a
meaningful set of research activities to be involved in on campus.

But I did find some interesting applications in two areas. One was in
drug delivery and transport of drugs, which was still considered to be a
very important area. I worked with some of the chemical engineers here,
[Bob
Dedrick](http://www.nih.gov/news/NIH-Record/03_07_2000/story03.htm),
[Paul Morrison](http://www.nih.gov/od/ors/dbeps/ddkr/); I also worked
with [Mark Hallett](http://www.ninds.nih.gov/find_people/labs/72.htm)
and [Brad Roth](http://personalwebs.oakland.edu/~roth/cv.htm) in the
area of magnetic stimulation. The reason that I think that's important
in terms of the invention and development of
[*DT-MRI*](http://www.nih.gov/news/pr/dec2000/dt-mri.pdf) is that those
two projects actually provided the impetus for the development of
[*DTI*,](http://www.nida.nih.gov/whatsnew/meetings/pediatric/neil.html)
so it's not inappropriate to discuss them in this context.

In the case of drug delivery, the first one, Bob Dedrick and Paul
Morrison and others in the chemical engineering program here had been
working with [Ed
Oldfield](http://www.ninds.nih.gov/find_people/labs/95.htm) and they had
proposed a very novel drug delivery strategy, which is now called
[*Convection Enhanced
Delivery*](../../../../../../../../fhttp/::www.ncbi.nlm.nih.gov:entrez:query.fcgi%3Fcmd=Retrieve&db=PubMed&list_uids=9950503&dopt=Citation)
or CED. They, Bob and Paul, had conceived of infusing drugs into the
brain, into the extracellular space to overcome the blood brain barrier,
particularly for people who suffered from cancer, from various brain
tumors, where the blood brain barrier prevented drugs in the body from
getting to the actual cancerous tissue in the brain. This direct
infusion was a real breakthrough, but there was some attendant problem
with it. That is, when you infused into white matter or near white
matter in the brain, they found in some of the early experiments in cat
brain that the drugs would shoot along what we later identified were
white matter tracts. And that's how I actually got involved in the
project because I had a background in porous media theory. I was
starting to help them with the mechanical processes of drug infusion:
Because the hydraulic permeability or resistance of the white matter was
so different along the fiber tracks as opposed to perpendicular to them,
and because it was so different from the surrounding gray matter, the
drugs didn't go where they expected them to go. Rather, the drugs shot
along the white matter pathways. And at that time these were really
unpredictable paths.

I realized at that point that we needed to know what the *hydraulic
permeability map* was. Not just how it changed from tissue to tissue,
from region of the brain to region of the brain (because it was
*heterogeneous*), but it was also *anisotropic*---there were preferred
directions along which the fluid would flow; we had actually the same
issue in magnetic stimulation.

You know Brad Roth and I had developed a model of how electric fields
interact with brain tissue, and we had parenthetically, in one of our
early papers, talked about how electric fields would affect a nerve that
was curved or kinked. I came up with a little model to describe how
magnetic stimulation would work if the nerve weren't straight anymore,
but took a sinuous path, which all of them do in white matter. And
without knowing their pathways of the nerve I also knew that we would be
unable to ever find out what the effects of magnetic stimulation of the
brain would be. So I was really primed at that point to find out a way
to look at the anisotropic and heterogeneous electrical and mechanical
properties of brain tissue.

CW: Was this the first step to diffusion tensor imaging?

Actually, the first exposure I had to diffusion imaging was a talk that
[Denis Le Bihan](http://www.meteoreservice.com/dlb) had given. He had
recently come to the NIH from France and talked about how diffusion
could be used---I think it was in stroke---and I thought it was very
interesting, but I didn't really initially make a connection to it. But
in the early 1990s, Denis Le Bihan and, I believe it was [Philippe
Douek](http://www.esmrmb.org/hauptframe.php?pid=37) had a poster
presentation at one of the NIH research festivals off in a corner in one
of the white tents that they had constructed over here in the parking
lot East of Building 30.

They had done something very novel. They had shown that they could color
code different parts of the brain according to what they thought was the
orientation of diffusion. That was a poster that resulted in a paper, I
think early in the next year, by Denis and Philippe.

But I visited that poster and I was there with my friend and colleague,
Brad Roth, the guy I was doing the magnetic stimulation with, and I
realized that there was something really fundamentally wrong with the
approach that Denis and Philippe were using.

*"THERE WAS SOMETHING REALLY FUNDAMENTALLY WRONG WITH THE APPROACH THAT
THEY WERE USING"*

They knew---as I found out others did in the field at the same time,
like [Mike
Moseley](http://med.stanford.edu/profiles/frdActionServlet?choiceId=showPublication&pubid=62895&fid=4240)
and others---they knew that water diffusion in the brain was
anisotropic; and they used a *scalar model of water diffusion*. They
used a model that assumes essentially that water diffuses at the same
rate in all directions. And I knew that it had to be described by a
*tensor,* at least, just the way the hydraulic conductivity has to be
described by a tensor, or the electrical conductivity in the brain has
to be described by a tensor. And I started asking Denis Le Bihan all
kinds of questions without really tipping my hand. I said, "Well how do
you measure diffusion in different directions?" And, , "Aren't there
other components that you have to consider?" and things like that. The
thoughts were racing through my head at that point about describing this
\[process\] with a tensor because I realized\--since I had a background
in applied mechanics\--that once you have a tensor description of a
material you have a tremendous amount of information about it that you
didn't have before.

