Signal Processing - November 2017 - 18

UNIVERSITY OF ARIZONA

FIGURE 1. University of Arizona Prof. Jennifer Barton holding a highly flexible falloposcope her
research team has developed to image the biomarkers of ovarian cancer, one of the most deadly
gynecological cancers.

CCD camera, and a multipurpose data
acquisition (DAQ) board to generate
control signals for scanning or excitation
of a source/filter wheel and to generate
any needed synchronization signals. "It
is always a struggle to increase signalto-noise, dynamic range, and contrast in
imaging systems," Barton says. These
attributes affect how fast-and deep-
one can image-in OCT. "We are limited
in the amount of light power we can
put on the tissue, so signal processing
techniques that efficiently extract the
signal from noise, background, and
unwanted artifacts are always important,"
she explains.
"In the past, systems were slow
enough that one didn't have to pay too
much attention to data acquisition and
signal processing," Barton observes.
"Nowadays, with linear CCDs running
at 100-KHz frame rates and 2k pixels,
there needs to be more careful consider-
ation of hardware and software process-
ing," she notes. "This is not extraordinary
as compared to some signal processing
applications, but it means that imaging
teams have to have new skill sets."
The team is now seeking additional
funding to build hospital-ready fallopo-
scopes so that research can be conduct-
ed on human subjects. Barton is hopeful
that the technology will lead to an earlier
and more accurate diagnosis of ovarian
18

cancer. "Our technique can either serve
as a primary screening method, or as a
follow-up to other tests," she says.

Imaging arteries
Plaque accumulating inside artery walls
can cause arteries to thicken and harden.
When a plaque accumulation ruptures,
it can restrict or even block blood flow,
leading to a heart attack, stroke, or other
serious medical issues. Accurate diag-
noses are limited by the fact that there
are no imaging tools available to con-
sistently and accurately detect plaque at
risk of rupturing in living patients.
An enhanced imaging technology-
intravascular photoacoustic (IVPA)
imaging-can generate three-dimen-
sional images of artery interiors, poten-
tially helping physicians to diagnose
plaques on the verge of rupturing. The
drawback is that developers have so far
struggled to develop imaging instru-
ments that are capable of illuminating
arteries to a useful depth and at fast
enough speeds while also meeting clini-
cal requirements.
Now, using signal processing and
other advanced tools and approaches, a
team of researchers from Purdue Uni-
versity, the Indiana University School
of Medicine, and the Shanghai Insti-
tute of Optics and Fine Mechanics
has -developed a new type of collinear
IEEE SIGNAL PROCESSING MAGAZINE

|

November 2017

|

catheter (Figure 2), featuring a design
that promises to greatly improve the
sensit ivit y a nd i magi ng dept h of
IVPA -imaging.
"Our photoacoustic catheter probe
integrates both photoacoustic and
ultrasound modalities within a very
tiny space-1 mm in diameter in our
most updated version," says Yingchun
Cao, a postdoctoral fellow working
in the research group led by Prof. JiXin Cheng of Purdue University. "The
most important feature of our cath-
eter is that we used a collinear design
for the optical-acoustic wave overlap
to greatly improve the imaging sen-
sitivity and depth," notes Cao, who is
the lead author of a research paper on
the project.
IVPA imaging functions by measur-
ing ultrasound signals from molecules
exposed to a light beam from a fastpulsing laser. The new collinear probe
allows the optical beam and sound wave
to share the same path throughout the
imaging process, rather than crossoverlapping as in previous designs. The
approach increases the instrument's
sensitivity as well as the imaging depth,
enabling high-quality IVPA imaging
of a human coronary artery over 6 mm
in depth from the lumen, the normally
open channel within arteries, to the
perivascular fat that surrounds the out-
side of most arteries and veins. "This
research can be used ... to help the doc-
tor for accurate diagnosis of plaque vul-
nerability and even for imaging-guided
intravascular surgery or drug delivery,"
Cao says.
"The unique advantage of our re--
search," Cao says, "is we can provide
quantitative information of lipid deposit
within the artery wall, including the
size and depth of lipid core with suf-
ficient spatial resolution. The coregis-
tered ultrasound image integrated in the
technique can provide morphological
structure of the artery for accurate posi-
tion identification of lipid deposit. "In
our most recent research, we can accu-
rately distinguish different lipid com-
positions by a self-developed numerical
approach," Cao notes.
Signal processing is an important part
of the research. "A high-quality real-time



Table of Contents for the Digital Edition of Signal Processing - November 2017

Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
Signal Processing - November 2017 - 5
Signal Processing - November 2017 - 6
Signal Processing - November 2017 - 7
Signal Processing - November 2017 - 8
Signal Processing - November 2017 - 9
Signal Processing - November 2017 - 10
Signal Processing - November 2017 - 11
Signal Processing - November 2017 - 12
Signal Processing - November 2017 - 13
Signal Processing - November 2017 - 14
Signal Processing - November 2017 - 15
Signal Processing - November 2017 - 16
Signal Processing - November 2017 - 17
Signal Processing - November 2017 - 18
Signal Processing - November 2017 - 19
Signal Processing - November 2017 - 20
Signal Processing - November 2017 - 21
Signal Processing - November 2017 - 22
Signal Processing - November 2017 - 23
Signal Processing - November 2017 - 24
Signal Processing - November 2017 - 25
Signal Processing - November 2017 - 26
Signal Processing - November 2017 - 27
Signal Processing - November 2017 - 28
Signal Processing - November 2017 - 29
Signal Processing - November 2017 - 30
Signal Processing - November 2017 - 31
Signal Processing - November 2017 - 32
Signal Processing - November 2017 - 33
Signal Processing - November 2017 - 34
Signal Processing - November 2017 - 35
Signal Processing - November 2017 - 36
Signal Processing - November 2017 - 37
Signal Processing - November 2017 - 38
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Signal Processing - November 2017 - 40
Signal Processing - November 2017 - 41
Signal Processing - November 2017 - 42
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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