I was coy about it, frankly. I didn't want to tell him what I was
thinking, which is maybe a little disingenuous, but I think scientists,
unfortunately, have to be a little protective these days. And I raced
back to the lab, and I was almost manically describing to Brad Roth, on
the way back, this picture of [*diffusion tensor
imaging*](http://www.nih.gov/news/pr/dec2000/dt-mri.pdf) that I was
imagining, where you would actually measure not a scalar diffusion
coefficient but a tensor. And, actually, very busily for the next few
days I was writing up a prospectus that I wanted to share with Denis and
his colleague [Robert
Turner](http://www.ion.ucl.ac.uk/research/imaging/imaging_physics.htm),
whom I knew and was friends with. And I came up with the idea of fiber
track following, the *eigenvalues* and *eigenvectors* of the diffusion
tensor providing useful quantities that people could image along with
the invariants of the tensor, like the Trace and other tensor derived
quantities that could provide useful information about the fiber
orientation and tissue properties, and I showed it to Denis.

I brought with me this multi-page disclosure, if you will, and Denis was
very excited about it, because he knew that this was something that
wasn't being done and had real potential. But he said in his thick
French accent, "There's one problem," he said, "You know we don't know
how to measure the tensor."

*"THERE'S ONE PROBLEM," HE SAID, "YOU KNOW WE DON'T KNOW HOW TO MEASURE
THE TENSOR."*

So I left the meeting very discouraged because I had this idea but we
didn't know how to measure the quantity that I wanted to measure, and
specifically they didn't know how to measure the so called *off diagonal
elements* of the tensor. So Bob Turner and Denis had suggested that I go
back and read some of the classic texts in the diffusion MR area, like
[Carr](http://www.garfield.library.upenn.edu/classics1983/A1983QN93100001.pdf)
and
[Purcell](http://nobelprize.org/physics/laureates/1952/purcell-lecture.html)
(1954) and particularly the work of Stejskal and Tanner (E.O. Stejskal
and J.E. Tanner, J. Chem. Phys. 42, 288, 1965) which I was totally
unfamiliar with. And I spent days pouring over those articles,
particularly Stejskal and Tanner's articles, and I realized there were a
couple of formulas that were very relevant to what we were interested
in, and I studied one formula in particular in which actually Stejskal
and Tanner have a tensor, a diffusion tensor, in their equation, but
nobody had actually measured that quantity. It was only of theoretical
interest. And I realized that that expression turned out to be just a
mathematical point, but the signal intensity that was measured in the
NMR experiment was related to the scalar quantity, which was essentially
a projection, a quadratic form. And it wasn't obvious from the way
Stejskal and Tanner wrote it, that it was of this mathematical type,
that was called a quadratic form. And when I rewrote it in the way that
I was familiar with in the vector notation, I realized what it really
was: It was a simple relationship between the signal that you measured
and the projection of the diffusion tensor (that appears in the
Bloch-Torrey equation) along different directions.

And then the question was, "Could you measure or estimate all the
components of the tensor from that equation?" And I realized that if you
applied gradients in more than three directions, it turns out six
directions, that you can get enough information. You could get enough
samples of the orientational distribution to be able to invert that
equation and to be able to solve for all of the elements of the tensor.
And I excitedly wrote Denis Le Bihan an e-mail at that point. I didn't
even know whether you could apply gradients in different directions
other than X, Y and Z because I had so little understanding of the
gradient hardware. And I asked him whether one can apply gradients along
any direction, and he said, "Yes" and I said, "Well in that case then we
can measure all of the elements of the tensor."

*"THOSE WERE THE KIND OF RUSHES THAT YOU GET IN THIS BUSINESS THAT
REALLY MAKE IT ALL WORTHWHILE"*

That was a very exciting moment. I don't know -- if you're not a
scientist and you're not interested in invention or discovery, those
moments may pass over as being not very significant, but those are the
kind of rushes that you get in this business that really make it all
worth while. You get this surge of excitement when you figure something
out that you have a sense is more significant than the little problems
that you solve on a day-to-day basis, and it's really fun. I have to say
that anybody who is willing to work for long periods of time and
periodically gets these moments of excitement ... I think---for me it
was just a very thrilling moment.

CW: And where did you take it from there?

PB: Well actually this was an amazing story too, because there are so
many people involved and activities that had to be done in order to
bring this from bench to bedside. So the first thing is Denis and I
started corresponding, and [James
Mattiello](../156.40.88.3/new/releases/MRI_technology.cfm) then, who was
working with Denis, and who was also working in our program \[BEIP\],
was a little frustrated with some of the projects he was working on and
decided that he wanted to start working with us. So I was excited about
that because Jim had a technical background in MRI. He had been working
in the area for maybe a year and a half at that point, and he would
provide a lot of experimental help, which I really couldn't provide
because my knowledge at that point of the NMR hardware and sequences and
things was almost nonexistent. And so we started doing diffusion
experiments with water. Denis got us some magnetic time down at the NMR
center and we started to measure. Since we had this mathematical
framework that related the signal that we measured to the diffusion
tensor the first thing that you want to do is show that the diffusion
tensor in water is an isotropic tensor, which means that if you look at
the diffusion process along any direction that it appears the same and
that it has a characteristic form when you write it as a tensor; if you
can't do that you can't look at other materials that are more complex.

CW: When was this?

PB: Well, it was around late September of 1991, I believe, when we had
that Science Fair here at the NIH, and this would have been now in the
November/December timeframe in 1991.

CW: And you started measuring in animals?

PB: No, those were a long time away. We started to look at water and
glycerin. I went to the *Safeway* and I bought pork loin, and Jim, who
had came from an Italian background, knew about something called
spaghetti squash, and he recommended that as something that we could
look at. And we looked at it, basically we looked at water and pork loin
initially and we wanted to see whether we could get the right
\[isotropic\] tensor properties in water and whether we could see
diffusion anisotropy in pork loin.

*IMAGING PORK LOIN AND WATER*

In fact people in the 1970s, Hazlewood, Chang, Cleveland, and others had
looked at diffusions anisotropy in skeletal muscle---Hansen, too---and I
had read some of their papers, but they didn't look at it they way we
did. They realized that there was diffusion anisotropy but they didn't
use a tensor framework to study it. But imaging created a special
problem which was that as tissues are heterogeneous and they may have
different diffusion properties in different areas, in different regions,
and the fiber direction, the structure may be different in different
places. And since the materials are turbid, anyway, you can't tell by
looking at it how those fibers are aligned. So, this idea of being able
to measure a local diffusion tensor, first without imaging gradients and
then with imaging gradients, was very powerful because it allowed you to
take into account the fact that the tissue properties were changing from
place to place.

The reason I say that is, because when we wrote our first abstract
describing it at the Society for Magnetic Resonance in Medicine (now
[ISMRM](http://www.ismrm.org/)), I think, which we presented in Berlin
in 1992, we looked at a sample of pork loin and we showed that we first
measured the diffusion tensor for a large region of that pork loin
specimen. Then, we physically rotated that specimen. Jim Mattiello
actually physically rotated the pork loin specimen in the magnet. We
repeated the experiments, calculated the tensor and we were able to show
that the directions that we calculated for the pork loin muscles
followed the direction of the rotation that he had applied physically on
that sample. So we were measuring something intrinsic to the tissue.
These principal directions that we were able to extract from the
diffusion tensor were fundamental to the tissue architecture and were
independent of the coordinate system that we made the measurement in,
which was really I think a very important demonstration then.

So we actually played around with the pork loin and the water for a long
time because as it turned out we weren't able initially to get the water
diffusion properties to look isotropic. And for a long time, we were
trying to figure out what it was, what artifact we were seeing. It turns
out that because the diffusion measurement is so sensitive to small
variations in the gradient strength, at the scale of the gradients
induced by eddy currents and other perturbations that we were having
trouble making water look isotropic. Actually that led to another part
of I think our original
[patent](http://ott.od.nih.gov/db/abstxt.asp?refno=323) disclosure,
which was the idea of using the diffusion tensor to calibrate gradients.

Denis had pointed out that the diffusion sensitization---the attenuation
of the diffusion signal---went with the square of the gradient and the
signal that you measured whereas phase changes go with the gradient
itself. So it turns out that the diffusion signal is much more sensitive
to the gradient value than the phase image and could be used as a way of
calibrating the gradients. And, it occurred to me that maybe one of the
things that we should do is, if we know that water is isotropic and
we're not getting an isotropic tensor, why don't we use that to
recalibrate the gradients to essentially correct them in software?
Because we know what the value should be and we know what we are
measuring, there's obviously a systematic error and we could use that to
correct the gradients in a tensorial way. So I had worked out the theory
for that and then we eventually put that into our patent application.

Now going from bench to bedside was a whole exciting story in itself,
because after Denis and Jim and I---we worked very well together we all
brought different things to the table, clearly---after we had worked on
the project for maybe a year and a half, both Jim and Denis announced to
me within a very short time that they were leaving the NIH. Denis got an
offer that he couldn't refuse in France to be a part of the *Atomic
Energy Commission* (CEA) and run a very large program, and Jim Mattiello
decided that he wanted to go off and be an entrepreneur and run a
robotics company in Michigan. And I was devastated because we were
really making such fantastic progress and this team was going to be
broken up and we would never bring this to fruition. I thought that this
could basically grind everything to a halt.

*"I WAS DEVASTATED BECAUSE* *WE WERE REALLY MAKING SUCH FANTASTIC
PROGRESS AND THIS TEAM WAS GOING TO BE BROKEN UP"*

Luckily people in the field, with the exception of maybe one or two
groups, totally overlooked our work and seemed to show no interest in it
at all. In fact one of our possibly greatest potential rivals, a
graduate student who was working at *Massachusetts General Hospital* at
the time, apparently was told by his thesis advisor to avoid doing work
in this area because it wasn't going to go anywhere. So we had a little
bit of breathing room as it turned out. Jim left and Denis left and for
about six months I was at wit's end because we needed to acquire data
and we needed to move the program ahead and work with animals. It turned
out that just around the time Jim left I started to spend time with an
Italian post-doc named [Carlo
Pierpaoli](http://www.brain-child.org/rcenters/DPC.html). Carlo at that
time had just recently come from *Georgetown University*, I believe,
where he was working on benzodiazepines and other drugs that affect
cognitive performance, but he was very interested in MRI and in
diffusion. He was familiar with Denis Le Bihan's work and was hearing
about what we were doing with Jim Mattiello. He approached me, if I
recall, about the possibility of working together and I was a little
leery because I didn't know much about him initially and, I didn't know
what the collaboration could lead to, but the more time I spent with him
I realized he was a brilliant very serious person who was learning the
diffusion technology very quickly and was genuinely interested in
[DTI](http://neuro-www.mgh.harvard.edu/cma/tfpn_f/anat_tools.html). And,
as time progressed we started to work very closely together. He got the
software from Jim Mattiello and he started to adapt it, and he was
interested in clinical problems.

His background was in neurology, which turned out to be exceptionally
helpful, and he turned DTI really into his major research focus. At that
time also he was working with [Alan
Barnett](../dir2.nichd.nih.gov/nichd/stbb/Radiology210.pdf) in
[NINDS](http://www.ninds.nih.gov/). Alan was a physicist, and he and
Alan started to work very closely together to develop MRI sequences that
would allow you to do these experiments in animals and eventually
humans. And working with Alan and then eventually with [Peter
Jezzard](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9581612&dopt=Abstract),
Carlo really created an infrastructure to do human MRIs at a very high
quality. Denis and Jim and I had actually attempted to do this in 1995,
and we have an article in Denis's book and we have a figure that shows,
I think it was my brain, it shows the corpus callosum, but it wasn't a
full three dimensional DTI acquisition, it was a diffusion ellipse image
rather than a diffusion ellipsoid image. But it shows the arcade shape
of the corpus callosum very clearly, but artifacts on the 4T whole body
scanner and the eddy current artifacts and the stability, the
homogeneity of the magnetic field was poor, and so we really weren't
doing high quality radiologically acceptable DTI. That was really just a
demonstration that it could be done.

*BRINGING DTI FROM BENCH TO BEDSIDE EFFECTIVELY*

But Carlo and Alan and Peter Jezzard really developed a diffusion
weighted imaging framework here that would allow us to do DTI voxel by
voxel, with high enough spatial resolution, high enough image quality to
be able to do this clinically. And actually Carlo got a very nice award
from [Story
Landis](http://www.nih.gov/news/NIH-Record/08_19_2003/story03.htm),
NINDS, for essentially bringing DTI from bench to bedside
effectively---so these pork loin and cat brain experiments that Denis
and Jim and I did, to being able to do this in sedated monkeys, and then
ultimately in humans. So I think a large part of the early bench to
bedside migration was due to Carlo's coaching and effort and involvement
in that project and the physics input from primarily Alan Barnett and
Peter Jezzard.

And then there was another period following the clinical paper in 1996
([Diffusion tensor MR imaging of the human
brain](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed))
where there was again a flat growth in the DTI field. People still
didn't understand how to measure a tensor. They didn't understand what
it was, what information content it provided...except for a few
groups...but I think that it was starting to be intriguing to enough
groups that we realized that we had to make it a little bit more
accessible. Unfortunately, we wrote some early papers that made it seem
more complicated than it is in a sense. We focused on some technical
issues, specifically estimation of what is called the b-matrix. It was a
very strong interest of Denis Le Bihan's and particularly Jim Mattiello
because Denis had played around with the cross terms in imaging, in
diffusion imaging, prior to DTI and he thought that these were important
and needed to be elucidated; and Jim was the one who calculated a lot of
these effects; so the papers we wrote about cross terms did put some
people off in the field because they thought it was complicated and
difficult to do.

I realized that we had to---I don't know how to say it tactfully---well
to "dumb it down" a little bit so that people wouldn't feel that it was
so difficult to do, to make it simpler and more accessible to a larger
group of people who otherwise wouldn't do it. And I went back to a
special case of the paper that we had written in 1994, in which we would
only use seven diffusion weighted images and I showed in that case you
can get an analytical relationship between the measured signals from the
MRI experiment and the diffusion tensor. And I published those formulae
so that all you had to do was get your signals and plug them in. It was
a little black box and in the afternoon you could program it up---and
you didn't have to do any fancy statistical estimation ... nothing ...
no real computer programming. You could do this in an Excel spreadsheet
if you wanted to.

*"PEOPLE REALIZED THAT YOU DIDN'T HAVE TO BE A ROCKET SCIENTIST TO DO
DTI"*

In fact, shortly thereafter, [*General
Electric*](http://www.ge.com/en/company/businesses/ge_healthcare.htm)
picked this up and started to provide it to its sites as an add-on in
its Functool package. And people, I think for the first time, realized
that you didn't have to be a rocket scientist to do
[DTI](http://www.masimax.com/nida_pm/pediatric/neil.cfm). You could take
your original diffusion weighted imaging data and you could process it
with a slightly more complicated model, calculate the tensor and then
calculate all of these useful parameters like the Trace, and the fiber
directions, and make the maps of the Fractional Anisotropy and the other
features that we had already presented and talked about, and that it
wasn't so scary. Actually, a number of people have told me that before
that paper came out that they were really not interested in doing DTI.
And then shortly thereafter, there was another increase in papers on the
subject and people started using it more and more.

I think there was enough of an interest among the sites that General
Electric decided ultimately to license the DTI technology from the NIH
and some of the other companies followed suit thereafter, and after that
point many clinical programs had the technology that they were able to
use because it was provided either by physicists in their program or
through these commercial sequences and analysis tools that the companies
were starting to provide and there was a sudden surge in clinical
applications. You started to see papers applying DTI to many, many
diseases and disorders. Frankly many of which I could never have
anticipated when we first started.

What was particularly interesting, frankly, was the possibility of using
DTI to look at some of the behavioral and cognitive disorders,
psychiatric disorders, because I really viewed DTI primarily as an
anatomical neurological diagnostic tool, initially. And then there were
reports like from the
[Klingberg-Gabrielli](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed)
study that suggested that there might be a relationship between the
white matter organization and reading ability. I found that finding
shocking, but it seems to have held up in some of [Brian
Wandell's](http://white.stanford.edu/~brian/refs.html) recent work; he
suggests that as well. And there was a re-discovery at that point in the
MRI field about what I guess we call the neural anatomical hypothesis
which is that the brain wiring is important, although people are all
focused on gray matter and [fMRI,](http://www.fmri.org/fmri.htm) the
white matter is important too.

CW: Was this the real advantage of DT-MRI over classical MRI that you
can picture white matter and classical MRI pictures gray matter?

PB: Not exactly because there is, for instance T1 imaging that can
provide a nice depiction of white matter where you can at least
distinguish it fairly well and at high resolution from gray matter. But,
it doesn't provide the fiber orientation and it doesn't allow you to
project fiber tracts or calculate tracts. That's something actually I
didn't mention in my telling, but maybe we'll get back to that how
[*tractography*](http://www.mr-tip.com/serv1.php?type=db1&dbs=Diffusion Tensor Tractography)
came about, but the possibility of following white matter pathways is a
very exciting information that DTI provides and that the other imaging
techniques don't.

*"YOU COULD MAKE A MAP OF THE BRAIN WHERE YOU COULD SEE THE DIRECTION OF
THE FIBER BY LOOKING AT THE COLOR"*

In fact [Carlo
Pierpaoli](http://www.ctm.ulpgc.es/tensor/BIBTENSOR/Author/PIERPAOLI-C.html)
and [Sinisa Pajevic](http://mscl.cit.nih.gov/spaj/) again came up with a
very compelling way of displaying the fiber tract information. (see
[*Color Schemes*,
1999](http://www.ctm.ulpgc.es/tensor/BIBTENSOR/Author/PIERPAOLI-C.html))
We had proposed back in 1994 that the principal direction of the
diffusion tensor, the one that's associated with the fastest diffusion
coefficient, is the local estimate of the fiber orientation. Carlo and
Sinisa realized that you could color code this---and I think just around
the same time [Derek
Jones](http://www.ucl.ac.uk/HCS/deafbrain/theteam/Derek Jones/) did this
too, in fairness---you could color code that vector with Red, Green, and
Blue, let's say, corresponding to the X, Y and Z components of that
vector, and in doing that you could make a map of the brain where you
could see the direction of the fiber by looking at the color.

Denis had actually proposed a color scheme back in that poster that I
talked to you about, but it was very, very different in the information
content. Carlo and Sinisa's approach was to take this fiber orientation
that we calculated from the diffusion tensor itself and to assign a
color to it that was related to its orientation in space. And it has
turned out to be a very, very powerful way of looking at white matter
pathways, primarily the large white matter pathways, the motor tracts
and the sensory tracts, and the association fibers, the commissural
fibers, the projection fibers. And in fact Carlo and Sinisa wrote a very
beautiful paper where they showed that for the first time with DTI you
could really visualize those pathways and their orientation ... and
tractography, in a sense, mathematically connects the dots. It allows
you to compute those trajectories that your eye is telling you exist in
those color maps that Carlo and Sinisa had produced.

*"THE IDEA OF DOING TRACTOGRAPHY ON THIS NOISY SPARSE DTI DATA SET
SEEMED LIKE SCIENCE FICTION TO ME BACK IN 1994"*

In fact the tractography dates back to my 1991 disclosure. I showed
actually Denis pictures of these fiber tracts as though I could
calculate them. I rendered them in *Mathematica* and showed what a fiber
field might look like. In fact, if you want I could pull that down and
show you my notebook. But the realization of that took a while because
the data was so poor initially. The idea of doing tractography on this
noisy sparse DTI data set seemed like science fiction to me back in
1994, but already towards the end of the '90s tractography started to
become a reality because the quality of the diffusion images were so
much better and the resolution was finer and the possibility of doing
tractography was much more palpable. It was a next step.

CW: So was that a matter of further development of MRI machines?

PB: No in fact it was post processing. Once you have the tensor field,
once you have the tensor sample, then you can construct these fiber
orientations in each voxel within an imaging volume. You know my
background is in fluid mechanics. So you have to realize I learned about
streamlines and streamsurfaces as a graduate student. So the idea of
following these lines of maximum diffusion was really very similar to
what I would have done in graduate school if I wanted to follow a flow
field in a laminar flow. If I wanted to, let's say, show where a fluid
was flowing in a curved pipe I would have used the same kind of
equations that we proposed for following the direction of the maximum
diffusivity. There was a little bit of a trick, the way that I had
conceived of it you had to have a model of a diffusion tensor field, a
continuous model, and that held up DTI-tractography probably for 4 or 5
years for me, because ... \[laugh\] that's another story.

[Akram Aldroubi](http://www.math.vanderbilt.edu/~aldroubi/), who was
working here \[at BEIP\] and who later had developed some of the
mathematical machinery to do this tensor field approximation was very
excited about doing this work, but he was so brilliant that he had so
many other things that he was working on that he never got around to
doing the tensor field approximation. I kept bugging him about it and
bugging him about it. But eventually he decided that it was important
enough that he had to do it, and he and Sinisa Pajevic---Akram's theory
and Sinisa's implementation---provided the software to be able to
essentially weave together these discrete measurements that we make in
individual voxels and turn it into a fabric that you could follow
seamlessly through the brain. And once that fabric was created then it
was a simple to model to solve a differential equation along one of
these fiber tracks, but it's just a mathematical model. A lot of people
don't realize it, we just take the diffusion data and we just follow
along on the direction of maximal diffusivity. Those fibers are
mathematical objects that are extracted from the tensor data. They're
not actually measured independently.

CW: So where do you see this going? Do you conceive of it more as a
diagnostic tool or are you still interested in drug delivery?

PB: Well we're going back to magnetic stimulation and drug delivery
again, which is interesting. It's "Back to the Future". I have a
collaboration with [Malisa
Sarntinoranont](http://aemes.mae.ufl.edu/~msarnt/), who is at the
University of Florida, and she wants to use DTI for personalized drug
delivery, particularly in some intractable tumors like in the Pontine
fiber area \[Pons\] where it is very hard to operate and where this
chemical drug delivery can potentially provide real relief. And with
[Pedro Miranda](http://ott.od.nih.gov/db/abstxt.asp?refno=184) we are
going back now to looking at magnetic stimulation using the electrical
conductivity properties---the heterogeneous and anisotropic properties
that we're able to extract from the tensor data---and now we use Finite
Element Method approaches, and we're using the tensor data to make a
better model of the electrical properties of the brain. So in fact those
initial problems that got me interested in the first place we're now in
a position to revisit with a lot more information. Now the convection
enhanced delivery is really a therapeutic application, and Carlo
Pierpaoli is working with neurosurgeons here at
[NINDS](http://www.clinicaltrials.gov/show/NCT00081432) to help with
surgical planning; to do DTI to see where the nerves are and to look at
tumor staging and to find out what's healthy tissue and what's edema. So
again it's been used in a therapeutic application, which is a little bit
surprising from the history of the ideas, but I think primarily DTI is
still a diagnostic tool.

*THE NIH PEDIATRIC ANATOMIC NEUROIMAGING DATABASE INITIATIVE*

I still view it primarily as a diagnostic tool. People in the
neurosciences field and mental health and people interested in cognitive
and behavioral disorders are starting to find that there are significant
differences, at least in populations of subjects that are normal as
compared to ones who exhibit particular symptoms of diseases. So we're
hoping, since we are in a developmentally oriented Institute
([NICHD](http://www.nichd.nih.gov/)) that we can really apply these
approaches to look at developmental abnormalities particularly in
children. We have an activity in the lab where we are working to advance
that very seriously. Carlo Pierpaoli again is involved in the
[*pediatric neuroimaging project*](http://www.brain-child.org/) which is
supported by a [consortium of
Institutes](http://grants.nih.gov/grants/guide/rfa-files/RFA-DA-02-001.html)
to obtain normal DTI data from infants from six months of age all the
way to 18 years of age to be able to at least get a baseline of normal
diffusion data with which you can compare diffusion tensor data from
subjects who have various abnormalities or diseases.

CW: And then the application would be screening of the entire
population?

PB: Well unfortunately the cost of DTI, the cost of MRI, is really too
high in this kind of environment for screening, but if there are some
technological breakthroughs in the next few years for instance that
would allow you to build super conducting magnets let's say out of
liquid nitrogen as opposed to liquid helium, or some people are starting
to do imaging at low magnetic fields, maybe one day there will be
bedside MRIs that could be used in pediatric applications when children
are born or possibly in intensive care units. Under those conditions
there could be significant screening applications. You know, if every
child were scanned, you might be able to tell if the brain's white
matter architecture were normal and if the neuroanatomical substrates
were all there and different pathways were in the right place at that
stage of development. Certainly with genetic screening that's going on
maybe there could be some metabolic screening looking at abnormal levels
of growth factors, things that you might be able to be detected from
blood tests, and then do subsequent MRIs to be able to look at white
matter architecture. That would be the next level of screening that
could be used. Certainly it could be used by drug companies that are
interested in neuro-protective agents or in the efficacy of psychotropic
drugs that are being given to see if there are any long-term changes in
white matter structure organization.

CW: So the reliability of the method, I think actually from one of your
papers that there was a problem at the beginning that you were getting a
lot of noise. Has that been resolved?

PB: I think it's improving. There are still some problems in
tractography. You know we talked about tractography but we also talked
about all of the blemishes and problems, the soft underbelly of
tractography, and there are many problems with it. And some of them are
being addressed, some of them haven't been. The diffusion weighted
imaging data is now of a higher quality than it was even five years ago
... much higher quality. The signal strength with respect to the noise
is much greater. Certain artifacts like eddy current artifacts are being
remedied to a larger extent. Motion artifacts are a little bit easier to
correct for also, in part, to some of the efforts that are going on here
in our lab to do better registration of MR data; some of these problems
are being chipped away.

*IT'S TRICKY TO TRACK "KISSING" FIBERS*

Now in terms of fiber tracking itself there are some topological
problems that I'm not sure that DTI is ever going to resolve: Fibers
that cross or that merge or what we call "kiss". It's not possible
currently with the DTI data, or even more advanced models of diffusion
(that look at the apparent diffusion properties in many, many more
directions) to necessarily divine or determine the underlying topology
without either using *a priori* information or some other methodologies
that are complimentary that provide information that would allow you to
tease apart those underlying topological differences.

So we're confident for instance in the big tracts that run fairly
parallel and that run by themselves; and particularly in peripheral
nerves it's very exciting because the peripheral nerves are much more
regular, and there aren't that many geometric possibilities for them.
There, I think, tractography will really come into its own in the next
five years looking at things ranging from *carpal tunnel syndrome* to
problems in the spine. But in the brain there are regions like in the
*pons*, or subcortical white matter where the white matter travels along
and all of a sudden shoots out at a right angle towards the cortex.
Following even white matter from gyrus to gyrus through sulci can be
difficult because you don't know where individual fibers terminate and
where they continue.

The microstructure is so much more complicated than the macroscopic
picture that DTI provides that I'm not sure that it is ever going to be
sufficient for doing high resolution tractography. But I think that with
maybe some other pieces of information that other imaging methods can
provide and maybe from some neurological types of test that could be
performed, you could start to eliminate some of these ambiguities and
maybe connect one region of the brain to another and show that certain
topologies that could be there can't actually be anatomically correct.

So I would say we've advanced the technology a lot particularly in
diffusion weighted imaging technology---well not we, but I'm speaking
about the field now---the companies have provided better sequences.
There's been a lot more improvement. There've been improvements from the
universities and hospitals, and the underlying theory, and the hardware,
and the software for acquiring diffusion imaging is better, and some of
the models are better now, but there are still underlying problems that
DTI or any diffusion imaging approach aren't going to be able to solve
with respect to tractography from where I sit now. Ten years from
now---who knows whether I'll even be in this field---but ten years from
now, I may have a different perspective.

*LOOKING BACK AT THE HISTORY OF MAGNETIC RESONANCE IMAGING*

CW: Can you say briefly what is DWI compared to DTI?

PB: Oh, I'm sorry. DWI is [*diffusion weighted
imaging*](../diffusion%20weighted%20imaging). This is the technique
which was actually prophesied or augured by [Paul
Lauterbur](http://www.scs.uiuc.edu/chem/lauterb.htm) back in 1973 in his
famous *Nature* article (P.C. Lauterbur, Image formation by induced
local interactions: examples employing nuclear magnetic resonance.
Nature 242:190-191 (1973), but was brought to life by a number of
groups, Merboldt and others, Denis Le Bihan and Taylor and Bushell
around the same time. They proposed the possibility of producing images
in which each voxel within the imaging volume were weighted by the local
diffusion properties. So the intensity of the signal that you measured
was related to the diffusion properties and all of them in their own way
proposed formulas that you could use to, in fact, take that diffusion
weighted imaging data and turn it into a measured diffusion coefficient
in each voxel.

You probably should know that the measurements of diffusion using NMR go
back really to the late 1940s / early '50s. The theory and the
underlying experiments that are the basis of diffusion measurements with
NMR are classical and quite beautiful work by Edward Mills Purcell and
[Carr](http://www.garfield.library.upenn.edu/classics1983/A1983QN93100001.pdf)
and [Hahn](http://www.emrf.org/FAQs MRI History.html). Hahn realized
that the spin echo could be affected by diffusion and Carr and Purcell
actually in 1954 published the first measurements of diffusion
coefficient using NMR and described it as: "One could not imagine a more
non-perturbing measurement of diffusion." It was a very understated but
somewhat self-congratulatory phrase at the end of the paper, but they
realized that this was a really powerful way to measure diffusion
without actually physically labeling the molecules, without introducing
a new species, and you were labeling the nuclear spins, which really
didn't play a role in the chemical activity of the molecules. So this
was a real breakthrough\--and actually Purcell won a [*Nobel Prize in
Physics*](http://nobelprize.org/physics/laureates/1952/) (together with
Felix Bloch) in 1952, I think in part for the measurement of the
diffusion coefficient using NMR\--and then there was work in the
mid-1960s.

*DTI IN STROKE ASSESSMENT*

The most important work was probably by Stejskal and Tanner, who not
only improved the hardware, but came up with a more advanced theory of
how you can relate the measured NMR signal to the diffusion coefficient.
And then there were [Mike
Moseley](http://med.stanford.edu/profiles/frdActionServlet?choiceId=showPublication&pubid=62895&fid=4240)
and his group, I think there were two papers by [Wesbey, Ehman and
Moseley](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Search&db=PubMed&term=Wesbey+GE%2C+Moseley+ME%2C+Ehman+RL.+Translational+molecular+self-diffusion+in+magnetic+resonance+imaging.&tool=QuerySuggestion),
**(**Wesbey GE, Moseley ME, Ehman RL. Translational molecular
self-diffusion in magnetic resonance imaging. I. Effects on observed
spin-spin relaxation. Invest Radiol 1984;19(6):484-490.; Wesbey GE,
Moseley ME, Ehman RL. Translational molecular self-diffusion in magnetic
resonance imaging. II. Measurement of the self-diffusion coefficient.
Invest Radiol 1984;19(6):491-498.**)** which I think, as far as I can
tell, were the first demonstration of diffusion weighted imaging where
they not only showed the formulas but they also produced the images that
were diffusion weighted. It suffered from a technical problem, which
prevented it from being useful clinically, but it was that data that I
think Le Bihan and Bushell and Taylor and Merboldt were drawing from
when they proposed a clinically feasible way of doing diffusion weighted
imaging. In fact DTI uses those diffusion-weighted images with a new
model and a new experimental design to extract new and useful data: The
tensor and the quantities that you can derive from it, the various
scalar quantities like the Trace, which is used a lot in stroke
assessment. A lot of people don't know that this a DTI quantity, but it
is probably the most important one that we proposed; and then there is
the fiber orientation information in the vectors, and then you have
tractography which comes from looking at those fiber orientations in
different voxels and following the direction of maximum diffusion. So
there's a lot of information that flows but the actual input is the
diffusion weighted imaging data and the output is the tensor and the
quantities you derive from it.

CW: So where are you going to go from now? What's your next project?

PB: Are you going to turn your recorder off?

CW: \[laugh\]

PB: No I'm kidding. Well in fact we continue to think that the diffusion
sensitization provides new and useful information but we are moving
away, frankly, from DTI *per se* because it's based on a very simple
model of diffusion which works under clinical conditions right now
extremely well, but as scanners are becoming more powerful gradients can
be made larger, diffusion weighting can be made larger the model may not
hold. As you increase the diffusion gradient strength we can probe
smaller and smaller length scales and you can start to see the effects
of what's called restriction. You can see, in fact, what are populations
of spins that are probably trapped within axons. And we are very
interested in looking at different populations of water within brain and
within other soft tissue, which we really weren't able to see with DTI
with hardware and software that we had available in the 1990s.

*THE FUTURE: TRACING MICROSTRUCTURAL CHANGES IN DEVELOPMENTAL DISORDERS*

There is a lot more information that you can glean from diffusion
weighted data than we're currently getting. So one part of the lab's
activity is to improve our models of tissue microstructure and exchange
of water from one compartment to another, and our ability to use
diffusion imaging to look at microstructural features that we really
didn't think would be possible to do even three or four years ago. In
fact I proposed in our last Board of Scientific Counselors meeting a
project that I would have actually laughed at myself four years ago. I
never would have even considered doing it because it wasn't practicable.
But now we want to be able to look at some *microstructural features*
and we're confident that we can do it and we're also thinking about ways
of measuring *displacement distributions* using a vastly reduced number
of acquisitions so that not just a tensor, but other higher order
parameters that we can measure and that provide additional information
about microstructure, and possibly the number of subcompartments that
may exist within a voxel, could be gleaned in about the same amount of
time that we're doing DTI.

It won't solve the problem of tractography and the issue of fibers
whether they're kissing or crossing you know, some of those topological
problems, but it will give you an idea of how many distinct compartments
you have, how much water you might have intracellularly as opposed to
extracellularly. We actually have a *patent application* for some
abstracts that deal with measuring the fiber diameter distribution in
white mater. That's a microstructural quantity that's of great
significance and I never would have thought that we could have done that
even a few years ago. But it turns out that the models that we developed
are very amenable to measuring it. We made some measurements in sciatic
nerve, in optic nerve, in spinal cord and we get histologically very
plausible fiber diameter distributions when we compare it to the
histologic sections that we've done.

So that could be important in a lot of developmental disorders, maybe
*Amyotrophic Lateral Sclerosis*, ALS where large diameter fibers drop
out early on in the process, particularly in the central nervous system
and it's very hard to make that diagnosis. Also developmental disorders,
*autism*, because of the poor regulation of nerve growth factors that
other groups in our Institute have identified in
[*autism*](http://www.nichd.nih.gov/autism/)---the up regulation and
down regulation of nerve growth factors. I think there's a suspicion
that the fiber diameter distribution is skewed in a pathological way in
some of these neural developmental disorders, and I think it would be
very exciting to use these new techniques there. So we're trying to
"push the envelope" in terms of what we can see microstructurally. And
there are so many creative biologists and physicians out there, who,
once you give them a quantitative tool, will apply it in ways that you
couldn't have imagined, but then are delighted by. I think that's going
to spawn out another generation of developments that we'll be happy
about. So those are the directions that we're currently moving in.